{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed5a21da",
   "metadata": {},
   "source": [
    "# Ensemble Methods: Random Forest Tutorial\n",
    "## Understanding How Multiple Trees Beat Single Trees\n",
    "\n",
    "### Overview\n",
    "In this tutorial, we'll explore Random Forest, one of the most powerful and widely-used ensemble methods. We'll cover:\n",
    "\n",
    "1. **What is Random Forest?** - The intuition behind combining multiple decision trees\n",
    "2. **How it works** - Bootstrap sampling and random feature selection\n",
    "3. **Bias-Variance Trade-off** - How Random Forest reduces variance while maintaining low bias\n",
    "4. **Practical Implementation** - Step-by-step coding with visualizations\n",
    "5. **Performance Analysis** - Comparing single trees vs Random Forest\n",
    "\n",
    "### Key Concepts\n",
    "- **Bootstrap Aggregation (Bagging)**: Training multiple models on different subsets of data\n",
    "- **Random Feature Selection**: Each tree sees only a random subset of features\n",
    "- **Ensemble Voting**: Combining predictions from multiple trees\n",
    "- **Variance Reduction**: How averaging reduces prediction variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "359cd021",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Libraries imported successfully!\n",
      "✓ NumPy, Matplotlib, Seaborn\n",
      "✓ Scikit-learn (Random Forest, Decision Trees, metrics)\n",
      "✓ Pandas\n",
      "Ready to explore Random Forest!\n"
     ]
    }
   ],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import make_classification, make_regression, load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, validation_curve\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor, plot_tree\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error, classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Set style for better visualizations\n",
    "plt.style.use('default')\n",
    "sns.set_palette(\"husl\")\n",
    "np.random.seed(42)\n",
    "\n",
    "print(\"Libraries imported successfully!\")\n",
    "print(\"✓ NumPy, Matplotlib, Seaborn\")\n",
    "print(\"✓ Scikit-learn (Random Forest, Decision Trees, metrics)\")\n",
    "print(\"✓ Pandas\")\n",
    "print(\"Ready to explore Random Forest!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5ea25f",
   "metadata": {},
   "source": [
    "## 1. The Intuition Behind Random Forest\n",
    "\n",
    "### The Wisdom of Crowds\n",
    "\n",
    "Imagine you're trying to estimate the number of jellybeans in a jar. You could:\n",
    "1. **Ask one expert** - might be very good or very wrong\n",
    "2. **Ask 100 people and average their guesses** - usually much more accurate!\n",
    "\n",
    "Random Forest applies this same principle to machine learning:\n",
    "- Instead of one decision tree, use many trees\n",
    "- Each tree sees a slightly different view of the data\n",
    "- Combine their predictions by voting (classification) or averaging (regression)\n",
    "\n",
    "### Why Does This Work?\n",
    "- **Individual trees are high variance** - small changes in data cause big changes in the tree\n",
    "- **Averaging reduces variance** - errors from individual trees cancel out\n",
    "- **Maintains low bias** - trees are still expressive models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c9140da1",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True number of jellybeans: 1000\n",
      "Average individual error: 144.7\n",
      "Crowd prediction error: 29.2\n",
      "\n",
      "Improvement: 5.0x better!\n"
     ]
    }
   ],
   "source": [
    "# Let's demonstrate the \"wisdom of crowds\" with a simple simulation\n",
    "np.random.seed(42)\n",
    "\n",
    "# True number of jellybeans\n",
    "true_count = 1000\n",
    "\n",
    "# Simulate individual guesses (with some systematic error and random noise)\n",
    "n_people = 100\n",
    "individual_guesses = np.random.normal(true_count + 50, 200, n_people)  # Slightly biased estimates\n",
    "\n",
    "# Calculate crowd average\n",
    "crowd_average = np.mean(individual_guesses)\n",
    "\n",
    "# Visualize the concept\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
    "\n",
    "# Plot 1: Distribution of individual guesses\n",
    "ax1.hist(individual_guesses, bins=20, alpha=0.7, color='lightblue', edgecolor='black')\n",
    "ax1.axvline(true_count, color='red', linestyle='--', linewidth=2, label=f'True Count: {true_count}')\n",
    "ax1.axvline(crowd_average, color='green', linestyle='-', linewidth=2, label=f'Crowd Average: {crowd_average:.0f}')\n",
    "ax1.set_xlabel('Estimated Jellybean Count')\n",
    "ax1.set_ylabel('Number of People')\n",
    "ax1.set_title('Individual Guesses vs Crowd Average')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 2: Error comparison\n",
    "individual_errors = np.abs(individual_guesses - true_count)\n",
    "crowd_error = abs(crowd_average - true_count)\n",
    "\n",
    "ax2.boxplot([individual_errors], labels=['Individual Guesses'])\n",
    "ax2.scatter([1], [crowd_error], color='red', s=100, label=f'Crowd Error: {crowd_error:.0f}', zorder=5)\n",
    "ax2.set_ylabel('Absolute Error')\n",
    "ax2.set_title('Error Comparison: Individual vs Crowd')\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"True number of jellybeans: {true_count}\")\n",
    "print(f\"Average individual error: {np.mean(individual_errors):.1f}\")\n",
    "print(f\"Crowd prediction error: {crowd_error:.1f}\")\n",
    "print(f\"\\nImprovement: {np.mean(individual_errors)/crowd_error:.1f}x better!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b692ca4",
   "metadata": {},
   "source": [
    "## 2. How Random Forest Works\n",
    "\n",
    "### Two Key Ingredients for Diversity\n",
    "\n",
    "1. **Bootstrap Sampling (Bagging)**\n",
    "   - Each tree trains on a different random subset of the training data\n",
    "   - Sampling with replacement (some examples appear multiple times, others not at all)\n",
    "   - Creates natural diversity between trees\n",
    "\n",
    "2. **Random Feature Selection**\n",
    "   - At each split in each tree, only consider a random subset of features\n",
    "   - Typically √(total features) for classification, total_features/3 for regression\n",
    "   - Prevents trees from all making the same splits\n",
    "\n",
    "### The Algorithm\n",
    "```\n",
    "For each tree in the forest:\n",
    "    1. Create bootstrap sample of training data\n",
    "    2. Train decision tree with random feature selection\n",
    "    3. Store the trained tree\n",
    "\n",
    "To make predictions:\n",
    "    1. Get prediction from each tree\n",
    "    2. Classification: Vote (majority wins)\n",
    "    3. Regression: Average the predictions\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1416eea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap Sampling Observations:\n",
      "• Each sample has the same size as the original dataset\n",
      "• Some values appear multiple times, others don't appear at all\n",
      "• Each sample is different, creating diversity between trees\n",
      "• On average, each bootstrap sample contains ~63% of unique original examples\n"
     ]
    }
   ],
   "source": [
    "# Let's visualize how bootstrap sampling works\n",
    "np.random.seed(42)\n",
    "\n",
    "# Create a simple dataset\n",
    "original_data = np.arange(1, 21)  # Numbers 1-20\n",
    "n_samples = len(original_data)\n",
    "\n",
    "# Create 3 bootstrap samples\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
    "\n",
    "# Original data\n",
    "axes[0, 0].bar(range(len(original_data)), original_data, alpha=0.7, color='lightblue')\n",
    "axes[0, 0].set_title('Original Dataset')\n",
    "axes[0, 0].set_xlabel('Index')\n",
    "axes[0, 0].set_ylabel('Value')\n",
    "axes[0, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# Bootstrap samples\n",
    "for i in range(3):\n",
    "    row = (i + 1) // 2\n",
    "    col = (i + 1) % 2\n",
    "    \n",
    "    # Create bootstrap sample\n",
    "    bootstrap_indices = np.random.choice(n_samples, size=n_samples, replace=True)\n",
    "    bootstrap_sample = original_data[bootstrap_indices]\n",
    "    \n",
    "    # Count frequency of each value\n",
    "    unique_values, counts = np.unique(bootstrap_sample, return_counts=True)\n",
    "    \n",
    "    # Plot\n",
    "    axes[row, col].bar(unique_values - 1, counts, alpha=0.7, color=f'C{i}')\n",
    "    axes[row, col].set_title(f'Bootstrap Sample {i+1}')\n",
    "    axes[row, col].set_xlabel('Original Index')\n",
    "    axes[row, col].set_ylabel('Frequency in Sample')\n",
    "    axes[row, col].set_xlim(-0.5, 19.5)\n",
    "    axes[row, col].grid(True, alpha=0.3)\n",
    "    \n",
    "    # Show some statistics\n",
    "    unique_count = len(np.unique(bootstrap_sample))\n",
    "    axes[row, col].text(0.02, 0.95, f'Unique values: {unique_count}/{n_samples}', \n",
    "                       transform=axes[row, col].transAxes, \n",
    "                       bbox=dict(boxstyle='round', facecolor='white', alpha=0.8))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Bootstrap Sampling Observations:\")\n",
    "print(\"• Each sample has the same size as the original dataset\")\n",
    "print(\"• Some values appear multiple times, others don't appear at all\")\n",
    "print(\"• Each sample is different, creating diversity between trees\")\n",
    "print(\"• On average, each bootstrap sample contains ~63% of unique original examples\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55b26d6d",
   "metadata": {},
   "source": [
    "## 3. Random Forest vs Single Decision Tree\n",
    "\n",
    "Let's create a practical example to see Random Forest in action and understand how it reduces variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca1db602",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset created:\n",
      "Training set: 700 samples, 10 features\n",
      "Test set: 300 samples\n",
      "Classes: [0 1] (balanced: Counter({np.int64(1): 503, np.int64(0): 497}))\n",
      "\n",
      "Training models...\n",
      "\n",
      "Results:\n",
      "Single Decision Tree Accuracy: 0.8867\n",
      "Random Forest Accuracy: 0.9400\n",
      "Improvement: 0.0533\n"
     ]
    }
   ],
   "source": [
    "# Create a classification dataset with some complexity\n",
    "X, y = make_classification(\n",
    "    n_samples=1000,\n",
    "    n_features=10,\n",
    "    n_informative=5,\n",
    "    n_redundant=2,\n",
    "    n_clusters_per_class=2,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "print(f\"Dataset created:\")\n",
    "print(f\"Training set: {X_train.shape[0]} samples, {X_train.shape[1]} features\")\n",
    "print(f\"Test set: {X_test.shape[0]} samples\")\n",
    "print(f\"Classes: {np.unique(y)} (balanced: {Counter(y)})\")\n",
    "\n",
    "# Train models\n",
    "print(\"\\nTraining models...\")\n",
    "\n",
    "# Single Decision Tree\n",
    "single_tree = DecisionTreeClassifier(random_state=42, max_depth=10)\n",
    "single_tree.fit(X_train, y_train)\n",
    "\n",
    "# Random Forest\n",
    "random_forest = RandomForestClassifier(\n",
    "    n_estimators=100,\n",
    "    max_depth=10,\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")\n",
    "random_forest.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "tree_pred = single_tree.predict(X_test)\n",
    "forest_pred = random_forest.predict(X_test)\n",
    "\n",
    "# Calculate accuracies\n",
    "tree_accuracy = accuracy_score(y_test, tree_pred)\n",
    "forest_accuracy = accuracy_score(y_test, forest_pred)\n",
    "\n",
    "print(f\"\\nResults:\")\n",
    "print(f\"Single Decision Tree Accuracy: {tree_accuracy:.4f}\")\n",
    "print(f\"Random Forest Accuracy: {forest_accuracy:.4f}\")\n",
    "print(f\"Improvement: {forest_accuracy - tree_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac7fec32",
   "metadata": {},
   "source": [
    "## 4. Visualizing Bias-Variance Trade-off\n",
    "\n",
    "One of the most important concepts in machine learning is the bias-variance trade-off. Let's see how Random Forest helps with this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6550385e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating bias-variance for different models...\n",
      "This may take a moment...\n",
      "Simulation complete!\n"
     ]
    }
   ],
   "source": [
    "# Create a regression dataset to better visualize bias-variance\n",
    "np.random.seed(42)\n",
    "\n",
    "# Generate true function (with some complexity)\n",
    "def true_function(x):\n",
    "    return 0.5 * x**3 - 2 * x**2 + x + 1 + np.sin(3*x)\n",
    "\n",
    "# Create training data\n",
    "n_train = 100\n",
    "X_train_reg = np.random.uniform(-2, 2, n_train).reshape(-1, 1)\n",
    "y_train_reg = true_function(X_train_reg.ravel()) + np.random.normal(0, 0.5, n_train)\n",
    "\n",
    "# Create test data for evaluation\n",
    "X_test_reg = np.linspace(-2, 2, 100).reshape(-1, 1)\n",
    "y_test_true = true_function(X_test_reg.ravel())\n",
    "\n",
    "# Function to simulate bias-variance by training on multiple datasets\n",
    "def simulate_bias_variance(model_class, n_simulations=50, **model_params):\n",
    "    predictions = []\n",
    "    \n",
    "    for i in range(n_simulations):\n",
    "        # Generate new training data with same underlying function\n",
    "        X_sim = np.random.uniform(-2, 2, n_train).reshape(-1, 1)\n",
    "        y_sim = true_function(X_sim.ravel()) + np.random.normal(0, 0.5, n_train)\n",
    "        \n",
    "        # Train model\n",
    "        model = model_class(**model_params, random_state=i)\n",
    "        model.fit(X_sim, y_sim)\n",
    "        \n",
    "        # Predict on test set\n",
    "        y_pred = model.predict(X_test_reg)\n",
    "        predictions.append(y_pred)\n",
    "    \n",
    "    return np.array(predictions)\n",
    "\n",
    "print(\"Simulating bias-variance for different models...\")\n",
    "print(\"This may take a moment...\")\n",
    "\n",
    "# Simulate for single tree and random forest\n",
    "tree_predictions = simulate_bias_variance(DecisionTreeRegressor, max_depth=8)\n",
    "forest_predictions = simulate_bias_variance(RandomForestRegressor, n_estimators=50, max_depth=8)\n",
    "\n",
    "print(\"Simulation complete!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "646e980d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bias-Variance Analysis:\n",
      "Single Tree - Mean Variance: 0.2625\n",
      "Random Forest - Mean Variance: 0.1270\n",
      "Variance Reduction: 51.6%\n",
      "\n",
      "Single Tree - Mean Bias²: 0.0111\n",
      "Random Forest - Mean Bias²: 0.0180\n",
      "Bias² Change: 61.8%\n"
     ]
    }
   ],
   "source": [
    "# Analyze and visualize bias-variance\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "\n",
    "# Calculate bias and variance\n",
    "tree_mean_pred = np.mean(tree_predictions, axis=0)\n",
    "tree_var = np.var(tree_predictions, axis=0)\n",
    "tree_bias_squared = (tree_mean_pred - y_test_true)**2\n",
    "\n",
    "forest_mean_pred = np.mean(forest_predictions, axis=0)\n",
    "forest_var = np.var(forest_predictions, axis=0)\n",
    "forest_bias_squared = (forest_mean_pred - y_test_true)**2\n",
    "\n",
    "# Plot 1: Single Tree Predictions\n",
    "axes[0, 0].plot(X_test_reg.ravel(), y_test_true, 'r-', linewidth=3, label='True Function', alpha=0.8)\n",
    "for i in range(min(10, len(tree_predictions))):\n",
    "    axes[0, 0].plot(X_test_reg.ravel(), tree_predictions[i], 'b-', alpha=0.3, linewidth=1)\n",
    "axes[0, 0].plot(X_test_reg.ravel(), tree_mean_pred, 'b-', linewidth=2, label='Average Prediction')\n",
    "axes[0, 0].set_title('Single Decision Tree\\n(High Variance)')\n",
    "axes[0, 0].set_xlabel('X')\n",
    "axes[0, 0].set_ylabel('Y')\n",
    "axes[0, 0].legend()\n",
    "axes[0, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 2: Random Forest Predictions\n",
    "axes[0, 1].plot(X_test_reg.ravel(), y_test_true, 'r-', linewidth=3, label='True Function', alpha=0.8)\n",
    "for i in range(min(10, len(forest_predictions))):\n",
    "    axes[0, 1].plot(X_test_reg.ravel(), forest_predictions[i], 'g-', alpha=0.3, linewidth=1)\n",
    "axes[0, 1].plot(X_test_reg.ravel(), forest_mean_pred, 'g-', linewidth=2, label='Average Prediction')\n",
    "axes[0, 1].set_title('Random Forest\\n(Lower Variance)')\n",
    "axes[0, 1].set_xlabel('X')\n",
    "axes[0, 1].set_ylabel('Y')\n",
    "axes[0, 1].legend()\n",
    "axes[0, 1].grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 3: Variance Comparison\n",
    "axes[1, 0].plot(X_test_reg.ravel(), tree_var, 'b-', linewidth=2, label='Single Tree')\n",
    "axes[1, 0].plot(X_test_reg.ravel(), forest_var, 'g-', linewidth=2, label='Random Forest')\n",
    "axes[1, 0].set_title('Prediction Variance')\n",
    "axes[1, 0].set_xlabel('X')\n",
    "axes[1, 0].set_ylabel('Variance')\n",
    "axes[1, 0].legend()\n",
    "axes[1, 0].grid(True, alpha=0.3)\n",
    "\n",
    "# Plot 4: Bias Comparison\n",
    "axes[1, 1].plot(X_test_reg.ravel(), tree_bias_squared, 'b-', linewidth=2, label='Single Tree')\n",
    "axes[1, 1].plot(X_test_reg.ravel(), forest_bias_squared, 'g-', linewidth=2, label='Random Forest')\n",
    "axes[1, 1].set_title('Prediction Bias²')\n",
    "axes[1, 1].set_xlabel('X')\n",
    "axes[1, 1].set_ylabel('Bias²')\n",
    "axes[1, 1].legend()\n",
    "axes[1, 1].grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Calculate overall statistics\n",
    "print(\"Bias-Variance Analysis:\")\n",
    "print(f\"Single Tree - Mean Variance: {np.mean(tree_var):.4f}\")\n",
    "print(f\"Random Forest - Mean Variance: {np.mean(forest_var):.4f}\")\n",
    "print(f\"Variance Reduction: {(np.mean(tree_var) - np.mean(forest_var))/np.mean(tree_var)*100:.1f}%\")\n",
    "print(f\"\\nSingle Tree - Mean Bias²: {np.mean(tree_bias_squared):.4f}\")\n",
    "print(f\"Random Forest - Mean Bias²: {np.mean(forest_bias_squared):.4f}\")\n",
    "print(f\"Bias² Change: {(np.mean(forest_bias_squared) - np.mean(tree_bias_squared))/np.mean(tree_bias_squared)*100:.1f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c515c227",
   "metadata": {},
   "source": [
    "## 5. Feature Importance in Random Forest\n",
    "\n",
    "One of the great advantages of Random Forest is that it provides feature importance scores, helping us understand which features are most useful for prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1a7e400a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breast Cancer Dataset:\n",
      "Samples: 569\n",
      "Features: 30\n",
      "Classes: ['malignant' 'benign']\n",
      "\n",
      "First 10 features: [np.str_('mean radius'), np.str_('mean texture'), np.str_('mean perimeter'), np.str_('mean area'), np.str_('mean smoothness'), np.str_('mean compactness'), np.str_('mean concavity'), np.str_('mean concave points'), np.str_('mean symmetry'), np.str_('mean fractal dimension')]\n",
      "\n",
      "Random Forest Accuracy: 0.9357\n"
     ]
    }
   ],
   "source": [
    "# Let's use the breast cancer dataset for a more interpretable example\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "# Load the dataset\n",
    "cancer_data = load_breast_cancer()\n",
    "X_cancer = cancer_data.data\n",
    "y_cancer = cancer_data.target\n",
    "feature_names = cancer_data.feature_names\n",
    "\n",
    "print(f\"Breast Cancer Dataset:\")\n",
    "print(f\"Samples: {X_cancer.shape[0]}\")\n",
    "print(f\"Features: {X_cancer.shape[1]}\")\n",
    "print(f\"Classes: {cancer_data.target_names}\")\n",
    "print(f\"\\nFirst 10 features: {list(feature_names[:10])}\")\n",
    "\n",
    "# Split the data\n",
    "X_train_cancer, X_test_cancer, y_train_cancer, y_test_cancer = train_test_split(\n",
    "    X_cancer, y_cancer, test_size=0.3, random_state=42, stratify=y_cancer\n",
    ")\n",
    "\n",
    "# Train Random Forest\n",
    "rf_cancer = RandomForestClassifier(\n",
    "    n_estimators=100,\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")\n",
    "rf_cancer.fit(X_train_cancer, y_train_cancer)\n",
    "\n",
    "# Get feature importance\n",
    "feature_importance = rf_cancer.feature_importances_\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'feature': feature_names,\n",
    "    'importance': feature_importance\n",
    "}).sort_values('importance', ascending=False)\n",
    "\n",
    "# Calculate accuracy\n",
    "cancer_accuracy = rf_cancer.score(X_test_cancer, y_test_cancer)\n",
    "print(f\"\\nRandom Forest Accuracy: {cancer_accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c20ca1f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0aeKIefLkyZo1a5a6dOkiSbrlllu0f/9+zZ8/X48++mi29yksLExz5syRYRiqWbOmfvrpJ82ZM0eDBg3Sr7/+qrVr1+qbb75R8+bNHdcNCwvTxx9/rO7du2d5TqvVqqVLl8r37z/w+/btq02bNunll1+Wn5+fSpYsKW9vb5UvX95xzLFjx9SwYUM1btxYkr0XZXZSU1OVmprqWE9KSrIv2Gz2fyh46bkm385Dzp3Lyfm+9gssxZHVapXNZiMXTkK+nY+cOxf5dj6zc36z142NjZXNZtOAAQM0ceJE+fn5OfaVLFlS4eHhatas2c2GCbi+1aul/fslf39p+HCzowEAAEAO8lz4q1evnmPZ3d1dgYGBqlu3rmNbSEiIJCkhIUGSvWfdV199JZ8sxn6Pi4tTjRo1FBcXp5iYGO3cuVOnTp1yvLk7duxYhsLf1dcODQ11XCerwt++fftUqVIlRwHtWgcOHFDHjh0zbLvzzjsVGxsri8Xi6Bl49TUNw1D58uUdz23fvn1q2LBhtkPDpKSkaOLEiVq3bp3++OMPXblyRRcvXnT0+JPsvf4WL16smJgY2Ww2vf/++4qOjpYknTx5UsePH9fAgQMdhVVJunLlSoY3rFlp2rRphqFsmjVrplmzZslisejAgQMqUaKE/vWvfzn2BwYGqmbNmjpw4EC25wwPD3cU/ST7PUjPRXYGDx6srl27au/evXrggQfUqVMnR7HxWlOnTs1yfo3SqedUqoQlx+sgv9jklZYsGdLf/0GBI+fO5dx8JyTw2mW1WpWYmCibzSY3N6YWLmjk2/nIuXORb+czO+fpUwvcqEcffdQxSsx9992nSpUq5UdYQPFitUqTJtmXo6Ol63weAQAAAHPlufDn4eGRYd0wjAzb0otN6cU7q9WqDh06aPr06ZnOlV6869Chg8LCwrRw4UJVqFBBVqtVERERSktLy/ba117nWqVKlcrxeWQ1x4Mtix4gWT3f9Gte7xpjxozRZ599ppkzZ6patWoqVaqUunXrluF5Pfzwwxo7dqz27t2rixcv6vjx4+rVq1eG57Zw4cIMRTpJjsLkjcjqeaZvz2rei3Q55SI7bdu21W+//aZPPvlEX3zxhe69914NHTpUM2fOzNR23LhxGjVqlGM9KSlJYWFhSvH0l6VUmRyvg3xis0k2KdkrQMrhZwH5iJw7l5PzHRwcWODXKOysVqsMw1BQUBAf0jsB+XY+cu5c5Nv5zM65l5fXTZ+jRIkSGjJkSI5fcgSQg48/lv77X6lMGempp8yOBgAAANeR58JfXjVq1EirV69WeHi4SpTIfLnTp0/rwIEDmj9/vlq0aCFJ2rZt201ft169ejpx4oRjONFr1a5dO9N1tm/frho1auS6qFavXj0tWrRIZ86cybLX39atWxUVFaXOnTtLss/5d/To0QxtKlWqpJYtW2r58uW6ePGi7rvvPkevyZCQEFWsWFGHDx92zPmXWzt37sy0Xr16dbm7u6t27dq6cuWKdu3a5eh9d/r0aR08eFC33XZbnq5ztZIlS8piydy7JSgoSFFRUYqKilKLFi00ZsyYLAt/np6e8vT0zHxiw6Ag4kzp+SbnzkPOncuJ+eZDaTvDMOTm5kY+nIR8Ox85dy7y7Xxm5jy/rvmvf/1L33//vapUqZIv5wOKDZvtn95+I0ZIAQHmxgMAAIDrKvDC39ChQ7Vw4UL17t1bY8aMUbly5XTo0CGtWLFCCxcuVEBAgAIDA7VgwQKFhobq2LFjGjt27E1fNzIyUi1btlTXrl01e/ZsVatWTf/73/9kGIbatGmjp59+Wk2aNNHkyZPVs2dP7dixQ2+88Ybmzp2b62v07t1bU6ZMUadOnTR16lSFhobq+++/V4UKFdSsWTNVq1ZNa9asUYcOHWQYhmJiYrLsIdenTx+9+OKLSktL05w5czLse/HFF/XUU0+pTJkyatu2rVJTU/Xdd9/p7NmzGXrHXev48eMaNWqUnnjiCe3du1evv/66Zs2aJUmqXr26OnbsqEGDBmn+/Pny9fXV2LFjVbFixUzDn+ZFeHi4du3apaNHj8rHx0dly5bViy++qNtvv1116tRRamqq1q1bd1PFRQAAAADIqyFDhujpp5/WiRMndPvtt6t06dIZ9l89xQOAq6xdK/3wg+TjYx/mEwAAAIVegX9ls0KFCvrmm29ksVjUunVrRUREaMSIEfLz83N8a3TFihXas2ePIiIiNHLkSM2YMSNfrr169Wo1adJEvXv3Vu3atfXMM884eqQ1atRIK1eu1IoVKxQREaHx48dr0qRJioqKyvX5S5Ysqc8//1zBwcFq166d6tatq2nTpjl6DM6ZM0cBAQFq3ry5OnTooNatW6tRo0aZztO9e3edPn1aFy5cUKdOnTLse+yxx7Ro0SItXbpUdevWVWRkpJYuXapbbrklx9j69eunixcv6o477tDQoUM1fPhwPf744479S5Ys0e2336727durWbNmstls+vTTTzMN55kXo0ePdvQoDAoK0rFjx1SyZEmNGzdO9erVU8uWLeXu7q4VK1bc8DUAAAAAIK969uypI0eO6KmnntKdd96pBg0aqGHDho5HAFm4urff8OFSFiMdAQAAoPAxbNlN+IYiq1WrVmrQoIFiY2PNDuWmJCUlyc/PTxO2xMnLlzn+nMJmk8/Fs0ouxXxzTkPOncvJ+R7bsFyBX6Ows1qtSkhIUHBwMMPyOQH5dj5y7lzk2/nMznn6e4LExESVKXPj7wl+++23HPcX9iFA85IHs+8ZCl6B32OLRdq6Vfr0U2nGDMnbW/rtN6kcf9s6C7/HxQP32fVxj10f99j1FaZ7nJf3BAU+1CcAAAAAAGYq7IU9oNBYs8Y+l9+JE/9sc3eXvv5a6tLFvLgAAACQaxT+AAAAAAAuLy4uTrGxsTpw4IAMw9Btt92mESNGqGrVqmaHBhQOa9ZI3brZh/i8WnKyffuHH1L8AwAAKALof+qCNm/eXOSH+QQAAACA/PLZZ5+pdu3a2r17t+rVq6eIiAjt2rVLderU0caNG80ODzCfxWLv6ZfVbDDp26Kj7e0AAABQqNHjDwAAAADg0saOHauRI0dq2rRpmbY/++yzuv/++02KDCgktm7NOLzntWw26fhxe7tWrZwWFgAAAPKOHn8AAAAAAJd24MABDRw4MNP2AQMGaP/+/SZEBBQy8fH52w4AAACmofAHAAAAAHBpQUFB2rdvX6bt+/btU3BwsPMDAgqb0ND8bQcAAADTMNQnAAAAAMClDRo0SI8//rgOHz6s5s2byzAMbdu2TdOnT9fTTz9tdniA+Vq0kCpVyn64T8Ow72/RwrlxAQAAIM8o/AEAAAAAXFpMTIx8fX01a9YsjRs3TpJUoUIFvfjii3rqqadMjg4oBNzdpZdekqKiMu8zDPtjbKy9HQAAAAo1Cn8AAAAAAJdmGIZGjhypkSNH6vz585IkX19fk6MCCpn0+fs8PKTLl//ZXqmSvejXpYspYQEAACBvKPwBAAAAAIqFhIQE/fLLLzIMQzVr1lRQUJDZIQGFw+XL0htv2JcXLJDCw+2FwNBQ+/Ce9PQDAAAoMij8AQAAAABcWlJSkoYOHar3339fVqtVkuTu7q6ePXvqzTfflJ+fn8kRAiZbvVr6/XcpJETq3Vvy9DQ7IgAAANwgCn8o9KLrlZW/v7/ZYRQLVqtVCQkWBQcHys3NzexwigVy7lzkGwCA4umxxx7Tvn379Mknn6hZs2YyDEPbt2/XiBEjNGjQIK1cudLsEAFzxcbaH4cOpegHAABQxFH4AwAAAAC4tE8++USfffaZ7rrrLse21q1ba+HChWrTpo2JkQGFwI4d0q5d9oLfE0+YHQ0AAABuEt0dAAAAAAAuLTAwMMvhPP38/BQQEGBCREAhkt7br08fKTjY1FAAAABw8yj8AQAAAABc2gsvvKBRo0YpPj7ese3PP//UmDFjFBMTY2JkgMmOHbPP7ydJI0aYGwsAAADyBUN9AgAAAABc2rx583To0CFVqVJFlStXliQdO3ZMnp6eOnnypObPn+9ou3fvXrPCBJzvzTcli0W65x6pXj2zowEAAEA+oPAHAAAAAHBpnTp1ytfzzZ07VzNmzFB8fLzq1Kmj2NhYtWjRItv2qampmjRpkt577z39+eefqlSpkp5//nkNGDAgX+MC8iQlRVqwwL4cHW1qKAAAAMg/FP4AAAAAAC5twoQJ+XauDz74QNHR0Zo7d67uvPNOzZ8/X23bttX+/fsdvQmv1aNHD/311196++23Va1aNSUkJOjKlSv5FhNwQ955Rzp3TqpWTXrwQbOjAQAAQD6h8AcAAAAAKDaSk5NltVozbCtTpkyuj589e7YGDhyoxx57TJIUGxurzz77TPPmzdPUqVMztd+wYYO2bNmiw4cPq2zZspKk8PDwG38CQH6wWqVXX7UvjxghubmZGw8AAADyDYU/FHqxP56Rly/fhnUKm00+F5OU/Ie7ZBhmR1M8kHOHsQ3LmR0CAABwUUeOHNGwYcO0efNmXbp0ybHdZrPJMAxZLJZcnSctLU179uzR2LFjM2x/4IEHtH379iyPWbt2rRo3bqxXXnlF7777rkqXLq2HHnpIkydPVqlSpW78SQE3Y8MG6ZdfJD8/KSrK7GgAAACQjyj8AQAAAABcWp8+fSRJixcvVkhIiIwb/MLVqVOnZLFYFBISkmF7SEiI/vzzzyyPOXz4sLZt2yYvLy999NFHOnXqlIYMGaIzZ85o8eLFWR6Tmpqq1NRUx3pSUpIkyWq1ZuqteC2r1SqbzXbddii68uMeG3PmyJBkGzhQNm9vew9AFBr8HhcP3GfXxz12fdxj11eY7nFeYqDwBwAAAABwaT/++KP27NmjmjVr5sv5ri0cpvcczIrVapVhGFq+fLn8/Pwk2YcL7datm958880se/1NnTpVEydOzLT95MmTGXosZne9xMRE2Ww2uTF8o0u62Xtc4pdfVO6LL2Rzc9OpXr1kSUgogChxM/g9Lh64z66Pe+z6uMeurzDd4/Pnz+e6LYU/AAAAAIBLa9KkiY4fP37Thb9y5crJ3d09U+++hISETL0A04WGhqpixYqOop8k3XbbbbLZbDpx4oSqV6+e6Zhx48Zp1KhRjvWkpCSFhYUpKCjouvMRphcag4KCTP9wAgXjZu+xERNjX+jUSYG3357P0SE/8HtcPHCfXR/32PVxj11fYbrHXl5euW5L4Q8AAAAA4NIWLVqkJ598Ur///rsiIiLk4eGRYX+9evVydZ6SJUvq9ttv18aNG9W5c2fH9o0bN6pjx45ZHnPnnXdq1apVSk5Olo+PjyTp4MGDcnNzU6VKlbI8xtPTU56enpm2u7m55eoDB8Mwct0WRdMN3+NTp6T33rOfY+RIGfyMFFr8HhcP3GfXxz12fdxj11dY7nFerk/hDwAAAADg0k6ePKm4uDj179/fsc0wDMcQnRaLJdfnGjVqlPr27avGjRurWbNmWrBggY4dO6Ynn3xSkr233u+//6533nlHkvTwww9r8uTJ6t+/vyZOnKhTp05pzJgxGjBgQJbDfAIFasEC6dIlqXFj6c47zY4GAAAABYDCHwAAAADApQ0YMEANGzbU+++/r5CQkGzn48uNnj176vTp05o0aZLi4+MVERGhTz/9VFWqVJEkxcfH69ixY472Pj4+2rhxo4YPH67GjRsrMDBQPXr00EsvvXTTzwvIk7Q06Y037MvR0dJN/B4AAACg8KLwBwAAAABwab/99pvWrl2ratWq5cv5hgwZoiFDhmS5b+nSpZm21apVSxs3bsyXawM3bNUqKT5eCg2Vunc3OxoAAAAUEAaeBQAAAAC4tHvuuUc//PCD2WEA5rHZpNhY+/LQoVLJkqaGAwAAgIJDjz8AAAAAgEvr0KGDRo4cqZ9++kl169aVh4dHhv0PPfSQSZEBTrJ9u/Tdd5KXl/T442ZHAwAAgAJE4Q8AAAAA4NKefPJJSdKkSZMy7TMMQxaLxdkhAc6V3tvvkUekoCBTQwEAAEDBovAHAAAAAHBpVqvV7BAA8/z2m7RmjX15xAhzYwEAAECBY44/3JCoqCh16tTJ7DAAAAAAAEBOXn9dslql+++XIiLMjgYAAAAFjB5/uCGvvvqqbDabY71Vq1Zq0KCBYtOHDwEAAAAAk7322mu5avfUU08VcCSASc6flxYtsi9HR5saCgAAAJyDwh9uiJ+fn9khAAAAAECO5syZc902hmFQ+IPrWrZMSkyUatSQ2rQxOxoAAAA4gUsM9dmqVSsNHz5c0dHRCggIUEhIiBYsWKCUlBT1799fvr6+qlq1qtavX5/huP3796tdu3by8fFRSEiI+vbtq1OnTjn2b9iwQXfddZf8/f0VGBio9u3bKy4uzrH/6NGjMgxDa9as0d133y1vb2/Vr19fO3bsyDHec+fO6fHHH1dISIi8vLwUERGhdevWOfavXr1aderUkaenp8LDwzVr1qwMx4eHh2vKlCkaMGCAfH19VblyZS1YsCBDmxMnTqhXr14qW7asSpcurcaNG2vXrl2SpLi4OHXs2FEhISHy8fFRkyZN9MUXXziOHTdunJo2bZop7nr16mnChAmSMg71GRUVpS1btujVV1+VYRgyDENHjhxRtWrVNHPmzAzn+O9//ys3N7cMeQQAAACAgnDkyJHr/jt8+LDZYQIFw2qVXn3VvjxihOTmEh8BAQAA4DpcpsffsmXL9Mwzz2j37t364IMPNHjwYH388cfq3LmznnvuOc2ZM0d9+/bVsWPH5O3trfj4eEVGRmrQoEGaPXu2Ll68qGeffVY9evTQl19+KUlKSUnRqFGjVLduXaWkpGj8+PHq3Lmz9u3bJ7er/mB+/vnnNXPmTFWvXl3PP/+8evfurUOHDqlEiczptVqtatu2rc6fP6/33ntPVatW1f79++Xu7i5J2rNnj3r06KEXX3xRPXv21Pbt2zVkyBAFBgYqKirKcZ5Zs2Zp8uTJeu655/Thhx9q8ODBatmypWrVqqXk5GRFRkaqYsWKWrt2rcqXL6+9e/c6JrRPTk5Wu3bt9NJLL8nLy0vLli1Thw4d9Msvv6hy5crq06ePpk2bpri4OFWtWlWS9PPPP+unn37Shx9+mOk5vfrqqzp48KAiIiI0adIkSVJQUJAGDBigJUuWaPTo0Y62ixcvVosWLRznvVpqaqpSU1Md60lJSfYFm83+DwUvPdfk23nIuUP6a1RBX8NmsznlWrAj585Fvp2PnDsX+XY+s3POvQZuwqefSocOSf7+Ur9+ZkcDAAAAJzFstqL/aXOrVq1ksVi0detWSZLFYpGfn5+6dOmid955R5L0559/KjQ0VDt27FDTpk01fvx47dq1S5999pnjPCdOnFBYWJh++eUX1ahRI9N1Tp48qeDgYP3000+KiIjQ0aNHdcstt2jRokUaOHCgJHsvwjp16ujAgQOqVatWpnN8/vnnatu2rQ4cOJDlNfr06aOTJ0/q888/d2x75pln9Mknn+jnn3+WZO/x16JFC7377ruSJJvNpvLly2vixIl68skntWDBAo0ePVpHjx5V2bJlc5XDOnXqaPDgwRo2bJgkqX79+urWrZtiYmIkSc8995y++OIL7d69W5K9l9+5c+f08ccfO+7BtXP8xcfHKywsTNu3b9cdd9yhy5cvq2LFipoxY4YeffTRTDG8+OKLmjhxYqbtr3z2nUqV9s3V88DNsskrNVmXPH0kGWYHU0yQ83TdqpYp8GtYrVYlJibKz88vwxc4UHDIuXORb+cj585Fvp3P7JyfP39eNWrUUGJiosqUKfi/FQqrpKQk+fn55SoPVqtVCQkJCg4O5vfEReX6Ht93n7RpkzRmjPTKK84LEDeN3+Pigfvs+rjHro977PoK0z3Oy3sCl+nxV69ePceyu7u7AgMDVbduXce2kJAQSVJCQoIke8+6r776Sj4+PpnOFRcXpxo1aiguLk4xMTHauXOnTp065fi26bFjxxQREZHltUNDQx3Xyarwt2/fPlWqVCnLop8kHThwQB07dsyw7c4771RsbKwsFoujZ+DV1zQMQ+XLl3c8t3379qlhw4bZFv1SUlI0ceJErVu3Tn/88YeuXLmiixcv6tixY442ffr00eLFixUTEyObzab3339f0XmcCDw0NFQPPvigFi9erDvuuEPr1q3TpUuX1L179yzbjxs3TqNGjXKsJyUlKSwsTCme/rKUKr5v8p3KZpNsUrJXgGQU7yKU05Bzh+DgwAK/htVqlWEYCgoKMv1/1sUFOXcu8u185Ny5yLfzmZ1zLy8vp18TcAk//WQv+rm7S39/wRcAAADFg8sU/jw8PDKsG4aRYZvx9wfq6cU7q9WqDh06aPr06ZnOlV6869Chg8LCwrRw4UJVqFBBVqtVERERSktLy/ba117nWqVKlcrxedhsNsc5rt52rayeb/o1r3eNMWPG6LPPPtPMmTNVrVo1lSpVSt26dcvwvB5++GGNHTtWe/fu1cWLF3X8+HH16tUrx/Nm5bHHHlPfvn01Z84cLVmyRD179pS3t3eWbT09PeXp6Zl5h2EU+4KIU6Xnm5w7DzmXJKd9mGgYhtzc3PjA2InIuXORb+cj585Fvp3PzJxzn4EblD4aT9euUuXKpoYCAAAA53KZwl9eNWrUSKtXr1Z4eHiWc/GdPn1aBw4c0Pz589WiRQtJ0rZt2276uvXq1dOJEyd08ODBLHv91a5dO9N1tm/frho1ajh6++XmGosWLdKZM2ey7PW3detWRUVFqXPnzpLsc/4dPXo0Q5tKlSqpZcuWWr58uS5evKj77rvP0WsyKyVLlpTFYsm0vV27dipdurTmzZun9evX6+uvv87VcwAAAAAAADcgIUFavty+nMeRewAAAFD0FduvTw4dOlRnzpxR7969tXv3bh0+fFiff/65BgwYIIvFooCAAAUGBmrBggU6dOiQvvzyywzDUN6oyMhItWzZUl27dtXGjRt15MgRrV+/Xhs2bJAkPf3009q0aZMmT56sgwcPatmyZXrjjTc0evToXF+jd+/eKl++vDp16qRvvvlGhw8f1urVq7Vjxw5JUrVq1bRmzRrt27dPP/zwgx5++OEseyj26dNHK1as0KpVq/TII4/keM3w8HDt2rVLR48ezTAsqru7u6KiojRu3DhVq1ZNzZo1y/XzAAAAAID8EhcXpxdeeEG9e/d2TJOwYcMGx1zqgMuYP19KTZXuuENq2tTsaAAAAOBkxbbwV6FCBX3zzTeyWCxq3bq1IiIiNGLECMek9W5ublqxYoX27NmjiIgIjRw5UjNmzMiXa69evVpNmjRR7969Vbt2bT3zzDOO3nKNGjXSypUrtWLFCkVERGj8+PGaNGmSoqKicn3+kiVL6vPPP1dwcLDatWununXratq0aY4eg3PmzFFAQICaN2+uDh06qHXr1mrUqFGm83Tv3l2nT5/WhQsX1KlTpxyvOXr0aLm7u6t27doKCgrKMF/gwIEDlZaWpgEDBuT6OQAAAABAftmyZYvq1q2rXbt2ac2aNUpOTpYk/fjjj5owYYLJ0QH5KDVVmjvXvhwdXeynEwAAACiODFtWE8gB+eibb75Rq1atdOLEiRyHC71WUlKS/Pz8NGFLnLx8yxRghHCw2eRz8aySSwXwBtFZyLnD2IblCvwaVqtVCQkJCg4OZs4gJyHnzkW+nY+cOxf5dj6zc57+niAxMVFlytz4e4JmzZqpe/fuGjVqlHx9ffXDDz/o1ltv1bfffqtOnTrp999/z8eo819e8mD2PUPBy/Eev/uu1K+fVKGCdPSo5OFhSoy4OfweFw/cZ9fHPXZ93GPXV5jucV7eE/DTiAKTmpqqQ4cOKSYmRj169MhT0Q8AAAAA8stPP/3kmOP8akFBQTp9+rQJEQEFwGaT5syxLw8bRtEPAACgmKLwhwLz/vvvq2bNmkpMTNQrr7xidjgAAAAAiil/f3/Fx8dn2v7999+rYsWKJkQEFICtW6Xvv5dKlZIef9zsaAAAAGASCn8oMFFRUbJYLNqzZw9vpgEAAACY5uGHH9azzz6rP//8U4ZhyGq16ptvvtHo0aPVr18/s8MD8kdsrP2xXz8pMNDUUAAAAGAeCn8AAAAAAJf28ssvq3LlyqpYsaKSk5NVu3ZttWzZUs2bN9cLL7xgdnjAzTt8WPr4Y/vyU0+ZGgoAAADMVcLsAAAAAAAAKEgeHh5avny5Jk2apO+//15Wq1UNGzZU9erVzQ4NyB9vvGGf4691a6l2bbOjAQAAgIko/AEAAAAAXNqWLVsUGRmpqlWrqmrVqmaHA+SvpCRp0SL7cnS0qaEAAADAfAz1CQAAAABwaffff78qV66ssWPH6r///a/Z4QD5a+lS6fx5qVYt6YEHzI4GAAAAJqPwBwAAAABwaX/88YeeeeYZbd26VfXq1VO9evX0yiuv6MSJE2aHBtwci0V67TX78ogRkhsf8wAAABR3/EUIAAAAAHBp5cqV07Bhw/TNN98oLi5OPXv21DvvvKPw8HDdc889ZocH3Lh166S4OCkgQOrXz+xoAAAAUAhQ+AMAAAAAFBu33HKLxo4dq2nTpqlu3brasmWL2SEBNy421v74xBOSt7epoQAAAKBwKGF2AMD1RNcrK39/f7PDKBasVqsSEiwKDg6UG0PEOAU5BwAAcJ5vvvlGy5cv14cffqhLly7poYce0pQpU8wOC7gx+/ZJmzdL7u7S0KFmRwMAAIBCgsIfAAAAAMClPffcc3r//ff1xx9/6L777lNsbKw6deokb3pIoaixWKQtW+T1yy8y/u//7Nu6d5cqVTI3LgAAABQaFP4AAAAAAC5t8+bNGj16tHr27Kly5cqZHQ5wY9askUaMkNuJE/K/env9+iYFBAAAgMKIwh8AAAAAwKVt377d7BCAm7NmjdStm2SzZd733HNSjRpSly7OjwsAAACFDoU/AAAAAIDLWbt2rdq2bSsPDw+tXbs2x7YPPfSQk6ICboDFIo0YkXXRL110tNSxo32+PwAAABRrFP4AAAAAAC6nU6dO+vPPPxUcHKxOnTpl284wDFksFucFBuTV1q3SiRPZ77fZpOPH7e1atXJaWAAAACicKPwBAAAAAFyO1WrNchkocuLj87cdAAAAXJqb2QEAAAAAAFCQ3nnnHaWmpmbanpaWpnfeeceEiIA8CA3N33YAAABwaRT+AAAAAAAurX///kpMTMy0/fz58+rfv78JEQF50KKFVKmSZBhZ7zcMKSzM3g4AAADFHkN9otCL/fGMvHyvmB1G8WCzyedikpL/cM/+TSXy13VyPrZhOROCAgAAcC02m01GFn9rnThxQn5+fiZEBOSBu7v06qtSt26Z96X/XMfG2tsBAACg2KPwBwAAAABwSQ0bNpRhGDIMQ/fee69KlPjnLbDFYtGRI0fUpk0bEyMEcqlLF6l3b+nf/864vVIle9GvSxdTwgIAAEDhQ+EPAAAAAOCSOnXqJEnat2+fWrduLR8fH8e+kiVLKjw8XF27djUpOiAPrlyRtm6VJFmffVZJVaqoTM2acouMpKcfAAAAMqDwBwAAAABwSRMmTJAkhYeHq2fPnvLy8jI5IuAGrVsnHT8ulSsnjR+vS0lJKhMcLLm5mR0ZAAAAChn+QgQAAAAAuLRHH32Uoh+KtjfesD8+9pjEzzIAAAByQI8/AAAAAIBLs1gsmjNnjlauXKljx44pLS0tw/4zZ86YFBmQC//7n7Rpk71335NPmh0NAAAACjl6/AEAAAAAXNrEiRM1e/Zs9ejRQ4mJiRo1apS6dOkiNzc3vfjii2aHB+Rs7lz7Y/v2UpUq5sYCAACAQo/CHwAAAADApS1fvlwLFy7U6NGjVaJECfXu3VuLFi3S+PHjtXPnTrPDA7KXnCwtW2ZfHjrU3FgAAABQJFD4AwAAAAC4tD///FN169aVJPn4+CgxMVGS1L59e33yySdmhgbk7L33pKQkqXp16b77zI4GAAAARQCFPwAAAACAS6tUqZLi4+MlSdWqVdPnn38uSfr222/l6elpZmhA9mw26c037ctDhtjn+AMAAACug78aAQAAAAAurXPnztq0aZMkacSIEYqJiVH16tXVr18/DRgwwOTogGxs3Sr997+St7cUFWV2NAAAACgiSpgdAAAAAAAABWnatGmO5W7duqlSpUravn27qlWrpoceesjEyIAcvPGG/fGRRyR/f1NDAQAAQNFB4Q8AAAAAUKw0bdpUTZs2NTsMIHt//CF99JF9eehQc2MBAABAkULhD3lmsVhkGIbcmF8AAAAAQCG1du3aXLel1x8KnQULpCtXpLvukurVMzsaAAAAFCFUbgpAq1atNHz4cEVHRysgIEAhISFasGCBUlJS1L9/f/n6+qpq1apav359huP279+vdu3aycfHRyEhIerbt69OnTrl2L9hwwbddddd8vf3V2BgoNq3b6+4uDjH/qNHj8owDK1Zs0Z33323vL29Vb9+fe3YsSPHeGfPnq26deuqdOnSCgsL05AhQ5ScnOzYv3TpUvn7+2vdunWqXbu2PD099dtvvyktLU3PPPOMKlasqNKlS+tf//qXNm/e7Dju9OnT6t27typVqiRvb2/VrVtX77///k1mFwAAAACur1OnTrn617lzZ7NDBTK6fNle+JPo7QcAAIA8o/BXQJYtW6Zy5cpp9+7dGj58uAYPHqzu3burefPm2rt3r1q3bq2+ffvqwoULkqT4+HhFRkaqQYMG+u6777Rhwwb99ddf6tGjh+OcKSkpGjVqlL799ltt2rRJbm5u6ty5s6xWa4ZrP//88xo9erT27dunGjVqqHfv3rpy5Uq2sbq5uem1117Tf//7Xy1btkxffvmlnnnmmQxtLly4oKlTp2rRokX6+eefFRwcrP79++ubb77RihUr9OOPP6p79+5q06aNfv31V0nSpUuXdPvtt2vdunX673//q8cff1x9+/bVrl278ivNAAAAAJAlq9Waq38Wi8XsUIGMPvpIio+XQkKkLl3MjgYAAABFjGGz2WxmB+FqWrVqJYvFoq1bt0qyD43p5+enLl266J133pEk/fnnnwoNDdWOHTvUtGlTjR8/Xrt27dJnn33mOM+JEycUFhamX375RTVq1Mh0nZMnTyo4OFg//fSTIiIidPToUd1yyy1atGiRBg4cKMnei7BOnTo6cOCAatWqlav4V61apcGDBzt6Gy5dulT9+/fXvn37VL9+fUlSXFycqlevrhMnTqhChQqOY++77z7dcccdmjJlSpbnfvDBB3Xbbbdp5syZmfalpqYqNTXVsZ6UlKSwsDBN2HxIXr5lchU7bpLNJp+LZ5VcKkAyDLOjKR6uk/NnGgSaEJTrslqtOnnypIKCghiu2EnIuXORb+cj585Fvp3P7JwnJSUpICBAiYmJKlOm+L4nSEpKkp+fX67yYLValZCQoODgYH5PiqrISOnrr6WYGGnSpEy7uceuj3tcPHCfXR/32PVxj11fYbrHeXlPwBx/BaTeVWPwu7u7KzAwUHXr1nVsCwkJkSQlJCRIkvbs2aOvvvpKPj4+mc4VFxenGjVqKC4uTjExMdq5c6dOnTrl6Ol37NgxRUREZHnt0NBQx3WyK/x99dVXmjJlivbv36+kpCRduXJFly5dUkpKikqXLi1JKlmyZIbz7t27VzabLVNBMjU1VYGB9kKFxWLRtGnT9MEHH+j33393FPbSz3mtqVOnauLEiZm2l049p1Il+Bauc9jklZYsGdLf/0GByznnCQn87Ocnq9WqxMRE2Ww20/9nXVyQc+ci385Hzp2LfDuf2Tk/f/58vpxnUhbFk6uNHz8+X64D3LSffrIX/dzdpSeeMDsaAAAAFEEU/gqIh4dHhnXDMDJsM/7u2ZNevLNarerQoYOmT5+e6VzpxbsOHTooLCxMCxcuVIUKFWS1WhUREaG0tLRsr33tda7122+/qV27dnryySc1efJklS1bVtu2bdPAgQN1+fJlR7tSpUo5zpV+Pnd3d+3Zs0fu7u4ZzplevJw1a5bmzJmj2NhYxxyC0dHRmeJNN27cOI0aNcqxnt7jL8XTX5ZSxffbvU5ls0k2KdmLHn9Oc52cBwfT4y8/Wa1WGYZBTxEnIufORb6dj5w7F/l2PrNz7uXllS/n+eijjzKsX758WUeOHFGJEiVUtWpVCn8oPN580/7YubNUsaK5sQAAAKBIovBXSDRq1EirV69WeHi4SpTIfFtOnz6tAwcOaP78+WrRooUkadu2bTd93e+++05XrlzRrFmzHG/kV65ced3jGjZsKIvFooSEBEc819q6das6duyoRx55RJL9Q4Nff/1Vt912W5btPT095enpmXmHYVCEcqb0fJNz58kh53yomf8Mw5Cbmxu5dSJy7lzk2/nIuXORb+czM+f5dc3vv/8+07akpCRFRUWpc+fO+XIN4KYlJkrvvWdfHjrU3FgAAABQZPFuuZAYOnSozpw5o969e2v37t06fPiwPv/8cw0YMEAWi0UBAQEKDAzUggULdOjQIX355ZcZesfdqKpVq+rKlSt6/fXXdfjwYb377rt66623rntcjRo11KdPH/Xr109r1qzRkSNH9O2332r69On69NNPJUnVqlXTxo0btX37dh04cEBPPPGE/vzzz5uOGQAAAABuVpkyZTRp0iTFxMSYHQpgt2yZlJIi1aljn+cPAAAAuAEU/gqJChUq6JtvvpHFYlHr1q0VERGhESNGyM/Pz/Ht2hUrVmjPnj2KiIjQyJEjNWPGjJu+boMGDTR79mxNnz5dERERWr58uaZOnZqrY5csWaJ+/frp6aefVs2aNfXQQw9p165dCgsLkyTFxMSoUaNGat26tVq1aqXy5curU6dONx0zAAAAAOSHc+fOKTEx0ewwAPsUAHPn2peHDGEEFgAAANwww2az2cwOAshKUlKS/Pz8NGFLnLx8mePPKWw2+Vw8q+RSzPHnNNfJ+diG5UwIynVZrVYlJCQoODiYIeKchJw7F/l2PnLuXOTb+czOefp7gsTERJUpc+PvCV577bUM6zabTfHx8Xr33XfVsmVLvf/++zcbaoHKSx7Mvme4QV98Id1/v+TrK/3+u/0xG9xj18c9Lh64z66Pe+z6uMeurzDd47y8J2COPwAAAACAS5szZ06GdTc3NwUFBenRRx/VuHHjTIoKuMqbb9of+/XLsegHAAAAXA+FPwAAAACASzty5IjZIQDZO3ZMWrvWvjxkiLmxAAAAoMij/ykAAAAAAIBZ3npLslqle+6Ratc2OxoAAAAUcfT4AwAAAAC4tEuXLun111/XV199pYSEBFmt1gz79+7da1JkKPZSU6VFi+zLQ4eaGwsAAABcAoU/AAAAAIBLGzBggDZu3Khu3brpjjvukGEYZocE2K1aJZ08KVWqJD30kNnRAAAAwAVQ+AMAAAAAuLRPPvlEn376qe68806zQwEyevNN++MTT0gl+IgGAAAAN485/gAAAAAALq1ixYry9fU1Owwgo717pZ07JQ8PadAgs6MBAACAi6DwBwAAAABwabNmzdKzzz6r3377zexQgH+k9/br1k0KCTE3FgAAALgMxpEAAAAAALi0xo0b69KlS7r11lvl7e0tDw+PDPvPnDljUmQots6ckf79b/vy0KHmxgIAAACXQuEPAAAAAODSevfurd9//11TpkxRSEiIDMMwOyQUd4sXS5cuSfXrS82bmx0NAAAAXAiFPxR60fXKyt/f3+wwigWr1aqEBIuCgwPl5sZIwM5AzgEAAAre9u3btWPHDtWvX9/sUADJapXmzbMvDxsmUYgGAABAPuJTZgAAAACAS6tVq5YuXrxodhiA3YYN0uHDkr+/9PDDZkcDAAAAF0PhDwAAAADg0qZNm6ann35amzdv1unTp5WUlJThH+BUb75pf+zfX/L2NjcWAAAAuByG+gQAAAAAuLQ2bdpIku69994M2202mwzDkMViMSMsFEeHD0vr19uXBw82NxYAAAC4JAp/AAAAAACX9tVXX5kdAmA3b55ks0mtW0vVq5sdDQAAAFwQhT8AAAAAgEuLjIw0OwRAunhRWrzYvjx0qLmxAAAAwGVR+AMAAAAAuLSvv/46x/0tW7Z0UiQo1laskM6ckapUkdq1MzsaAAAAuCgKfwAAAAAAl9aqVatM2wzDcCwzxx8KnM0mvfGGfXnIEMnd3dx4AAAA4LLczA4AAAAAAICCdPbs2Qz/EhIStGHDBjVp0kSff/652eGhONi1S9q7V/L0lAYMMDsaAAAAuDB6/KHQi/3xjLx8r5gdRvFgs8nnYpKS/3CXrvoGdFE3tmE5s0MAAACAifz8/DJtu//+++Xp6amRI0dqz549JkSFYuXNN+2PvXpJ5Xh/AgAAgIJDjz8AAAAAQLEUFBSkX375xeww4OoSEqSVK+3LQ4eaGwsAAABcHj3+AAAAAAAu7ccff8ywbrPZFB8fr2nTpql+/fomRYVi4+23pbQ0qUkT+z8AAACgAFH4AwAAAAC4tAYNGsgwDNlstgzbmzZtqsWLF5sUFYoFi0V66y37Mr39AAAA4AQU/gAAAAAALu3IkSMZ1t3c3BQUFCQvLy+TIkKxsW6ddOyYFBgo9expdjQAAAAoBij8AQAAAABcWpUqVcwOAcXVG2/YHwcOlCg0AwAAwAnczA4AAAAAAICC8OWXX6p27dpKSkrKtC8xMVF16tTR1q1bTYgMxcIvv0hffCEZhjR4sNnRAAAAoJig8AcAAAAAcEmxsbEaNGiQypQpk2mfn5+fnnjiCc2ePduEyFAszJ1rf2zfXgoPNzUUAAAAFB8U/gAAAAAALumHH35QmzZtst3/wAMPaM+ePU6MCC7PYpE2b5aWLJEWLbJvGzrU1JAAAABQvDDHHwAAAADAJf3111/y8PDIdn+JEiV08uRJJ0YEl7ZmjTRihHTixD/b3N2l8+fNiwkAAADFDj3+AAAAAAAuqWLFivrpp5+y3f/jjz8qNDTUiRHBZa1ZI3XrlrHoJ9l7APboYd8PAAAAOAGFP9yQVq1aKTo62uwwAAAAACBb7dq10/jx43Xp0qVM+y5evKgJEyaoffv2JkQGl2Kx2Hv62WzZt4mOtrcDAAAAChiFv3xkGIY+/vhjs8NwijVr1mjy5MmO9fDwcMXGxpoXEAAAAABc44UXXtCZM2dUo0YNvfLKK/q///s/rV27VtOnT1fNmjV15swZPf/882aHiaJu69bMPf2uZrNJx4/b2wEAAAAFjDn+ciktLU0lS5Y0O4xCo2zZsmaHAAAAAAA5CgkJ0fbt2zV48GCNGzdOtr97ZBmGodatW2vu3LkKCQkxOUoUefHx+dsOAAAAuAku0ePvP//5j/z9/WW1WiVJ+/btk2EYGjNmjKPNE088od69ezvWV69erTp16sjT01Ph4eGaNWtWhnOGh4frpZdeUlRUlPz8/DRo0CClpaVp2LBhCg0NlZeXl8LDwzV16lRHe0nq3LmzDMNwrGflxIkT6tWrl8qWLavSpUurcePG2rVrl2P/vHnzVLVqVZUsWVI1a9bUu+++m+F4wzC0aNEide7cWd7e3qpevbrWrl2boc3PP/+sBx98UGXKlJGvr69atGihuLg4SdK3336r+++/X+XKlZOfn58iIyO1d+9ex7G9e/dWr169Mpzv8uXLKleunJYsWSIp41CfrVq10m+//aaRI0fKMAwZhqGUlBSVKVNGH374YaZ7Vbp0aZ1ncnMAAAAATlClShV9+umnOnXqlHbt2qWdO3fq1KlT+vTTT3N83wbkWm7niWQ+SQAAADiBSxT+WrZsqfPnz+v777+XJG3ZskXlypXTli1bHG02b96syMhISdKePXvUo0cP9erVSz/99JNefPFFxcTEaOnSpRnOO2PGDEVERGjPnj2KiYnRa6+9prVr12rlypX65Zdf9N577zneKH777beSpCVLlig+Pt6xfq3k5GRFRkbqjz/+0Nq1a/XDDz/omWeecRQtP/roI40YMUJPP/20/vvf/+qJJ55Q//799dVXX2U4z8SJE9WjRw/9+OOPateunfr06aMzZ85Ikn7//Xe1bNlSXl5e+vLLL7Vnzx4NGDBAV65ckSSdP39ejz76qLZu3aqdO3eqevXqateunaMY16dPH61du1bJycmO63322WdKSUlR165dMz2nNWvWqFKlSpo0aZLi4+MVHx+v0qVLq1evXo5CYbolS5aoW7du8vX1zeZuAgAAAED+CwgIUJMmTXTHHXcoICDA7HDgSlq0kCpVkgwj6/2GIYWF2dsBAAAABcwlhvr08/NTgwYNtHnzZt1+++3avHmzRo4cqYkTJ+r8+fNKSUnRwYMH1apVK0nS7Nmzde+99yomJkaSVKNGDe3fv18zZsxQVFSU47z33HOPRo8e7Vg/duyYqlevrrvuukuGYahKlSqOfUFBQZIkf39/lS9fPttY//3vf+vkyZP69ttvHcNlVqtWzbF/5syZioqK0pAhQyRJo0aN0s6dOzVz5kzdfffdjnZRUVGOHoxTpkzR66+/rt27d6tNmzZ688035efnpxUrVsjDw8PxHK9+XlebP3++AgICtGXLFrVv316tW7dW6dKl9dFHH6lv376OuDt06KAyZcpkek5ly5aVu7u7fH19Mzz3xx57TM2bN9cff/yhChUq6NSpU1q3bp02btyYZW5SU1OVmprqWE9KSrIv2Gw5T5KO/JOeaxfLd3phvTCyWq2y2WyFOkZXQr6dj5w7F/l2PnLuXOTb+czOOfcaRYK7u/Tqq1IWX5R1FANjY+3tAAAAgALmEoU/yT7c5ObNmzVq1Cht3bpVL730klavXq1t27bp3LlzCgkJUa1atSRJBw4cUMeOHTMcf+eddyo2NlYWi0Xuf/8x3rhx4wxtoqKidP/996tmzZpq06aN2rdvrwceeCBPce7bt08NGzbMdo68AwcO6PHHH88U26uvvpphW7169RzLpUuXlq+vrxISEhzXaNGihaPod62EhASNHz9eX375pf766y9ZLBZduHBBx44dkyR5eHioe/fuWr58ufr27auUlBT93//9n/7973/n6bnecccdqlOnjt555x2NHTtW7777ripXrqyWLVtm2X7q1KmaOHFipu2lU8+pVAlLnq6NG2WTV1qyZEh//8clJCQU3p8fq9WqxMRE2Ww2ubm5RCfsQo18Ox85dy7y7Xzk3LnIt/OZnXOmCECR0aWL1KCBtG9fxu2VKtmLfl26mBAUAAAAiiOXKvy9/fbb+uGHH+Tm5qbatWsrMjJSW7Zs0dmzZx3DfEqSzWaTcc0QHLYsejiVLl06w3qjRo105MgRrV+/Xl988YV69Oih++67L9M8djkpVarUddtkFdu1264t6hmG4fg27PWuERUVpZMnTyo2NlZVqlSRp6enmjVrprS0NEebPn36KDIyUgkJCdq4caO8vLzUtm3b68Z+rccee0xvvPGGxo4dqyVLlqh///6Znku6cePGadSoUY71pKQkhYWFKcXTX5ZSmXsaogDYbJJNSvYKyH6YmiIoODjQ7BCyZbVaZRiGgoKC+ADTCci385Fz5yLfzkfOnYt8O5/ZOffy8nL6NYEbcujQP0W/5cvt76dCQ+3De9LTDwAAAE7kMoW/9Hn+YmNjFRkZKcMwFBkZqalTp+rs2bMaMWKEo23t2rW1bdu2DMdv375dNWrUcPT2y06ZMmXUs2dP9ezZU926dVObNm105swZlS1bVh4eHrJYcu5ZVK9ePS1atMhxzLVuu+02bdu2Tf369csQ22233ZabNDiusWzZMl2+fDnLXn9bt27V3Llz1a5dO0nS8ePHderUqQxtmjdvrrCwMH3wwQdav369unfvrpIlS2Z7zZIlS2b53B955BE988wzeu211/Tzzz/r0UcfzfYcnp6e8vT0zLzDMFyqCFXopefbhXJe2D8YNAxDbm5uhT5OV0G+nY+cOxf5dj5y7lzk2/nMzDn3GUXG/Pn2x7ZtpYcfNjcWAAAAFGsu8y4qfZ6/9957zzGXX8uWLbV3794M8/tJ0tNPP61NmzZp8uTJOnjwoJYtW6Y33ngjw3x+WZkzZ45WrFih//3vfzp48KBWrVql8uXLy9/fX5IUHh6uTZs26c8//9TZs2ezPEfv3r1Vvnx5derUSd98840OHz6s1atXa8eOHZKkMWPGaOnSpXrrrbf066+/avbs2VqzZs11Y7vasGHDlJSUpF69eum7777Tr7/+qnfffVe//PKLJPucgu+++64OHDigXbt2qU+fPpl6CRqGoYcfflhvvfWWNm7cqEceeSTHa4aHh+vrr7/W77//nqGIGBAQoC5dumjMmDF64IEHVKlSpVw/DwAAAAAACr2LF6XFi+3LgwebGwsAAACKPZcp/EnS3XffLYvF4ijyBQQEqHbt2goKCsrQY65Ro0ZauXKlVqxYoYiICI0fP16TJk1SVFRUjuf38fHR9OnT1bhxYzVp0kRHjx7Vp59+6vgW6qxZs7Rx40aFhYWpYcOGWZ6jZMmS+vzzzxUcHKx27dqpbt26mjZtmqOnYadOnfTqq69qxowZqlOnjubPn68lS5ZkKFxeT2BgoL788kslJycrMjJSt99+uxYuXOjo/bd48WKdPXtWDRs2VN++ffXUU08pODg403n69Omj/fv3q2LFirrzzjtzvOakSZN09OhRVa1aVUFBQRn2DRw4UGlpaRowYECunwMAAAAAAEXCqlXSmTNS5crS3yPrAAAAAGYxbFlNbgfko+XLl2vEiBH6448/chwu9FpJSUny8/PThC1x8vJljj+nsNnkc/Gskku51hx/YxuWMzuEbFmtViUkJCg4OJihrJyAfDsfOXcu8u185Ny5yLfzmZ3z9PcEiYmJKlOm+L4nyEsezL5nxVLTptKuXdLLL0vPPVfgl+Meuz7ucfHAfXZ93GPXxz12fYXpHuflPYHLzPGHwufChQs6cuSIpk6dqieeeCJPRT8AAAAAAAq977+3F/08PKSBA82OBgAAAHCtoT5RuLzyyitq0KCBQkJCNG7cOLPDAQAAAAAgf82bZ3/s2lUKCTE3FgAAAEAU/lCAXnzxRV2+fFmbNm2Sj4+P2eEAAAAAAJB/EhOl5cvty4MHmxsLAAAA8DcKfwAAAAAAAHn1zjvShQtSnTpSixZmRwMAAABIovAHAAAAAACQNzbbP8N8Dh4sGYa58QAAAAB/o/AHAAAAAACQF1u2SAcOSKVLS337mh0NAAAA4EDhDwAAAAAAIC/mzrU/PvKIVKaMubEAAAAAV6HwBwAAAAAAkFvx8dJHH9mXBw82NxYAAADgGhT+AAAAAAAAcmvRIunKFal5c6l+fbOjAQAAADKg8AcAAAAAAJAbV65ICxbYl4cMMTcWAAAAIAsU/gAAAAAAAHJj3TrpxAmpXDmpWzezowEAAAAyKWF2AMD1RNcrK39/f7PDKBasVqsSEiwKDg6UmxvfCwAAAACADObNsz8OHCh5epobCwAAAJAFPtkHAAAAAAC4nkOHpM8/lwxDeuIJs6MBAAAAskThDwAAAAAA4Hreesv+2KaNdMst5sYCAAAAZIPCHwAAAAAAQE4uXpSWLLEvDxlibiwAAABADij8AQAAAAAA5GTlSunMGalKFaltW7OjAQAAALJF4Q8AAAAAACAn8+bZH594QnJ3NzcWAAAAIAcU/gAAAAD8f3t3Hh/Tvf9x/D1JSIgsliSiIrFvsa+pJai91FKNoip0R1GquC1iKXUV6XL1qvYXbttbFHW1RW2NWotUcCu1a7TVhhYJKiRzfn/kZmokCE3OJJPX8/FIZ+ac7znncz7fjOnJZ77fAwC4lW+/lb75RipSRHriCUdHAwAAANwWhT8AAAAAAIBbyRzt16eP5O/v2FgAAACAO3BzdADAnUQf+F0eXmmODqNwMAyV+CNZl352lSyWXN/9+AZlcn2fAAAAAJBnLlyQPvoo4/lzzzk0FAAAACAnGPEHAAAAAACQnX/9S/rjDyk0VGrZ0tHRAAAAAHdE4Q8AAAAAAOBmhvHnNJ/PPZcns6IAAAAAuY3CHwAAAAAAwM1iY6Xvv5dKlJAee8zR0QAAAAA5QuEPAAAAAADgZpmj/R57TPL2dmwsAAAAQA5R+AMAAAAAALjRmTPSp59mPH/uOcfGAgAAANwFCn8AAAAAAAA3eu89KS1NatFCqlvX0dEAAAAAOUbhDwAAAAAAIFNamrRgQcZzRvsBAACggKHwBwAAAAAAkOnzz6WffpLKlJH69HF0NAAAAMBdofAHAAAAAACQaf78jMcnnpDc3R0bCwAAAHCXKPwBAAAAAABI0tGj0oYNksUiPfOMo6MBAAAA7hqFPwAAAAAAAEn65z8zHrt0kSpWdGwsAAAAwD2g8AcAAAAAAPDHH1JMTMbzoUMdGwsAAABwjwpN4c9isWjVqlWODsNhFi1aJF9fX0eHAQAAAABA/rR0qXT+vBQcLHXu7OhoAAAAgHviFIW/a9euOToEAAAAAABQkL3zTsbjs89Krq6OjQUAAAC4R3le+Pvss8/k6+srq9UqSYqPj5fFYtHYsWNtbZ555hn169fP9nrFihWqXbu23N3dFRISojlz5tjtMyQkRNOnT1dkZKR8fHz01FzMM44AAGZeSURBVFNP6dq1axo+fLgCAwPl4eGhkJAQzZw509Zeknr16iWLxWJ7nZ0ff/xRjz76qEqVKiVPT081btxY33zzjW39O++8o8qVK6to0aKqXr26PvjgA7vtLRaLFixYoG7duql48eKqWbOmdu7cqWPHjqlNmzby9PRUWFiYjh8/btsmKipK9evX14IFCxQUFKTixYvrkUce0YULF2xt9uzZow4dOqhMmTLy8fFReHi4vv32W7tjX7hwQU8//bQCAgLk4eGh0NBQff7554qNjdXgwYN18eJFWSwWWSwWRUVF2XIzY8YMDRkyRF5eXqpQoYLeffddu/3+9NNP6tu3r0qWLKnSpUurR48eOnXqlG19bGysmjZtKk9PT/n6+qpFixb64YcfJEn79+9X27Zt5eXlJW9vbzVq1Eh79+69Zf4BAAAAADBdXJy0e7dUpIg0ZIijowEAAADumVteH6B169ZKSUnRvn371KhRI23ZskVlypTRli1bbG1iY2P1wgsvSJLi4uIUERGhqKgo9e3bVzt27NDQoUNVunRpRUZG2raZPXu2Jk6cqFdeeUWS9Oabb2r16tVatmyZKlSooNOnT+v06dOSMopm/v7+iomJUefOneV6i2/uXbp0SeHh4brvvvu0evVqlS1bVt9++62taPnpp59q5MiRio6OVvv27fX5559r8ODBKl++vNq2bWvbz7Rp0zR37lzNnTtX48aNU//+/VWpUiVNmDBBFSpU0JAhQzR8+HCtXbvWts2xY8e0bNkyffbZZ0pOTtYTTzyhYcOG6aOPPpIkpaSkaNCgQXrzzTclSXPmzFHXrl119OhReXl5yWq1qkuXLkpJSdGHH36oypUr69ChQ3J1ddX999+v6OhoTZo0SYcPH5YklShRwnbsOXPmaNq0afrb3/6m5cuX67nnnlPr1q1Vo0YNXblyRW3btlWrVq309ddfy83NTdOnT1fnzp114MABubi4qGfPnnrqqaf08ccf69q1a9q9e7csFoskacCAAWrQoIHeeecdubq6Kj4+XkWKFMk2/6mpqUpNTbW9Tk5OznhiGBk/yHuZuc6jfGe+l/Anq9UqwzDIjUnIt/nIubnIt/nIubnIt/kcnXP6GqbJHO33yCOSv79jYwEAAAD+gjwv/Pn4+Kh+/fqKjY1Vo0aNbEW+KVOmKCUlRZcvX9aRI0fUpk0bSdLcuXP1wAMPaOLEiZKkatWq6dChQ5o9e7Zd4a9du3Z68cUXba8TExNVtWpVtWzZUhaLRcHBwbZ1fn5+kiRfX1+VLVv2lrH++9//1tmzZ7Vnzx6VKlVKklSlShXb+tdff12RkZEa+r+bfI8ePVq7du3S66+/blf4Gzx4sCIiIiRJ48aNU1hYmCZOnKhOnTpJkkaOHKnBgwfbHfvq1atavHixypcvL0l666239OCDD2rOnDkqW7as2rVrZ9d+wYIFKlmypLZs2aJu3bpp48aN2r17txISElStWjVJUqVKlez6wWKxZHv+Xbt2tZ3TuHHjNG/ePMXGxqpGjRpasmSJXFxc9N5779mKeTExMfL19VVsbKwaN26sixcvqlu3bqpcubIkqWbNmnb9MnbsWNWoUUOSVLVq1Vvmf+bMmZoyZUqW5Z6pF1TMLf2W2yE3GfK4dkmySP/7T65KSqIfb2a1WnXx4kUZhiEXF6eYfTlfI9/mI+fmIt/mI+fmIt/mc3TOU1JSTD8mCqELF6R//zvj+XPPOTQUAAAA4K/K88KfJLVp00axsbEaPXq0tm7dqunTp2vFihXatm2bLly4oICAAFthKCEhQT169LDbvkWLFoqOjlZ6erpttF7jxo3t2kRGRqpDhw6qXr26OnfurG7duqljx453FWd8fLwaNGhgK/rdLCEhQU8//XSW2N544w27ZXXr1rU9DwgIkCTVqVPHbtnVq1eVnJwsb29vSVKFChVsRT9JCgsLk9Vq1eHDh1W2bFklJSVp0qRJ2rx5s3799Velp6frypUrSkxMtMVevnx5W9HvbtwYb2ZxMCkpSVLGCMxjx47Jy8vLbpurV6/q+PHj6tixoyIjI9WpUyd16NBB7du3V0REhAIDAyVlFEeffPJJffDBB2rfvr0eeeQRW4HwZhMmTNDo0aNtr5OTkxUUFKTL7r5KL+Z91+eFe2AYkiFd8igpWXK/8OfvXzrX91nQWa1WWSwW+fn58QdME5Bv85Fzc5Fv85Fzc5Fv8zk65x4eHqYfE4XQ4sXSH39IoaFSixaOjgYAAAD4S0wr/L3//vvav3+/XFxcVKtWLYWHh2vLli06f/68wsPDbW0Nw7CNLLtx2c08PT3tXjds2FAnT57U2rVrtXHjRkVERKh9+/Zavnx5juMsVqzYHdtkF9vNy26cyjJzXXbLbjdtTWabzMfIyEidPXtW0dHRCg4Olru7u8LCwnTt2rUcx34rN0+9abFYbLFZrVY1atTINuXojTJHUsbExGjEiBFat26dli5dqldeeUUbNmxQ8+bNFRUVpf79++uLL77Q2rVrNXnyZC1ZskS9evXKsj93d3e5u7tnl4w8KULhFjLznQc55w902bNYLHJxcSE/JiHf5iPn5iLf5iPn5iLf5nNkzvNrP8+fP1+zZ8/WmTNnVLt2bUVHR6tVq1Z33G779u0KDw9XaGio4uPj8z5Q3Jlh/DnN59ChXHsCAACgwDPlKirzPn/R0dEKDw+XxWJReHi4YmNjFRsba1f4q1WrlrZt22a3/Y4dO1StWrVb3psvk7e3t/r27auFCxdq6dKlWrFihX7//XdJGcWt9PTbTzNYt25dxcfH27a5Wc2aNbON7capLe9VYmKifv75Z9vrnTt3ysXFxTaCb+vWrRoxYoS6du2q2rVry93dXefOnbOL/ccff9SRI0ey3X/RokXveP7ZadiwoY4ePSp/f39VqVLF7sfHx8fWrkGDBpowYYJ27Nih0NBQ/TtzmhRlTNf6wgsvaP369erdu7diYmLuOg4AAAAAyA+WLl2qUaNG6eWXX9a+ffvUqlUrdenSxTYby61cvHhRjz/+uB544AGTIkWOfPWVdPiwVKKE9Nhjjo4GAAAA+MtMKfxl3ufvww8/tN3Lr3Xr1vr222/t7u8nSWPGjNGmTZs0bdo0HTlyRIsXL9bbb79tdz+/7MybN09LlizR999/ryNHjuiTTz5R2bJl5evrK0kKCQnRpk2b9Msvv+j8+fPZ7qNfv34qW7asevbsqe3bt+vEiRNasWKFdu7cKUkaO3asFi1apH/+8586evSo5s6dq5UrV94xtpzw8PDQoEGDtH//fluRLyIiwnZPvipVquiDDz5QQkKCvvnmGw0YMMBulF94eLhat26thx9+WBs2bLCNfly3bp3t/C9duqRNmzbp3LlzunLlSo7iGjBggMqUKaMePXpo69atOnnypLZs2aKRI0fqxx9/1MmTJzVhwgTt3LlTP/zwg9avX68jR46oZs2a+uOPPzR8+HDFxsbqhx9+0Pbt27Vnz55cKZQCAAAAgCPMnTtXTzzxhJ588knVrFlT0dHRCgoK0juZo8Zu4ZlnnlH//v0VFhZmUqTIkcx+GzhQuukWFwAAAEBBZNq8KW3btlV6erqtyFeyZEnVqlVLfn5+doWghg0batmyZVqyZIlCQ0M1adIkTZ06VZGRkbfdf4kSJTRr1iw1btxYTZo00alTp7RmzRrb1DBz5szRhg0bFBQUpAYNGmS7j6JFi2r9+vXy9/dX165dVadOHb322mu2kYY9e/bUG2+8odmzZ6t27dpasGCBYmJi7AqX96pKlSrq3bu3unbtqo4dOyo0NFTz58+3rf+///s/nT9/Xg0aNNDAgQM1YsQI+fv72+1jxYoVatKkifr166datWrppZdeso3yu//++/Xss8+qb9++8vPz09///vccxVW8eHF9/fXXqlChgnr37q2aNWtqyJAh+uOPP+Tt7a3ixYvr+++/18MPP6xq1arp6aef1vDhw/XMM8/I1dVVv/32mx5//HFVq1ZNERER6tKli6ZMmfKX8wUAAAAAZrt27Zri4uKy3E++Y8eO2rFjxy23i4mJ0fHjxzV58uS8DhF34+efpU8/zXj+3HOOjQUAAADIJRYjuxvowVRRUVFatWoV93i4SXJysnx8fDR5y3F5eHk7OpzCwTBU4o/zulSsZJ7c22J8gzK5vs+Czmq1KikpSf7+/vn2HjbOhHybj5ybi3ybj5ybi3ybz9E5z7wmuHjxory9HX9N8PPPP+u+++7T9u3bdf/999uWz5gxQ4sXL9bhw4ezbHP06FG1bNlSW7duVbVq1XJ0/ZeamqrU1FTb6+TkZAUFBen8+fN3zIPVatXZs2fl5+fH++ROpk2TS1SUjJYtZWzZ4uhocow+dn70ceFAPzs/+tj50cfOLz/1cXJyskqWLJmjayM3k2ICAAAAAMApWG76kpxhGFmWSVJ6err69++vKVOm2O7fnhMzZ87MdqaUs2fP6urVq7fd1mq16uLFizIMw+F/nMjX0tLkt2CBJOliv366mpTk4IByjj52fvRx4UA/Oz/62PnRx84vP/VxSkpKjttS+AMAAAAAIAfKlCkjV1dX/fLLL3bLk5KSFBAQkKV9SkqK9u7dq3379mn48OGSMv54YBiG3NzctH79erVr1y7LdhMmTNDo0aNtrzNH/Pn5+eVoxJ/FYskX30rOl9LTpa1bpc8+k8uZMzLKlJH34MHydnd3dGQ5Rh87P/q4cKCfnR997PzoY+eXn/rYw8Mjx20p/OUDUVFRioqKcnQYAAAAAIDbKFq0qBo1aqQNGzaoV69etuUbNmxQjx49srT39vbWwYMH7ZbNnz9fmzdv1vLly1WxYsVsj+Pu7i73bApRLi4uOfqDg8ViyXHbQmXlSmnkSOnHH22LLKmpsqxdK/Xu7cDA7h597Pzo48KBfnZ+9LHzo4+dX37p47s5PoU/AAAAAAByaPTo0Ro4cKAaN26ssLAwvfvuu0pMTNSzzz4rKWO03k8//aR//etfcnFxUWhoqN32/v7+8vDwyLIceWzlSqlPH8kw7JdfupSxfPnyAlf8AwAAALJD4Q8AAAAAgBzq27evfvvtN02dOlVnzpxRaGio1qxZo+DgYEnSmTNnlJiY6OAoYSc9PWOk381FPyljmcUijRol9eghubqaHh4AAACQmxh/CgAAAADAXRg6dKhOnTql1NRUxcXFqXXr1rZ1ixYtUmxs7C23jYqKUnx8fN4HiT9t3Wo3vWcWhiGdPp3RDgAAACjgKPwBAAAAAADndeZM7rYDAAAA8jEKfwAAAAAAwHkFBuZuOwAAACAfo/AHAAAAAACcV6tWUvnyGffyy47FIgUFZbQDAAAACjgKfwAAAAAAwHm5ukpvvJFxL7+bZRYDo6Mz2gEAAAAFnJujAwDuZFTdUvL19XV0GIWC1WpVUlK6/P1Ly8WF7wUAAAAAcBI9e0oBAdKvv9ovL18+o+jXu7cjogIAAAByHYU/AAAAAADg3LZuzSj6lSghLVsmXbiQcU+/Vq0Y6QcAAACnQuEPAAAAAAA4t5iYjMdHH5W6dHFsLAAAAEAeYi4/AAAAAADgvFJSpE8+yXg+eLBjYwEAAADyGIU/AAAAAADgvD75RLpyRapeXQoLc3Q0AAAAQJ6i8AcAAAAAAJxX5jSfkZGSxeLQUAAAAIC8RuEPAAAAAAA4p6NHpW3bJBcX6fHHHR0NAAAAkOco/AEAAAAAAOe0aFHGY6dOUrlyDg0FAAAAMAOFPwAAAAAA4HzS06XFizOeDx7s2FgAAAAAk7g5OgDgTqIP/C4PrzRHh1E4GIYi+RIsAAAAAGewcaP0009SqVLSQw85OhoAAADAFIz4AwAAAAAAzicmJuOxf3/J3d2xsQAAAAAmofAHAAAAAACcy/nz0qpVGc+Z5hMAAACFCIU/AAAAAADgXD7+WEpNlerWlRo0cHQ0AAAAgGko/AEAAAAAAOeSOc3n4MGSxeLYWAAAAAATUfgDAAAAAADO47//lfbuldzcpAEDHB0NAAAAYCoKfwAAAAAAwHlkjvbr3l3y83NsLAAAAIDJKPwBAAAAAADncP269OGHGc8HD3ZsLAAAAIADUPgDAAAAAADOYc0aKSlJCgiQunRxdDQAAACA6Sj8AQAAAAAA55A5zefAgRn3+AMAAAAKGQp/AAAAAACg4EtKkr74IuM503wCAACgkKLwBwAAAAAACr4PP5TS0qSmTaVatRwdDQAAAOAQFP4AAAAAAEDBZhh/TvPJaD8AAAAUYhT+8rHr1687OgSba9euZbv8XmPMT+cGAAAAACjg4uKk//5X8vCQHn3U0dEAAAAADkPhzyTr1q1Ty5Yt5evrq9KlS6tbt246fvy4bf2pU6dksVi0bNkytWnTRh4eHvrwww8lSTExMapZs6Y8PDxUo0YNzZ8/327f48aNU7Vq1VS8eHFVqlRJEydOvGNh7aefflLfvn1VsmRJlS5dWj169NCpU6ds6yMjI9WzZ0/NnDlT5cqVU7Vq1W4Zo9Vq1dSpU1W+fHm5u7urfv36WrduXY7ODQAAAACAvyxztF+vXpKvr0NDAQAAAByJwp9JLl++rNGjR2vPnj3atGmTXFxc1KtXL1mtVrt248aN04gRI5SQkKBOnTpp4cKFevnll/Xqq68qISFBM2bM0MSJE7V48WLbNl5eXlq0aJEOHTqkN954QwsXLtS8efNuGcuVK1fUtm1blShRQl9//bW2bdumEiVKqHPnznYj+zZt2qSEhARt2LBBn3/++S1jfOONNzRnzhy9/vrrOnDggDp16qSHHnpIR48eve25AQAAAADwl129Kv373xnPmeYTAAAAhZybowMoLB5++GG71++//778/f116NAhhYaG2paPGjVKvXv3tr2eNm2a5syZY1tWsWJFHTp0SAsWLNCgQYMkSa+88oqtfUhIiMaMGaOlS5fqpZdeyjaWJUuWyMXFRe+9954sFoukjFGFvr6+io2NVceOHSVJnp6eeu+991S0aFFJso0IvDnG119/XePGjdOj/5tOZdasWfrqq68UHR2tf/zjH7c8t5ulpqYqNTXV9jo5OTnjiWFk/CDvGYYMw8hSkEbesVqt5NxE5Nt85Nxc5Nt85Nxc5Nt8js45fY0cWbVKunBBCgqS2rVzdDQAAACAQ1H4M8nx48c1ceJE7dq1S+fOnbNdwCYmJtoV/ho3bmx7fvbsWZ0+fVpPPPGEnnrqKdvytLQ0+fj42F4vX75c0dHROnbsmC5duqS0tDR5e3vfMpa4uDgdO3ZMXl5edsuvXr1qN/1onTp1bEW/G90YY3Jysn7++We1aNHCrk2LFi20f//+W26XnZkzZ2rKlClZlnumXlAxt/TbbovcYujChYw/7ri4MCDYDFarVRcvXiTnJiHf5iPn5iLf5iPn5iLf5nN0zlNSUkw/JgqgzGk+IyMlV1eHhgIAAAA4GoU/k3Tv3l1BQUFauHChypUrJ6vVqtDQULupNaWMUXaZMouDCxcuVLNmzezauf7vYmbXrl169NFHNWXKFHXq1Ek+Pj5asmSJ5syZc8tYrFarGjVqpI8++ijLOj8/v2xjuVWMmTJHDmYyDCPLslvtL9OECRM0evRo2+vk5GQFBQXpsruv0ovdupCJXGQY8vVNl5+fH39MM4nVapXFYiHnJiHf5iPn5iLf5iPn5iLf5nN0zj08PEw/JgqY06elDRsynkdGOjQUAAAAID+g8GeC3377TQkJCVqwYIFatWolSdq2bdsdtwsICNB9992nEydOaMCAAdm22b59u4KDg/Xyyy/blv3www+33W/Dhg21dOlS+fv733ZkYE54e3urXLly2rZtm1q3bm1bvmPHDjVt2vSu9uXu7i53d/esKyyWjB+YwmKxyMXFhT+mmYicm4t8m4+cm4t8m4+cm4t8m8+ROaefcUf/+lfGrSHCw6VKlRwdDQAAAOBwFP5MULJkSZUuXVrvvvuuAgMDlZiYqPHjx+do26ioKI0YMULe3t7q0qWLUlNTtXfvXp0/f16jR49WlSpVlJiYqCVLlqhJkyb64osv9Omnn952nwMGDNDs2bPVo0cPTZ06VeXLl1diYqJWrlypsWPHqnz58nd1fmPHjtXkyZNVuXJl1a9fXzExMYqPj892RCEAAAAAALnCMKRFizKeDx7s0FAAAACA/IKvT5rAxcVFS5YsUVxcnEJDQ/XCCy9o9uzZOdr2ySef1HvvvadFixapTp06Cg8P16JFi1SxYkVJUo8ePfTCCy9o+PDhql+/vnbs2KGJEyfedp/FixfX119/rQoVKqh3796qWbOmhgwZoj/++OOeRgCOGDFCY8aM0ZgxY1SnTh2tW7dOq1evVtWqVe96XwAAAAAA5Mi2bdKxY1KJElKfPo6OBgAAAMgXLIZhGI4OAshOcnKyfHx8NHnLcXl4cY8/UxiGIsuly9/fn2mVTGK1WpWUlETOTUK+zUfOzUW+zUfOzUW+zefonGdeE1y8ePEv36agILubPDi6z0w1ZIgUE5Px+P77jo7GNIWqjwsp+rhwoJ+dH33s/Ohj55ef+vhurgn4bQQAAAAAAAXLpUvSsmUZz5nmEwAAALCh8AcAAAAAAAqW5culy5elqlWlFi0cHQ0AAACQb1D4AwAAAAAABUtMTMZjZKRksTg0FAAAACA/ofAHAAAAAAAKjuPHpa+/llxcpMcfd3Q0AAAAQL5C4Q8AAAAAABQcixZlPHboIJUv79BQAAAAgPyGwh8AAAAAACgY0tOlxYszng8e7NhYAAAAgHyIwh8AAAAAACgYNm+WTp+WfH2lHj0cHQ0AAACQ71D4AwAAAAAABUNMTMZj//6Sh4djYwEAAADyIQp/AAAAAAAg/7twQfr004znTPMJAAAAZIvCHwAAAAAAyP+WLJGuXpVCQ6VGjRwdDQAAAJAvuTk6AOBORtUtJV9fX0eHUShYrVYlJSU5OgwAAAAAyCpzms/BgyWLxbGxAAAAAPkUI/4AAAAAAED+duiQtHu35OYmPfaYo6MBAAAA8i0KfwAAAAAAIH/LHO334IOSv79jYwEAAADyMQp/AAAAAAAg/7p+Xfrgg4zngwc7NhYAAAAgn6PwBwAAAAAA8q9166Rff80Y6de1q6OjAQAAAPI1Cn8AAAAAACD/ypzm87HHpCJFHBsLAAAAkM9R+AMAAAAAAPnT2bPSZ59lPGeaTwAAAOCOKPwBAAAAAID86aOPpLQ0qXFjKTTU0dEAAAAA+R6FPwAAAAAAkP8Yxp/TfDLaDwAAAMgRN0cHANxJ9IHf5eGV5ugwHGp8gzKODgEAAAAAzLVvn3TggOTuLvXr5+hoAAAAgAKBEX8AAAAAACD/yRzt16uXVLKkY2MBAAAACggKfwAAAAAAIH9JTZX+/e+M50zzCQAAAOQYhT8AAAAAAJC/rF4t/f67VL689MADjo4GAAAAKDC4xx8AAAAAAMgf0tOlrVul6dMzXg8cKLm6OjYmAAAAoABhxB8AAAAAAHC8lSulkBCpbVvpwIGMZTExGcsBAAAA5AiFPwAAAAAA4FgrV0p9+kg//mi//NdfM5ZT/AMAAAByhMIfAAAAAABwnPR0aeRIyTCyrstcNmpURjsAAAAAt0XhDwAAAAAAOM7WrVlH+t3IMKTTpzPaAQAAALgtCn8AAAAAAMBxzpzJ3XYAAABAIUbhDwAAAAAAOE5gYO62AwAAAAoxCn9OzmKxaNWqVY4OAwAAAACA7LVqJZUvL1ks2a+3WKSgoIx2AAAAAG6Lwt8NCnKRLCoqSvXr13d0GAAAAAAA3B1XV+mNN7Jfl1kMjI7OaAcAAADgtgpN4e/atWuODgEAAAAAAGSnd2/p+eezLi9fXlq+PGM9AAAAgDvKF4W/zz77TL6+vrJarZKk+Ph4WSwWjR071tbmmWeeUb9+/WyvV6xYodq1a8vd3V0hISGaM2eO3T5DQkI0ffp0RUZGysfHR0899ZSuXbum4cOHKzAwUB4eHgoJCdHMmTNt7SWpV69eslgsttc3u90+pIxRgwsWLFC3bt1UvHhx1axZUzt37tSxY8fUpk0beXp6KiwsTMePH7fb7zvvvKPKlSuraNGiql69uj744AO79YmJierRo4dKlCghb29vRURE6Ndff5UkLVq0SFOmTNH+/ftlsVhksVi0aNEi27bnzp1Tr169VLx4cVWtWlWrV6+2rYuNjZXFYtGmTZvUuHFjFS9eXPfff78OHz6cpY8aNWokDw8PVapUSVOmTFFaWpptfVRUlCpUqCB3d3eVK1dOI0aMsK2bP3++qlatKg8PDwUEBKhPnz7Z5hYAAAAAUIhlXmP26SP9+9/SV19JJ09S9AMAAADuQr4o/LVu3VopKSnat2+fJGnLli0qU6aMtmzZYmsTGxur8PBwSVJcXJwiIiL06KOP6uDBg4qKitLEiRPtil2SNHv2bIWGhiouLk4TJ07Um2++qdWrV2vZsmU6fPiwPvzwQ1uBb8+ePZKkmJgYnTlzxvb6ZrfbR6Zp06bp8ccfV3x8vGrUqKH+/fvrmWee0YQJE7R3715J0vDhw23tP/30U40cOVJjxozRf//7Xz3zzDMaPHiwvvrqK0mSYRjq2bOnfv/9d23ZskUbNmzQ8ePH1bdvX0lS3759NWbMGNWuXVtnzpzRmTNnbOskacqUKYqIiNCBAwfUtWtXDRgwQL///rtdzC+//LLmzJmjvXv3ys3NTUOGDLGt+/LLL/XYY49pxIgROnTokBYsWKBFixbp1VdflSQtX75c8+bN04IFC3T06FGtWrVKderUkSTt3btXI0aM0NSpU3X48GGtW7dOrVu3zja3AAAAAIBCbNeujMeICKlfP6lNG6b3BAAAAO6Sm6MDkCQfHx/Vr19fsbGxatSokWJjY/XCCy9oypQpSklJ0eXLl3XkyBG1adNGkjR37lw98MADmjhxoiSpWrVqOnTokGbPnq3IyEjbftu1a6cXX3zR9joxMVFVq1ZVy5YtZbFYFBwcbFvn5+cnSfL19VXZsmVvGevt9pFp8ODBioiIkCSNGzdOYWFhmjhxojp16iRJGjlypAYPHmxr//rrrysyMlJDhw6VJI0ePVq7du3S66+/rrZt22rjxo06cOCATp48qaCgIEnSBx98oNq1a2vPnj1q0qSJSpQoITc3t2xjj4yMtI2WnDFjht566y3t3r1bnTt3trV59dVXbYXV8ePH68EHH9TVq1fl4eGhV199VePHj9egQYMkSZUqVdK0adP00ksvafLkyUpMTFTZsmXVvn17FSlSRBUqVFDTpk1t+fL09FS3bt3k5eWl4OBgNWjQINvcpqamKjU11fY6OTk544lhZPwUYpmjYc04jmEYph0P5Nxs5Nt85Nxc5Nt85Nxc5Nt8js45fV2IXL4s7d+f8TwszLGxAAAAAAVYvij8SVKbNm0UGxur0aNHa+vWrZo+fbpWrFihbdu26cKFCwoICFCNGjUkSQkJCerRo4fd9i1atFB0dLTS09Pl+r9vBDZu3NiuTWRkpDp06KDq1aurc+fO6tatmzp27HhXceZkH3Xr1rU9DwgIkCTbCLjMZVevXlVycrK8vb2VkJCgp59+Osv5vPG/m5snJCQoKCjIVvSTpFq1asnX11cJCQlq0qTJbWO+MR5PT095eXkpKSnplm0CAwMlSUlJSapQoYLi4uK0Z88e2wg/SUpPT9fVq1d15coVPfLII4qOjlalSpXUuXNnde3aVd27d5ebm5s6dOig4OBg27rOnTvbph292cyZMzVlypQsyz1TL6iYW/ptz9HZJSWZc/5Wq1UXL16UYRhycckXA4KdHjk3F/k2Hzk3F/k2Hzk3F/k2n6NznpKSYvox4SB790rp6dJ992Xc1w8AAADAPclXhb/3339f+/fvl4uLi2rVqqXw8HBt2bJF58+ft41GkzKmvrRYLHbbG9mMCPP09LR73bBhQ508eVJr167Vxo0bFRERofbt22v58uU5jjMn+yhSpIjteWac2S278dur2Z1P5rLszvd2y29247Ezj3XzN2dvF5/VatWUKVPUO5v7Knh4eCgoKEiHDx/Whg0btHHjRg0dOlSzZ8/Wli1b5OXlpW+//VaxsbFav369Jk2apKioKO3Zs0e+vr52+5owYYJGjx5te52cnKygoCBddvdVejHvO56nM/P3L23KcaxWqywWi/z8/PhjmknIubnIt/nIubnIt/nIubnIt/kcnXMPDw/TjwkHyZzmk9F+AAAAwF+Sbwp/mff5i46OVnh4uCwWi8LDwzVz5kydP39eI0eOtLWtVauWtm3bZrf9jh07VK1aNdtov1vx9vZW37591bdvX/Xp00edO3fW77//rlKlSqlIkSJKT7/zyKrb7eNe1KxZU9u2bdPjjz9udz41a9aUlHG+iYmJOn36tG3U36FDh3Tx4kVbm6JFi+Yo9nvRsGFDHT58WFWqVLllm2LFiumhhx7SQw89pGHDhqlGjRo6ePCgGjZsKDc3N7Vv317t27fX5MmT5evrq82bN2cpJLq7u8vd3T3rzi2WjJ9CzMw/slgsFrm4uPDHNBORc3ORb/ORc3ORb/ORc3ORb/M5Muf0cyGyc2fGI4U/AAAA4C/JN4W/zPv8ffjhh7YpLlu3bq1HHnlE169ft93fT5LGjBmjJk2aaNq0aerbt6927typt99+W/Pnz7/tMebNm6fAwEDVr19fLi4u+uSTT1S2bFnbyLOQkBBt2rRJLVq0kLu7u0qWLHnX+7gXY8eOVUREhBo2bKgHHnhAn332mVauXKmNGzdKktq3b6+6detqwIABio6OVlpamoYOHarw8HDbdKYhISE6efKk4uPjVb58eXl5eWVfRLsHkyZNUrdu3RQUFKRHHnlELi4uOnDggA4ePKjp06dr0aJFSk9PV7NmzVS8eHF98MEHKlasmIKDg/X555/rxIkTat26tUqWLKk1a9bIarWqevXquRIbAAAAAKCAM4w/C3/Nmzs2FgAAAKCAy1dfn2zbtq3S09NtRb6SJUuqVq1a8vPzs41skzJGoC1btkxLlixRaGioJk2apKlTpyoyMvK2+y9RooRmzZqlxo0bq0mTJjp16pTWrFlj+xbpnDlztGHDBgUFBalBgwb3tI970bNnT73xxhuaPXu2ateurQULFigmJsaWB4vFolWrVqlkyZJq3bq12rdvr0qVKmnp0qW2fTz88MPq3Lmz2rZtKz8/P3388cf3HM/NOnXqpM8//1wbNmxQkyZN1Lx5c82dO1fBwcGSJF9fXy1cuFAtWrRQ3bp1tWnTJn322WcqXbq0fH19tXLlSrVr1041a9bUP//5T3388ceqXbt2rsUHAAAAACjATp2SkpKkIkWkhg0dHQ0AAABQoFmM7G6OB+QDycnJ8vHx0eQtx+XhVbjv8Te+QRlTjmO1WpWUlCR/f3+mVTIJOTcX+TYfOTcX+TYfOTcX+Tafo3OeeU1w8eJFeXsX3muCu8mDo/vsnvz739KAAVKzZn/e6w+3VCD7GHeFPi4c6GfnRx87P/rY+eWnPr6bawJ+GwEAAAAAgOMwzScAAACQayj8AQAAAAAAx8kc5RcW5tg4AAAAACdA4Q8AAAAAADjGH39I8fEZzyn8AQAAAH8ZhT8AAAAAAOAYcXFSWpoUGCgFBTk6GgAAAKDAo/AHAAAAAAAcI/P+fmFhksXi2FgAAAAAJ0DhDwAAAAAAOEZm4a95c8fGAQAAADgJCn8AAAAAAMB8hmE/4g8AAADAX0bhDwAAAAAAmC8xUfrlF8nNTWrUyNHRAAAAAE6Bwh8AAAAAADBf5mi/+vWlYsUcGgoAAADgLCj8AQAAAAAA8+3alfHINJ8AAABArqHwBwAAAAAAzMf9/QAAAIBc5+boAIA7GVW3lHx9fR0dBgAAAAAgt1y9Ku3bl/G8eXPHxgIAAAA4EUb8AQAAAAAAc337rXT9uhQQIIWEODoaAAAAwGlQ+AMAAAAAAOa6cZpPi8WxsQAAAABOhMIfAAAAAAAwV2bhj2k+AQAAgFxF4Q8AAAAAAJhr166Mx7Awx8YBAAAAOBkKfwAAAAAAwDynT0s//SS5ukqNGzs6GgAAAMCpUPgDAAAAAADmyZzms149qXhxx8YCAAAAOBkKfwAAAAAAwDxM8wkAAADkGTdHBwDcSfSB3+XhleboMEw3vkEZR4cAAAAAALkvc8QfhT8AAAAg1zHiDwAAAAAAmCM1Vfr224znzZs7NhYAAADACVH4AwAAAAAA5ti3T7p2TfLzkypVcnQ0AAAAgNOh8AcAAAAAAMxx4zSfFotjYwEAAACcEIU/AAAAAABgjszCH9N8AgAAAHmCwh8AAAAAADDHrl0Zj2Fhjo0DAAAAcFIU/gAAAAAAQN776Sfp9GnJxUVq0sTR0QAAAABOicIfAAAAAADIe5mj/erWlTw9HRsLAAAA4KQo/AEAAAAAgLyXeX8/pvkEAAAA8gyFPwAAAAAAkPcyC3/Nmzs2DgAAAMCJUfgDAAAAAAB569o1KS4u4zkj/gAAAIA8Q+EPAAAAAADkrfh4KTVVKl1aqlLF0dEAAAAATovCHwAAAAAAyFs3TvNpsTg2FgAAAMCJUfjLZywWi1atWnXbNqdOnZLFYlF8fHyuHjuv9gsAAAAAKOR27cp4ZJpPAAAAIE9R+DPRtWvXHB2CaQrTuQIAAAAA7iBzxB+FPwAAACBPUfj7n88++0y+vr6yWq2SpPj4eFksFo0dO9bW5plnnlG/fv1sr1esWKHatWvL3d1dISEhmjNnjt0+Q0JCNH36dEVGRsrHx0dPPfWUrl27puHDhyswMFAeHh4KCQnRzJkzbe0lqVevXrJYLLbXN6tYsaIkqUGDBrJYLGrTpo1tXUxMjGrWrCkPDw/VqFFD8+fPt60bMmSI6tatq9TUVEnS9evX1ahRIw0YMOC2+23Tpo1GjRplF0PPnj0VGRl523OVpB07dqh169YqVqyYgoKCNGLECF2+fDnb8wIAAAAAOKEzZ6QffpBcXKQmTRwdDQAAAODU3BwdQH7RunVrpaSkaN++fWrUqJG2bNmiMmXKaMuWLbY2sbGxeuGFFyRJcXFxioiIUFRUlPr27asdO3Zo6NChKl26tF1BbPbs2Zo4caJeeeUVSdKbb76p1atXa9myZapQoYJOnz6t06dPS5L27Nkjf39/xcTEqHPnznJ1dc021t27d6tp06bauHGjateuraJFi0qSFi5cqMmTJ+vtt99WgwYNtG/fPj311FPy9PTUoEGD9Oabb6pevXoaP3685s2bp4kTJ+rcuXO24uCt9ptTN5/rwYMH1alTJ02bNk3vv/++zp49q+HDh2v48OGKiYnJsn1qaqqtKClJycnJGU8MI+OnkMksQpt9TMMwHHLswoqcm4t8m4+cm4t8m4+cm4t8m8/ROaevnUTmNJ+hoZKXl2NjAQAAAJwchb//8fHxUf369RUbG6tGjRrZinxTpkxRSkqKLl++rCNHjthGwc2dO1cPPPCAJk6cKEmqVq2aDh06pNmzZ9sV/tq1a6cXX3zR9joxMVFVq1ZVy5YtZbFYFBwcbFvn5+cnSfL19VXZsmVvGWtmu9KlS9u1mzZtmubMmaPevXtLyhjBd+jQIS1YsECDBg1SiRIl9OGHHyo8PFxeXl6aM2eONm3aJB8fn9vuN6duPtfHH39c/fv3t40WrFq1qt58802Fh4frnXfekYeHh932M2fO1JQpU7Ls1zP1goq5pd91PAVdUpL552y1WnXx4kUZhiEXFwYEm4Gcm4t8m4+cm4t8m4+cm4t8m8/ROU9JSTH9mMgDTPMJAAAAmIbC3w3atGmj2NhYjR49Wlu3btX06dO1YsUKbdu2TRcuXFBAQIBq1KghSUpISFCPHj3stm/RooWio6OVnp5uG63XuHFjuzaRkZHq0KGDqlevrs6dO6tbt27q2LHjX4797NmzOn36tJ544gnbNJuSlJaWZivsSVJYWJhefPFFTZs2TePGjVPr1q3/8rEz3XyucXFxOnbsmD766CPbssxvC588eVI1a9a0az9hwgSNHj3a9jo5OVlBQUG67O6r9GLeuRZnQeHvX9r0Y1qtVlksFvn5+fHHNJOQc3ORb/ORc3ORb/ORc3ORb/M5Ouc3f1kQBVRm4a95c8fGAQAAABQCFP5u0KZNG73//vvav3+/XFxcVKtWLYWHh2vLli06f/68wsPDbW0Nw5DFYrHb3shmOkpPT0+71w0bNtTJkye1du1abdy4UREREWrfvr2WL1/+l2LPnAJn4cKFatasmd26G6cMtVqt2r59u1xdXXX06NEc7dvFxSXLuV2/fj1Lu5vP1Wq16plnntGIESOytK1QoUKWZe7u7nJ3d88agMWS8VPIOOqPWRaLRS4uLvwxzUTk3Fzk23zk3Fzk23zk3Fzk23yOzDn97ASuX5f27s14zog/AAAAIM9R+LtB5n3+oqOjFR4eLovFovDwcM2cOVPnz5/XyJEjbW1r1aqlbdu22W2/Y8cOVatW7Zb35svk7e2tvn37qm/fvurTp486d+6s33//XaVKlVKRIkWUnn77KR4z7713Y7uAgADdd999OnHihAYMGHDLbWfPnq2EhARt2bJFnTp1UkxMjAYPHnzL/UoZU4CeOXPG9jo9PV3//e9/1bZt29vG2bBhQ3333XeqUqXKbdsBAAAAAJzU/v3S1atSyZJStWqOjgYAAABwenx98gaZ9/n78MMPbffya926tb799lu7+/tJ0pgxY7Rp0yZNmzZNR44c0eLFi/X222/b3eMuO/PmzdOSJUv0/fff68iRI/rkk09UtmxZ+fr6SpJCQkK0adMm/fLLLzp//ny2+/D391exYsW0bt06/frrr7p48aIkKSoqSjNnztQbb7yhI0eO6ODBg4qJidHcuXMlSfHx8Zo0aZLef/99tWjRQm+88YZGjhypEydO3Ha/7dq10xdffKEvvvhC33//vYYOHaoLFy7cMZ/jxo3Tzp07NWzYMMXHx+vo0aNavXq1nn/++TtuCwAAAABwAjdO81kIZ3IBAAAAzEbh7yZt27ZVenq6rchXsmRJ1apVS35+fnb3pGvYsKGWLVumJUuWKDQ0VJMmTdLUqVMVGRl52/2XKFFCs2bNUuPGjdWkSROdOnVKa9assU1hM2fOHG3YsEFBQUFq0KBBtvtwc3PTm2++qQULFqhcuXK2ew0++eSTeu+997Ro0SLVqVNH4eHhWrRokSpWrKirV69qwIABioyMVPfu3SVJTzzxhNq3b6+BAwcqPT39lvsdMmSIBg0apMcff1zh4eGqWLHiHUf7SVLdunW1ZcsWHT16VK1atVKDBg00ceJEBQYG3nFbAAAAAIAT2LUr45FpPgEAAABTWIzsbkwH5APJycny8fHR5C3H5eHl7ehwTDe+QRnTj2m1WpWUlCR/f3/up2IScm4u8m0+cm4u8m0+cm4u8m0+R+c885rg4sWL8vYufNcEme4mD47usywqVZJOnpQ2bJDat3d0NE4h3/Uxch19XDjQz86PPnZ+9LHzy099fDfXBPw2AgAAAACA3PfrrxlFP4tFatrU0dEAAAAAhQKFPwAAAAAAkPsyp/msXVsqxCM2AQAAADNR+AMAAAAAALlv586MR+7vBwAAAJiGwh8AAAAAAMh9mYW/5s0dGwcAAABQiFD4AwAAAAAAuSstTdqzJ+M5I/4AAAAA01D4AwAAAAAAuevAAemPPyRfX6l6dUdHAwAAABQaFP4AAAAAAEDu2rUr47FZM8mFPz0AAAAAZuH/vgEAAAAAQO7KvL8f03wCAAAApqLwBwAAAAAAcldm4a95c8fGAQAAABQyFP4AAAAAAEDuOXtWOn4843mzZo6NBQAAAChk3BwdAHAno+qWkq+vr6PDAAAAAADkROb9/WrVkriWAwAAAEzFiD8AAAAAAJB7mOYTAAAAcBgKfwAAAAAAIPdkjvgLC3NsHAAAAEAhROEPAAAAAADkjrQ0affujOcU/gAAAADTUfgDAAAAAAC547//lS5flry9pZo1HR0NAAAAUOhQ+AMAAAAAALkjc5rPZs0kF/7kAAAAAJiN/wsHAAAAAAC5Y+fOjEem+QQAAAAcgsIfAAAAAAB3Yf78+apYsaI8PDzUqFEjbd269ZZtV65cqQ4dOsjPz0/e3t4KCwvTl19+aWK0Jsss/DVv7tg4AAAAgEKKwh8AAAAAADm0dOlSjRo1Si+//LL27dunVq1aqUuXLkpMTMy2/ddff60OHTpozZo1iouLU9u2bdW9e3ft27fP5MhN8Ntv0tGjGc8p/AEAAAAO4eboAIA7iT7wuzy80hwdhmnGNyjj6BAAAAAA3MLcuXP1xBNP6Mknn5QkRUdH68svv9Q777yjmTNnZmkfHR1t93rGjBn6z3/+o88++0wNGjQwI2TzZN7fr0YNqWRJx8YCAAAAFFKM+AMAAAAAIAeuXbumuLg4dezY0W55x44dtWPHjhztw2q1KiUlRaVKlcqLEB2LaT4BAAAAh2PEHwAAAAAAOXDu3Dmlp6crICDAbnlAQIB++eWXHO1jzpw5unz5siIiIm7ZJjU1VampqbbXycnJkjKKhlar9bb7t1qtMgzjju3ygmXXLlkkWZs1kxxw/MLCkX0Mc9DHhQP97PzoY+dHHzu//NTHdxMDhT8AAAAAAO6CxWKxe20YRpZl2fn4448VFRWl//znP/L3979lu5kzZ2rKlClZlp89e1ZXr1697TGsVqsuXrwowzDk4mLiJD/p6fL/X+Hv9+rVlZaUZN6xCxmH9TFMQx8XDvSz86OPnR997PzyUx+npKTkuC2FPwAAAAAAcqBMmTJydXXNMrovKSkpyyjAmy1dulRPPPGEPvnkE7Vv3/62bSdMmKDRo0fbXicnJysoKEh+fn7y9va+7bZWq1UWi0V+fn7m/nHiwAG5XL4sw8tLpVq2lFxdzTt2IeOwPoZp6OPCgX52fvSx86OPnV9+6mMPD48ct6XwBwAAAABADhQtWlSNGjXShg0b1KtXL9vyDRs2qEePHrfc7uOPP9aQIUP08ccf68EHH7zjcdzd3eXu7p5luYuLS47+4GCxWHLcNtfs3p1x7KZNZSlSxLzjFlIO6WOYij4uHOhn50cfOz/62Pnllz6+m+NT+AMAAAAAIIdGjx6tgQMHqnHjxgoLC9O7776rxMREPfvss5IyRuv99NNP+te//iUpo+j3+OOP64033lDz5s1towWLFSsmHx8fh51Hrtu5M+MxLMyxcQAAAACFHIU/AAAAAAByqG/fvvrtt980depUnTlzRqGhoVqzZo2Cg4MlSWfOnFFiYqKt/YIFC5SWlqZhw4Zp2LBhtuWDBg3SokWLzA4/7+zalfHYvLlj4wAAAAAKOQp/AAAAAADchaFDh2ro0KHZrru5mBcbG5v3ATna779L33+f8ZzCHwAAAOBQTDwLAAAAAADu3TffZDxWrSqVLu3YWAAAAIBCjsIfAAAAAAC4d5nTfHJ/PwAAAMDhKPwBAAAAAIB7t3NnxiOFPwAAAMDhKPwVErGxsbJYLLpw4YKjQwEAAAAAOAur9c+pPrm/HwAAAOBwFP4AAAAAAMC9SUiQkpMlT08pNNTR0QAAAACFXp4X/tq0aaPnn39eo0aNUsmSJRUQEKB3331Xly9f1uDBg+Xl5aXKlStr7dq1dtsdOnRIXbt2VYkSJRQQEKCBAwfq3LlztvXr1q1Ty5Yt5evrq9KlS6tbt246fvy4bf2pU6dksVi0cuVKtW3bVsWLF1e9evW0M3MKklu4cOGCnn76aQUEBMjDw0OhoaH6/PPPbetXrFih2rVry93dXSEhIZozZ47d9iEhIZo+fboef/xxlShRQsHBwfrPf/6js2fPqkePHipRooTq1KmjvXv32rZZtGiRfH19tWrVKlWrVk0eHh7q0KGDTp8+bWtz/Phx9ejRQwEBASpRooSaNGmijRs32h07NTVVL730koKCguTu7q6qVavq/fff16lTp9S2bVtJUsmSJWWxWBQZGWnrnxEjRuill15SqVKlVLZsWUVFRdnt9+LFi3r66afl7+8vb29vtWvXTvv377et379/v9q2bSsvLy95e3urUaNGtvP74Ycf1L17d5UsWVKenp6qXbu21qxZc9s+AAAAAAAUEJnX2E2bSm5ujo0FAAAAgEz5v/LFixfrpZde0u7du7V06VI999xzWrVqlXr16qW//e1vmjdvngYOHKjExEQVL15cZ86cUXh4uJ566inNnTtXf/zxh8aNG6eIiAht3rxZknT58mWNHj1aderU0eXLlzVp0iT16tVL8fHxcnH5s5758ssv6/XXX1fVqlX18ssvq1+/fjp27JjcsrkgsVqt6tKli1JSUvThhx+qcuXKOnTokFxdXSVJcXFxioiIUFRUlPr27asdO3Zo6NChKl26tK2QJknz5s3TjBkzNHHiRNu5tWjRQkOGDNHs2bM1btw4Pf744/ruu+9ksVgkSVeuXNGrr76qxYsXq2jRoho6dKgeffRRbd++XZJ06dIlde3aVdOnT5eHh4cWL16s7t276/Dhw6pQoYIk6fHHH9fOnTv15ptvql69ejp58qTOnTunoKAgrVixQg8//LAOHz4sb29vFStWzK5/Ro8erW+++UY7d+5UZGSkWrRooQ4dOsgwDD344IMqVaqU1qxZIx8fHy1YsEAPPPCAjhw5olKlSmnAgAFq0KCB3nnnHbm6uio+Pl5FihSRJA0bNkzXrl3T119/LU9PTx06dEglSpTI9vckNTVVqampttfJyckZTwwj46eQsFqtDj22YRgOjaGwIefmIt/mI+fmIt/mI+fmIt/mc3TO6esCILPwxzSfAAAAQL5gMYy8rai0adNG6enp2rp1qyQpPT1dPj4+6t27t/71r39Jkn755RcFBgZq586dat68uSZNmqRvvvlGX375pW0/P/74o4KCgnT48GFVq1Yty3HOnj0rf39/HTx4UKGhoTp16pQqVqyo9957T0888YSkjFGEtWvXVkJCgmrUqJFlH+vXr1eXLl2UkJCQ7TEGDBigs2fPav369bZlL730kr744gt99913kjJG/LVq1UoffPCB3blNnDhRU6dOlSTt2rVLYWFhOnPmjMqWLatFixZp8ODB2rVrl5o1ayZJ+v7771WzZk198803atq0aba5rV27tp577jkNHz5cR44cUfXq1bVhwwa1b98+S9vY2Fi1bdtW58+fl6+v7y37R5KaNm2qdu3a6bXXXtPmzZvVq1cvJSUlyd3d3damSpUqeumll/T000/L29tbb731lgYNGpTluHXr1tXDDz+syZMnZ3sON4qKitKUKVOyLP/7l3tVzNPrjts7iz6VvR12bKvVqosXL8rHx8eugI68Q87NRb7NR87NRb7NR87NRb7N5+icp6SkqFq1arp48aK8vR33/8mOlpycLB8fnxzlwWq1KikpSf7+/ub0We3a0qFD0urVUvfueX88mN/HMB19XDjQz86PPnZ+9LHzy099fDfXBKaM+Ktbt67tuaurq0qXLq06derYlgUEBEiSkpKSJGWMrPvqq6+yHRl2/PhxVatWTcePH9fEiRO1a9cunTt3zvZN0MTERIXecF+BG48dGBhoO052hb/4+HiVL18+26KfJCUkJKhHjx52y1q0aKHo6Gilp6fbRgbeeMzMc7vV+ZYtW1aS5ObmpsaNG9va1KhRQ76+vkpISFDTpk11+fJlTZkyRZ9//rl+/vlnpaWl6Y8//lBiYqItdldXV4WHh2cb++3cGK+Ukacb++LSpUsqXbq0XZs//vjDNrXq6NGj9eSTT+qDDz5Q+/bt9cgjj6hy5cqSpBEjRui5557T+vXr1b59ez388MNZjpdpwoQJGj16tO11cnKygoKCdNndV+nFCs9Fvr9/6Ts3yiNWq1UWi0V+fn4O/4essCDn5iLf5iPn5iLf5iPn5iLf5nN0zj08PEw/Ju7ChQsZRT+JEX8AAABAPmFK4S9z2sdMFovFblnmdJeZxTur1aru3btr1qxZWfaVWbzr3r27goKCtHDhQpUrV05Wq1WhoaG6du3aLY9983FuduP0l9kxDMO2jxuX3Sy7Y+Ykjpv3feOysWPH6ssvv9Trr7+uKlWqqFixYurTp4/tfO8U++1k1z839kVgYKBiY2OzbJc5cjAqKkr9+/fXF198obVr12ry5MlasmSJevXqpSeffFKdOnXSF198ofXr12vmzJmaM2eOnn/++Sz7c3d3txtVeENAGT+FhKP/iGWxWOTi4uLwOAoTcm4u8m0+cm4u8m0+cm4u8m0+R+acfs7nvvkm47FyZcnPz7GxAAAAAJAk5curqIYNG+q7775TSEiIqlSpYvfj6emp3377TQkJCXrllVf0wAMPqGbNmjp//vxfPm7dunX1448/6siRI9mur1WrlrZt22a3bMeOHapWrZpttN+9SktL0969e22vDx8+rAsXLthGJm7dulWRkZHq1auX6tSpo7Jly+r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",
      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature Importance Analysis:\n",
      "Total features: 30\n",
      "Features for 80% importance: 10\n",
      "Features for 90% importance: 15\n",
      "\n",
      "Top 5 most important features:\n",
      "  worst concave points: 0.1590\n",
      "  worst area: 0.1470\n",
      "  worst perimeter: 0.0858\n",
      "  worst radius: 0.0790\n",
      "  mean radius: 0.0777\n"
     ]
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 8))\n",
    "\n",
    "# Plot 1: Top 15 most important features\n",
    "top_features = feature_importance_df.head(15)\n",
    "ax1.barh(range(len(top_features)), top_features['importance'], color='skyblue')\n",
    "ax1.set_yticks(range(len(top_features)))\n",
    "ax1.set_yticklabels(top_features['feature'])\n",
    "ax1.set_xlabel('Feature Importance')\n",
    "ax1.set_title('Top 15 Most Important Features\\n(Random Forest)')\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Invert y-axis so most important is at top\n",
    "ax1.invert_yaxis()\n",
    "\n",
    "# Plot 2: Cumulative importance\n",
    "cumulative_importance = np.cumsum(feature_importance_df['importance'])\n",
    "ax2.plot(range(1, len(cumulative_importance) + 1), cumulative_importance, 'o-', color='red')\n",
    "ax2.axhline(y=0.8, color='green', linestyle='--', label='80% of importance')\n",
    "ax2.axhline(y=0.9, color='orange', linestyle='--', label='90% of importance')\n",
    "ax2.set_xlabel('Number of Features')\n",
    "ax2.set_ylabel('Cumulative Importance')\n",
    "ax2.set_title('Cumulative Feature Importance')\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Find how many features capture 80% and 90% of importance\n",
    "n_features_80 = np.argmax(cumulative_importance >= 0.8) + 1\n",
    "n_features_90 = np.argmax(cumulative_importance >= 0.9) + 1\n",
    "\n",
    "print(f\"\\nFeature Importance Analysis:\")\n",
    "print(f\"Total features: {len(feature_names)}\")\n",
    "print(f\"Features for 80% importance: {n_features_80}\")\n",
    "print(f\"Features for 90% importance: {n_features_90}\")\n",
    "print(f\"\\nTop 5 most important features:\")\n",
    "for i, row in feature_importance_df.head(5).iterrows():\n",
    "    print(f\"  {row['feature']}: {row['importance']:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33953ba",
   "metadata": {},
   "source": [
    "## 6. Hyperparameter Tuning for Random Forest\n",
    "\n",
    "Random Forest has several important hyperparameters that can significantly affect performance. Let's explore the most important ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6c2aa078",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hyperparameter validation completed!\n"
     ]
    }
   ],
   "source": [
    "# Let's examine how different hyperparameters affect performance\n",
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "# 1. Number of trees (n_estimators)\n",
    "n_estimators_range = [10, 25, 50, 75, 100, 150, 200]\n",
    "\n",
    "train_scores_trees, val_scores_trees = validation_curve(\n",
    "    RandomForestClassifier(random_state=42, n_jobs=-1),\n",
    "    X_train_cancer, y_train_cancer,\n",
    "    param_name='n_estimators',\n",
    "    param_range=n_estimators_range,\n",
    "    cv=5, scoring='accuracy'\n",
    ")\n",
    "\n",
    "# 2. Maximum depth\n",
    "max_depth_range = [3, 5, 7, 10, 15, 20, None]\n",
    "\n",
    "train_scores_depth, val_scores_depth = validation_curve(\n",
    "    RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1),\n",
    "    X_train_cancer, y_train_cancer,\n",
    "    param_name='max_depth',\n",
    "    param_range=max_depth_range,\n",
    "    cv=5, scoring='accuracy'\n",
    ")\n",
    "\n",
    "# 3. Maximum features\n",
    "max_features_range = ['sqrt', 'log2', 0.3, 0.5, 0.7, None]\n",
    "\n",
    "train_scores_features, val_scores_features = validation_curve(\n",
    "    RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1),\n",
    "    X_train_cancer, y_train_cancer,\n",
    "    param_name='max_features',\n",
    "    param_range=max_features_range,\n",
    "    cv=5, scoring='accuracy'\n",
    ")\n",
    "\n",
    "print(\"Hyperparameter validation completed!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "91f7075a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DnYiNlbtvX/M1NlYnTh5Wjx5kKjpHHsvSkZoaWZLstGWbou0Dw2NZ5v21vFz2s00+aIGKioomX9vswN+ePXt0++2366OPPpLNZqtLqWk7+Q/ITYeh4+GDUcf0z39KCxdKX3xhXt91lzR7tlTvQwgQcBdeKH3ta+aQpOpq6ZNPvFcFfvmlCQ5+9NGp+3r3PrUi8OqrpYsvbt7vJn63BZcONFhFXwoBsXmz9OyzspeUqEftueho6ZFH2NsEaG/4/9pytF3LNNRuffpIzz8v3XFHAAvWMH/1pUpLS+V2u+VyubzOu1wuFRcXN3jPuHHjtGrVKk2aNEmDBw9WXl6esrKyVFNTo9LSUsXExHhdn5ubqx07dmj16tWNliU1NVUpKSl1r8vLyxUbG6uoqChFREQ0qT7N4QkJkU1SVEiI7KSa96/zzzdbXUhmcmsTAn+eH/yg4T0I0XK7d8v+4otnvczmdsu5d6+ce/f6/MyKiDAT7OPiZNXu4Vi7pyNjb03isSzzuyYsjOBTG6PtA6Ou3SMiZI+O9vvz608+akyzA39z5sxRv3799Oc//1n9+/dXbm6uysrK9PDDD+vZs+VORvvDB6OOp7JSeuklacMG8/qii6Qf/1gaMiSw5QKaKiTk1Ko+yaSW2b//VCBw+3bp00/Nuf37T+1b2bWrmVFZGwwcONB8qGoIv9uCSwcbrKIvhTa3ebP06KO+50tKzPklS/jdCLQX/H9tOdquZc7Ubvv3S3feKb3+ervrT/m7L2WrNyBqWZbPuVrz589XcXGxhg8fLsuy5HK5NHXqVC1ZskSOBiadrV69WgMHDtR1Z0klGBoaqtAGtj6w2+2y2+3NqE0T2Wyy2WyynzzQSgYNMp9DG1v14XLJfu+97XbSYoc1bJi0ceNZ214ZGWZ7ksJCs6fjZ5+Z74uLZSsvl3bskHbskM//kl69TgUC4+JOfd+rF3+X9fC7JnBo+8CwyayybI337+Y8s9mBv7///e/avHmzoqKi6jog119/vRYtWqQf/vCHys/Pb+4jESh8MOp4tm+XnnrKdEIk8wFszhwTEAE6KpvNBGn69JG+9S1z7uhR08GuDQbu2GFS2/7jH+aove+SS7xXBfbuLf3v//K7LZh0wMEq+lJoU263dLZB0KVLpZtuYpACCLSm/H995hnp2mv5/1qf223apjG0na/G2s2yTH977lzpttvaVbv5qy8VGRkph8Phs7qvpKTEZxVgrfDwcGVlZWnFihU6cOCAYmJilJmZqW7duimy3lYFx48f14YNG7Rw4cKWVRQdn8NhJp829Hml1sMPt6v/X51GU9u+diXfyJHeP6usNHs3nh4MrP2+okIqLjZH/RWdISFmbOP0YGDfvlJ8vEkPSwAGQBtpduDP7Xbr/JMrLCIjI/XFF19owIABiouL065du/xeQLQSBoE6lupqKTNTWrfObAwaHS098YRvxwToLM4/Xxo+3ByS+Z31n/+cSg26fbsJ7OzebY7f/MZcd8EFJkDYGH63dR6NvZe148Eq+lJoU/n5jc90lqQDB8x1ZA8AAufECemtt87+//XgQWns2LYpU2dD2zWfZZmB7y1bTqXtbwf81ZcKCQlRYmKisrOzdfvtt9edz87O1m233dbovU6nU3369JEkbdiwQRMmTPCZhf/aa6+pqqpK9957b5PLhE5ozBgz+fTZZ71/x7tcJvDEpNTWcy5tHxZm0q/WT8FqWdLhw97BwNqvn39uxu/27DFHfaelDvVaLUjqUACtoNmBv4EDB2r79u3q37+/hg0bpiVLligkJESZmZnqX5vDGu0fg0AdxyefSE8+aYIeknTLLWbWUivk+QfaLYdDuuwyc9x5pzlXWuqdHvTjj81egWdz4IB0662dumNtkxTZSIqiTqOysvH3snY6WEVfCm2qtLRp1z36qJSYKF15pTkSEs6cThnAufF4zPvTv/8t7dxpjl27zB61QHtUVBToEnjxZ18qJSVFU6ZM0ZAhQzRixAhlZmaqsLBQs2bNkmT23tu/f7/WrVsnSSooKFBubq6GDRumQ4cOKS0tTTt27NDatWt9nr169WpNmjRJF1544blXGh3bmDHSTTfJk5+v8qIiRcTEyM4e9G3D321vs0k9e5rj2mu9f+Z2m1WA9VcInkwdqtNSh/qonzq0NjhI6lAALdTswN8TTzyhYydXU/z0pz/VhAkTdMMNN+jCCy/Uxo0b/V5AtJKmDgI19Tr434kT0ssvS6tWmc7DBRdIP/pRuxq8BgIqMlL6+tfNIZnBsjVrpJUrz37vgQOtWrRAs6kFb/CdWTsbrKIvhTZz4IBJd9sU5eUmVfL//u+pc3Fx0hVXmEDgFVeYyRedeNIE0Cosy/xfrA3w1R5Hj/peGxZmJrWczfLlJlCPU/LypNmzz34dbeetqe0WE9P6ZWkGf/alkpKSVFZWpoULF6qoqEgDBw7Upk2bFBcXJ0kqKipSYe1WGzKrDZcuXapdu3bJ6XRq9OjRysnJUXx8vNdzCwoK9MEHH+jdd989t8qi83A4pMREVVZWKiIsjJSPbamt2t7hMNuP9O7dOqlD668UJHUogEY0e1xw3Lhxdd/3799fO3fu1JdffqmePXt2/pUFnUm93PNn9Ne/mhksvXq1ZmlQ33/+Y/by+/hj83rMGCk11cwoAtCw0FAzkNOUwN8jj0gDBrR+mQLEY1k6VF2tniEhnXsT5127zp62Wmp3g1X0pdDqTpyQXntN+sUvpOPHz359dLTpd3zyyamgxBdfnBqM+OMfzXUOh3TxxacCgVdcYV53YaoBUOfwYe+VfDt3SmVlvteFhpq+SO3/pSuuMAOFt93W+Gp2l8tkZGH2v7chQ8zvMtquec7WbrV7cd9wQ9uW6yz83ZdKTk5WcnJygz9bs2aN1+uEhIQm7SF42WWXybKsZpcFQCfVWqlD6+8lSOpQACc161P6iRMnFBYWpm3btmngwIF15y+44AK/FwytbNCgs38wkqQ//9nM/L75Zunee6XLL2+b8gUrt1t69VUpI0OqqTFv4o8+Ko0bxyweoCma8rvN5ZK+853OPehjWaqprDSd/c78u+Pqq83epx1osIq+FFrdv/8tPf20CYxL5v/JN74hpaWd+Z5HHpGuu84ctQ4d8g5c/PvfJp1yQYE5fvtbc11DwYu+faV6+xwBndKxY2ai3un/V774wve62qB57f+RK688c9D8kUdM//9MHn64c/dhWsrhoO1aorF2q+1DLlvWrtqNvhSATqUlqUM/+8xkEygvlz76yBz19erlu0KQ1KFAUGlW4K9Lly6Ki4uT2+1urfKgrTTlg9H3v2/ePP7v/6Q//ckcQ4eaAODIkZ17MDkQPv/c7OW3fbt5ff310uOPS1FRgS0X0JEw6BNcOuBgFX0ptJqjR6X0dOnXvzYzh7t1k37wA2nSJBOE69XLrJA9PVDucpnfiWPG+D6vZ09p1ChzSKfSFdZfyXTs2Km9Vmudf77ZI/D0lYEuF31HdGzV1Sboffr/gb17zf+N+vr29f73P2BA02fejxkjLVnSvP+vMGi7ljlTu/XpY/pRd9wRsKI1hL4UgKDRnNShpwcHT08d+o9/eN/XWOrQHj38W363W8rPV1hRkcnCw76WQJtq0R5/qampeuWVV5hR1dGd6Rd6/Q9Gn3xiVqG9+64JAv7f/0n9+0v33CONH2/eNNByHo/Zf+eFF8wbd9euUkqKdOutDJABLcGgT3DpYINVEn0p+JllSX/5i7R0qXTwoDn3zW9KDz0kXXjhqevGjJFuukme/HyVFxUpIiZG9uZ8+LbZTPCwV69Te6t6PGaA4fRA4K5dJghZ22esdeGF3qsCr7zS/4MLgL+cOGGCeqcH+XbvNufrc7m898JMSDCB93Nxrv9fgxlt1zKnt1tBgSKuu072O+9st+1GXwpA0GtK6tDaYGBTU4d2724CgaenDo2PN5+tm5s6dPNm6dlnZS8pUY/ac9HRZuIuYzJAm7BZzUw6PmjQIH366aeqqalRXFycunbt6vXzf/3rX34tYCCUl5ere/fuOnLkiCIiIvz/Bxw5Im3ZYlZyBWpPFMuSpk0zs7PvuEOesWPP/sGouFjasMGkdjq5kbYuvFBKSpK+/W3zBtHBeCxLJZWVig4LC8w+WEVF0sKFpwbGhg6VfvzjNtmPKuB1DxDqHUT1druDdtAnqP++W3Gwyl/9A/pS585z6JBKPvhA0VFRsjudfn9+h/HFF9LixdLf/mZex8ZKjz0mDR9+xlta/ffDiRNmr+LTU4T+5z9mxm99F13kHQxMSDAToNqhoPy96iftvu0sS9q3zzvI98knZkJefT16eAf5rrjCO8DuZ+2+7dox2q5lPJalkn37FD14sOx9+/r9+fSlmo6+VOfE76bAoe1l+uNFRd7BwNNTh55J7QTA04OCtYfL5fu5e/PmxrMwLVlC8K8N8G8+MNpTX6rZUadJkya1tFxoT/72NxP0Cw2VZsyQIiNVWVmpiMb2g+rVS5o7V5o+Xfrd76Rf/cq8MaSnS1lZZoXa3XebmSBonGVJb75p9ts5dszMnPnhD6U772RPHMBfHA4pMfHsv9vQOdT+fbtcihg8uF0HeelL4ZydOGGyMWRmSlVVZiLZ1KnS975n+naB1KWLSWs4YIB0++3mXGWlSZFYGwj897/NYMMXX5jjz38219lsZlbx6asCL7008HVC51JScurf4s6dZo++8nLf6847z+xvfnqQ76KL6E8A7QB9KQBoAYfDjNn26dP81KFFReZoKHVobOypgGBsrPTSS42XY+lS6aab2vVndqAzaHbg78knn2yNcqAteTwmWCeZ1XpRUQ3vTXEm559v9vmbPFnKzpbWrzeDOa+9ZlJWjh4tTZkinbbRNk5z8KD005+emp1/9dXSU0+ZN0kAQKdHXwrn5MMPpUWLpE8/Na8HD5ZSU6V+/QJbrsaEhZn+ztVXnzpXUWECLqcHYA4ckP77X3O8/ba5rksX6ZJLvIOB/foFLmsGOpYjR7z/je3cKZWW+l7ndEqXXea9mi8ujgEpoJ2iLwUAftac1KG1X2tTh/7nP+ZoqgMHpPx8acgQv1YBgDc+MQejd981gbquXaX772/5c7p0MXv8ffObJlXlK69IOTlmn5m//EW69loTILzxRlaxSebN8p13zJL28nIzwJCcbFZJMqgAAAAaU14uvfiiSbkumRTrc+dKEyZ0zBVI3bpJ111njlplZd4pQnfuNAMNn3xijjfeMNeFhZmVWKcHA/v06ZjtAP/56ivz7+T0IN++fb7X2e1mv/LT//1cconpmwMAAOAUm03q2dMc117r/bPTU4fWBgP/9a+mBQEbmogFwK+aHfiz2+2yNfKh2t3Q/h1oP06ckH7xC/P9lCn+2ZfPZjs1cPPppyb11B//KG3bZo6+fU1wa8KE5m8G21kcOiT9/OcmICqZPWyeekq6+OKAFgsA0PboS6FZaicOpaVJX35pzk2cKM2ZY/Yb60wuvFC64QZzSKbuRUXegcBPPjFp0mv7mbUiIkz/6vQVW9HRgagF2kJNjbR7t/e/jf/+12Q2qS821jvIN2CAFB7e9mUG4Df0pQCgHWgodeg//ynNmnX2e//v/8y+5J3t8wzQjjQ78Pfb2lnGJ9XU1Cg/P19r167VggUL/FYwtJI33zQzXy+4wATj/O2SS6QnnzQr2WpTfxYWmqDXL34hfec75rjgAv//2e3VX/8qPf20GaxzOMyeilOnkqIKAIIUfSk02eefm7SeubnmdXy89KMfmfSewcBmM3uqXXSR9I1vmHMej5lRfPqqrl27zIrIf/zDe9+RyEjvgM8VV7Rs0pvbLeXnK6yoSIqJkQYNIltDU/mj7dxuae9e79Wgu3eb4F99UVHef+cJCf6Z6AigXaEvBQDt1KBBZvJdSUnj1/3+99Kf/iR961tmfDo+vk2KBwSTZkcebrvtNp9zd955p6688kpt3LhR06ZN80vB0AoqK6WVK8333/++2bC+tURFSbNnS9/7ngk2/vKX0hdfmD9/3Trplluke+7p3L/Yy8ulZ5+VNm0yry++WFqwwKSmAgAELfpSOKvqatNfysoy34eEmL7bffeZ74OZ3W72+OvXz2STkEwA6NNPvYOBe/aYFELvv2+OWr17nwoKXXml6Zc1tvpr82bp2WdlLylRj9pz0dHSI49IY8a0UiU7iZa0nWVJ+/ef+nusXeV5/LjvtRERp4J8tX+nUVGtVBkA7Ql9KQBopxwO09d79NEzX5OUZDJ37NplUvm/8YZZMXj33dKwYaTvB/zEb0uOhg0bphkzZvjrcWgNv/61dPCg1KuXdMcdbfNnnneeNHmydOed0v/+r7R+vfkA/9vfmuPGG03K0Wuv7Vy/2P/+d+knPzEzXOx2M1D3wAMM1gEAzqilfan09HQ988wzKioq0pVXXqlly5bphtpUiQ1Yvny5XnrpJe3du1d9+/bV448/rvvuu6/u51/72tf03nvv+dx3yy236O233252+dBMeXkmU8Bnn5nXw4ZJ8+aZdIVomNNpVnYlJJw699VXZjDh9GDg55+boNL+/VJ2trmuNpB4+qrASy81z9y8ueFBi5ISc37JEoJ/Z9LUtist9Q7y/fvf0pEjvveFhZ1K5Vr7d9W7d+f6/ADgnDEuBQDtwJgxpq/37LPeK/9cLunhh83PLcvsB/jLX5pJejk55ujf3wQAv/nN4N0uCvATvwT+vvrqK7344ovq06ePPx6H1nD0qLRmjfk+EAGoLl2km282aZq2bTMBwNoZ2O+/bz68T5kife1rHTsF5rFj0rJlJqgpmf0NFyyQrroqoMUCALRvLe1Lbdy4UXPnzlV6erpGjRqlFStWaPz48dq5c6f69u3rc31GRoZSU1O1cuVKDR06VLm5uZoxY4Z69uypiRMnSpLeeOMNVVdX191TVlama665Rt/5znfOrZJo3OHD0vPPS2+9ZV5feKH00EPSuHEEN1oiPNxMLLv22lPnysu9g0w7d5rBiP/8xxy1be90mvT1e/c2/mcsXSrddBNpP+tzu81AT2Pmz/cdDKrVpYt02WXeq/n69aOdATSKcSkAaEfGjJFuukme/HyVFxUpIiZG9tNTvttsUmKiOT7/XNqwwWSM27NH+ulPpZdeMotI7rzTpO8H0GzNjrD07NnTaxNly7JUUVGh8847T6+88opfCwc/evVVM3s2Pt6k2QwUm83kex40yAymvPqq9PbbZnbvvHlm5u5dd0m33tq6qUhbQ16eCfJ98YV5fdddJt0pM1QAAKfxZ18qLS1N06ZN0/Tp0yVJy5Yt0zvvvKOMjAwtWrTI5/r169dr5syZSkpKkiT1799fW7du1eLFi+sCfxfU24d3w4YNOu+88xoN/FVVVamqqqrudXl5uSTJ4/HI4/E0q05N4bEsWZYlj2WZ2aIdmWVJb70l2wsvyHZypZN1xx2yZs82qQxrr/ETr7YLNt26mRWUw4adOldaKv3737LVBgI//tj8PXz88dmfd+CArHvvNc/FKRUVsp1tX5eqKqmkRJbNZj6fXHGFrNqVfJde2vAkxSD8NxvU/1/PEW3XMh7LknXyq1rj/dtPz2RcCgA6AIdDSkxUZWWlIsLCzjyZMTZW+n//T5o1y+z9t2GDVFwsrVolrV1rJkLedZc0YEDblh/o4Jod+Hvuuee8Olh2u11RUVEaNmyYevbs6dfCwU8OHTIBNkl68MH2s6IuPl56/HFTpl//WnrtNZN66dlnpRUrzKyOpKT2P7OjslJavlz61a/M64sukn78Y2nIkMCWCwDQLvmrL1VdXa28vDzNmzfP6/zYsWOVk5PT4D1VVVUKqzchJTw8XLm5uaqpqZHT6fS5Z/Xq1Zo8ebK6du16xrIsWrRICxYs8Dl/8OBBVVZWNqU6zeI5elRHJFnV1bK73X5/fltx7N2r7s8+q5APP5Qk1fTvr/L/9/9UM3CguaA12s6ydKSmRpYkOysJpfPP9w4GWpYcX3yh8157TV3feOOst9t2727lAnZeFfffr+N33y2r/mQ/j6dV/u13RPx/bTnarmU8lmXeX8vLZT9bAL8FKioq/PIcxqUAoBPq1k26916zZdRf/2rSgG7fLv3hD+ZITDRpQG+4waTrB9CoZkeApk6d2grFQKvKypKOHzf7YrTHfUguuECaOVO6/37zi/yXv5QKC6WXXzYpQcePl+65x6Rcam8++kh68klTXkm6/XZp7lypkcFRAEBw81dfqrS0VG63Wy6Xy+u8y+VScXFxg/eMGzdOq1at0qRJkzR48GDl5eUpKytLNTU1Ki0tVUxMjNf1ubm52rFjh1avXt1oWVJTU5WSklL3ury8XLGxsYqKilJE7ao1P/KEhMgmKSokRPYGgpXtXmWlbC+/LK1bJ9uJE7LCwmQ98IAcd92lnq08QctjWabtwsIYDD+Tiy+Wxo6VmhD480yfbvYiwSl79si+atVZL+s6YoS61lthDG/8f2052q5l6totIkL26Gi/P7/+5KOWYlwKADqxLl3MVlHf+Ia0Y4cZJ/7LX0ymtbw8s0Jw8mRp4sSOly0OaEPNHll4+eWXdf755/uke/r1r3+t48eP6/777/db4eAHxcXS66+b72fPbt97xISFmVV+t98ubdlign4ffmj2W3nrLWnkSDPzY+jQwNejulpaudIsOfd4pKgos1fJyJGBLRcAoN3zd1/KVu890bIsn3O15s+fr+LiYg0fPlyWZcnlcmnq1KlasmSJHA3sn7V69WoNHDhQ1113XaNlCA0NVWhoqM95u90ue2vMxrTZZLPZZD95dChbt0o//7m0b595ff31sj32mGz1gq6tqcO2XVsaNEiKjm54D7paLpfsM2aw91x9brfZo+VsbTdoUOD79B0A/19bjrZrGZvMKsnWeP/21zMZlwKAIDFwoPT002Z8+7XXpN/+1uwJ+Mwz0i9+IU2aZLLF9eoV6JIC7U6ze10///nPFdlA6sXo6Gg9/fTTfikU/GjlSqmmxiyHPn0/k/bM4ZC+9jVp9Wqz6u/rXzdLuHNypORks/pv0ybpxInAlO+TT6T77jNl83jMnokbNxL0AwA0ib/6UpGRkXI4HD6r+0pKSnxWAdYKDw9XVlaWjh8/rr1796qwsFDx8fHq1q2bT5mOHz+uDRs21O0fiHNUWmpSnP/P/5igX1SUtHix9NxzUhsG/dBEDof0yCONX/PwwwT9GkLbAWhljEsBQJDp1Uv64Q+lt9+WHn1U6ttXqqgwi0Zuu01KTTVZ2QDUaXbg77PPPlO/fv18zsfFxamwNt0h2oe9e81KOckMMnXEmY5XXWUGxd54Q/rud82qwIICs4febbeZX/BHj7ZNWU6cMBvL3n+/9OmnUs+eZobJwoVSK6QxAwB0Tv7qS4WEhCgxMVHZ2dle57OzszXyLJNRnE6n+vTpI4fDoQ0bNmjChAk+s/Bfe+01VVVV6d57721ymdAAj8dkX7jzTumdd8xkpsmTzf7GX/96x+yfBYsxY6QlS8zKv9O5XOZ8e0yh317QdgBaEeNSABCkzjvPjA+//rqUlmaywrndUna29L3vmSM7O3CLRYB2pNmpPqOjo7V9+3bFx8d7nf/www914YUX+qtc8Idf/MIMNt14owmgdWR9+pgZHQ88IP3mN2Z594ED0vPPm2Dc7bebQbTWWtq9Z4/Zy+/jj83rMWPMbBI2DgcANJM/+1IpKSmaMmWKhgwZohEjRigzM1OFhYWaNWuWJLP33v79+7Vu3TpJUkFBgXJzczVs2DAdOnRIaWlp2rFjh9auXevz7NWrV2vSpEn0787F7t3SokVmU3pJuvxys+ovISGw5ULTjRkj3XSTPPn5Ki8qUkRMjElRyWq1s6PtALQSxqUAIMjZ7Wa8+8YbzQKRX/1K+tOfzKq/1FQzPpyUZFKBdusW6NICAdHswN/kyZP1wx/+UN26ddONN94oSXrvvfc0Z84cTZ482e8FRAt98on05z+bWeTJyYEujf/06CFNm2b2+vvTn6RXXpH++1/z9Ve/ksaONalAL7/cP3+e2202kc3IMPv6RUSYAOS4cczQBwC0iD/7UklJSSorK9PChQtVVFSkgQMHatOmTYqLi5MkFRUVec18d7vdWrp0qXbt2iWn06nRo0crJyfHZ+CsoKBAH3zwgd59991zq2yw+uorKTPT9CHcbjMz9cEHpe98x2xWj47F4ZASE1VZWamIsDD6gM1B2wFoBYxLAQDqXHaZWawxe7ZZLPL662ZPwOefN5/JJk6U7rpLio0NdEmBNtXskYef/vSn+uyzz/T1r39dXU4OXHg8Ht13333kUm9Pli83X7/5TemSSwJbltYQGmpSfU6caPb+e+UV6Z//lP74R3MMHWqCgyNHtnyA4fPPpaeekj780LweNUp64gmzJw8AAC3k775UcnKyks8wyWfNmjVerxMSEpSfn3/WZ1522WWyLKvZZYGkLVtMKsOiIvN69Giz39kZ9l0EAADNw7gUAMBHZKQ0c6Y0daoZG/7Vr6T//Mdkjfv1r6UbbpDuvltKTGQyGoJCswN/ISEh2rhxo376059q27ZtCg8P11VXXVU3sxztQF6e9Pe/mxm2M2cGujSty26Xrr/eHB9/bAKAf/6z9H//Z47+/U0A8JvflEJCvO91u6X8fIUVFUkxMVJt6qHavXheeEGqrJS6dpUeesgEGnljAACcI/pSnVRJifTss9LmzeZ1r17SY4+ZD5gAAMBv6EsBAM4oNNSk+LztNik312Rh+dvfpPffN8dll5kA4NixvmPFQCfS4lxDl156qS699FJ/lgX+YFmnVvvdfrvZGy9YJCRIP/uZ9IMfmFkdv/2t2Ztv4ULTJpMnS3fcIXXvbgblnn1W9pIS9ai9Pzpamj7dBA5zc825oUOlH//YBAYBAPAj+lKdhNttZpBmZEjHjplJRHffbfYlDg8PdOkAAOi06EsBAM7IZpOGDTPH3r1mrPgPfzB7Aj71lPTii2Yrhm9/W+rZM9ClBfzO3twb7rzzTv385z/3Of/MM8/oO9/5jl8KhXPwwQfS9u1mdsP06YEuTWD06mVW6L39tvTDH5qAXlmZCf5961vSnDlmn76SEu/7Skqkp582Qb/QUOn//T9zD0E/AIAf0ZfqRD7+2KSSefZZE/S76ipp/XrT1yDoBwBAq6AvBQBolvh4KTXVjBX/z/+YbZzKyqRf/EKaMEH66U9NWlCgE2l24O+9997Tt771LZ/z3/zmN/X+++/7pVBoIY9HSk833yclmdzGwaxbN+m++6Tf/15asEC69FKTuvNvf2v8PqfTpAxNSjKpRAEA8CP6Up3AsWPS0qXS/feb4N/550vz5kmrV5vUMQAAoNXQlwIAtEiPHmbi5ltvmWDfFVdIVVXS735nxoH/53+knByTUQ/o4Jod1Th69KhCGsh/63Q6VV5e7pdCoYXefVfavdsMPt1/f6BL0344nWal3y9/aWbgn01NjZn1AQBAK6Av1YFZlkkX/p3vmFQxHo80bpzZG/jOO5kwBABAG6AvBQA4J126SN/8prR2rbRqlTR6tPkst3WryR733e9Kv/mNWUACdFDNHp0YOHCgNm7c6HN+w4YNuuKKK/xSKLTAiRNmebIkTZli9rGDN5vNLOVuitLS1i0LACBo0ZfqoIqKpJSUU+nC+/SRXnrJ7C8c7FkWAABoQ/SlAAB+YbNJ114rPfOM9Nvfmr3au3aV/vtfadEis5Bk+XLp4MFAlxRoti7NvWH+/Pn69re/rf/85z8aM2aMJOkvf/mLfvnLX+r111/3ewHRRL//vbRvn3TBBdJddwW6NO1XUwfmGMADALQS+lIdzIkTJmtAZqaZ8dmli0kl/v3vS2FhgS4dAABBh74UAMDvevc2Ez0feEB6801p40Zp/37p5ZfNPu433yzdc490+eWBLinQJM0O/N1666363e9+p6efflqvv/66wsPDdc0112jz5s2KiIhojTLibCorzbJkyQxCnXdeYMvTng0aJEVHm5n6Z+JymesAAGgF9KU6kI8+kp5+2qRSl6TBg82m8P36BbZcAAAEMfpSAIBWc/75ZuVfUpL03ntmEui2bdIf/2iOwYPNopsbb5QcjkCXFjijZgf+JOlb3/pW3UbKhw8f1quvvqq5c+fqww8/lNvt9msB0QS//rVZchwTI91xR6BL0745HNIjj5g0XWfy8MP84gYAtCr6Uu1cRYVJ4/nGG2Zfv+7dzT7BEyeadDAAACCg6EsBAFqVwyGNGWOOnTvNHu/vviv961/m6N1bmjxZuvVWkx4UaGeavcdfrc2bN+vee+/VRRddpJdeekm33HKL/vnPf/qzbGiKo0elNWvM9w88IDWwwTXqGTNGWrLErPw7nctlzp9MFQIAQGuiL9UOWZb0pz9Jd95pNnO3LGnCBOn1180HOoJ+AAC0G/SlAABt4oorpJ/8RHrrLel73zMTQ/fvl5YulW65RXruOemLLwJdSsBLs1b87du3T2vWrFFWVpaOHTum7373u6qpqdFvfvMbNlAOlFdekY4cMSmnbrkl0KXpOMaMkW66SZ78fJUXFSkiJkb2QYNY6QcAaFX0pdqxffukn/9c2rrVvI6LM2k9hwwJbLkAAEAd+lIAgICJjpZmz5amTZP+8AezCvCzz6RXXzXfjx5t0oBecw2TRhFwTV7xd8stt+iKK67Qzp079eKLL+qLL77Qiy++2Jplw9l8+aX5xSJJs2YRtGouh0NKTFTlN74hJSbSfgCAVkVfqp2qqZFWrzZ7OGzdarInzJplPrgR9AMAoN2gLwUAaBfCwkyWmF//Wnr+eWnYMMnjkf7yF2n6dOn++00mmRMnAl1SBLEmr/h799139cMf/lAPPvigLr300tYsE5rq5Zelr74yy41JTwkAQLtGX6od+te/pEWLpP/+17y+7jpp3jypb9/AlgsAAPigLwUAaFfsdmnUKHN8+qmZPPrHP5o9AZ94QnrxRek735HuuEOKiAh0aRFkmrzib8uWLaqoqNCQIUM0bNgwvfTSSzp48GBrlg2NKS42+81IUnIyy4cBAGjn6Eu1I4cPSwsWmP2R//tf6YILzJ4Ny5cT9AMAoJ2iLwUAaLcuuUSaP9+kAJ01S7rwQunAAemll8z2XIsXm7SgQBtpcuBvxIgRWrlypYqKijRz5kxt2LBBvXv3lsfjUXZ2tioqKlqznKgvM9OkphoyxCwnBgAA7Rp9qXbAsswHsTvvNBuzS9Ltt5vJVOPHM5EKAIB2jL4UAKDdu+ACk+7zrbekp56SLrtMqqw0aUG//W1p7lwpN9d8NgVaUZMDf7XOO+88ff/739cHH3ygjz76SA8//LB+/vOfKzo6WrfeemtrlBH17d1rBq0ks6Eog1QAAHQY9KUCZO9eM/PyqafMir+LLzZ7+z3+OGlXAADoQOhLAQDavZAQacIE6dVXpV/8QrrhBjOG/8EHJnvf3XdLb74pVVUFuqTopJod+DvdgAEDtGTJEu3bt0+/+tWvWvSM9PR09evXT2FhYUpMTNSWLVsavX758uVKSEhQeHi4BgwYoHXr1vlcc/jwYc2ePVsxMTEKCwtTQkKCNm3a1KLytUu/+IXZMPTGG6Wrrgp0aQAAQAv5oy+Fs6iqMn2nu+6S8vKk0FDpf/7HfAC75ppAlw4AAJwD+lIAgHbNZjMZ+557TvrNb8yef2Fh0u7d0sKF0sSJ0ooVUllZoEuKTqaLPx7icDg0adIkTZo0qVn3bdy4UXPnzlV6erpGjRqlFStWaPz48dq5c6f6NrC/SkZGhlJTU7Vy5UoNHTpUubm5mjFjhnr27KmJEydKkqqrq3XzzTcrOjpar7/+uvr06aPPP/9c3bp180dVA++TT6Q//9n80khODnRpAACAH7S0L4Wz+Mc/pJ//XPr8c/N61Cjp0Uel3r0DWy4AAOBX9KUAAO1e377SY49JDz4o/e530saNZh/AlSulNWukb35Tuuces18gcI78EvhrqbS0NE2bNk3Tp0+XJC1btkzvvPOOMjIytGjRIp/r169fr5kzZyopKUmS1L9/f23dulWLFy+uC/xlZWXpyy+/VE5OjpxOpyQpLi6u0XJUVVWp6rRlteXl5ZIkj8cjj8dz7hWtz+MxeXxrj2awvfSSbJKsb35T1sUX+y0fsMeyZFmWPEGWXzhY6y0Fb92pN/UOBkFd75Nf1Qrv363SJ0Dr+PJLM6Pyj380r6OipIcflr7+dVKkAwAAAAACJyJCuu8+k+5z82bpl7+Uduww+wK+9ZZ03XXmZyNHSvZzStiIIBawwF91dbXy8vI0b948r/Njx45VTk5Og/dUVVUpLCzM61x4eLhyc3NVU1Mjp9OpN998UyNGjNDs2bP1+9//XlFRUbr77rv12GOPyeFwNPjcRYsWacGCBT7nDx48qMrKyhbWsBHHjpmvVVXSiRNNvs2Zn68Lt26V5XCodOpUuf1YNo9l6UhNjSxJ9iAaEAvWekvBW3fqTb2DQVDXW5JVXi57SYnfn19RUeH3Z8LPPB4zc/LFF6WKChPk++53zYzK888PdOkAAAAAADC6dJHGjjXH9u0mALh5s5Sba46+fc2WFRMmSOHhgS4tOpiABf5KS0vldrvlcrm8zrtcLhUXFzd4z7hx47Rq1SpNmjRJgwcPVl5enrKyslRTU6PS0lLFxMRoz5492rx5s+655x5t2rRJu3fv1uzZs3XixAn9+Mc/bvC5qampSklJqXtdXl6u2NhYRUVFKSIiwn+VrnXkiPkaGmr+gzeFZcm2cqX5ftIkXdi/v1+L5LEs2SRFhYUF3SBxMNZbCt66U2/qHQyCvt4REbJHR/v9+fUnHyFA3G7pgw8U9t57Uv/+Zr8Eh0P69FPp6afNByZJGjBA+tGPpCuvDGx5AQAAAABozNVXm6OoSHrtNem3v5UKC6XFi6WMDOn2282k1nqxFOBMAprqU5Js9QYkLcvyOVdr/vz5Ki4u1vDhw2VZllwul6ZOnaolS5bUrebzeDyKjo5WZmamHA6HEhMT9cUXX+iZZ545Y+A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",
      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best Hyperparameters (based on validation score):\n",
      "n_estimators: 100\n",
      "max_depth: 5\n",
      "max_features: sqrt\n"
     ]
    }
   ],
   "source": [
    "# Plot the validation curves\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
    "\n",
    "# Function to plot validation curve\n",
    "def plot_validation_curve(ax, param_range, train_scores, val_scores, param_name, title):\n",
    "    train_mean = np.mean(train_scores, axis=1)\n",
    "    train_std = np.std(train_scores, axis=1)\n",
    "    val_mean = np.mean(val_scores, axis=1)\n",
    "    val_std = np.std(val_scores, axis=1)\n",
    "    \n",
    "    # Convert param_range to strings if needed\n",
    "    if param_name == 'max_depth':\n",
    "        x_labels = [str(x) if x is not None else 'None' for x in param_range]\n",
    "        x_pos = range(len(param_range))\n",
    "    elif param_name == 'max_features':\n",
    "        x_labels = [str(x) for x in param_range]\n",
    "        x_pos = range(len(param_range))\n",
    "    else:\n",
    "        x_labels = param_range\n",
    "        x_pos = param_range\n",
    "    \n",
    "    ax.plot(x_pos, train_mean, 'o-', color='blue', label='Training Score')\n",
    "    ax.fill_between(x_pos, train_mean - train_std, train_mean + train_std, alpha=0.2, color='blue')\n",
    "    \n",
    "    ax.plot(x_pos, val_mean, 'o-', color='red', label='Validation Score')\n",
    "    ax.fill_between(x_pos, val_mean - val_std, val_mean + val_std, alpha=0.2, color='red')\n",
    "    \n",
    "    ax.set_xlabel(param_name)\n",
    "    ax.set_ylabel('Accuracy')\n",
    "    ax.set_title(title)\n",
    "    ax.legend()\n",
    "    ax.grid(True, alpha=0.3)\n",
    "    \n",
    "    if param_name in ['max_depth', 'max_features']:\n",
    "        ax.set_xticks(x_pos)\n",
    "        ax.set_xticklabels(x_labels, rotation=45)\n",
    "\n",
    "# Plot 1: Number of estimators\n",
    "plot_validation_curve(\n",
    "    axes[0], n_estimators_range, train_scores_trees, val_scores_trees,\n",
    "    'n_estimators', 'Effect of Number of Trees'\n",
    ")\n",
    "\n",
    "# Plot 2: Maximum depth\n",
    "plot_validation_curve(\n",
    "    axes[1], max_depth_range, train_scores_depth, val_scores_depth,\n",
    "    'max_depth', 'Effect of Maximum Depth'\n",
    ")\n",
    "\n",
    "# Plot 3: Maximum features\n",
    "plot_validation_curve(\n",
    "    axes[2], max_features_range, train_scores_features, val_scores_features,\n",
    "    'max_features', 'Effect of Max Features per Split'\n",
    ")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Find best parameters\n",
    "best_n_estimators = n_estimators_range[np.argmax(np.mean(val_scores_trees, axis=1))]\n",
    "best_max_depth = max_depth_range[np.argmax(np.mean(val_scores_depth, axis=1))]\n",
    "best_max_features = max_features_range[np.argmax(np.mean(val_scores_features, axis=1))]\n",
    "\n",
    "print(f\"\\nBest Hyperparameters (based on validation score):\")\n",
    "print(f\"n_estimators: {best_n_estimators}\")\n",
    "print(f\"max_depth: {best_max_depth}\")\n",
    "print(f\"max_features: {best_max_features}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6be3874",
   "metadata": {},
   "source": [
    "## 7. Understanding Individual Trees in the Forest\n",
    "\n",
    "Let's peek inside the Random Forest to see how individual trees differ from each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d58bc854",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing individual trees in the Random Forest...\n",
      "\n",
      "Tree Analysis:\n",
      "Tree 1: Accuracy = 0.9064, Top feature = worst concave points\n",
      "Tree 2: Accuracy = 0.9181, Top feature = worst concavity\n",
      "Tree 3: Accuracy = 0.9240, Top feature = worst concave points\n",
      "Tree 4: Accuracy = 0.9064, Top feature = worst radius\n",
      "Tree 5: Accuracy = 0.9064, Top feature = area error\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ensemble Accuracy: 0.9181\n",
      "Average Individual Tree Accuracy: 0.9123\n",
      "Ensemble Improvement: 0.0058\n"
     ]
    }
   ],
   "source": [
    "# Create a simple Random Forest with fewer trees for visualization\n",
    "rf_simple = RandomForestClassifier(\n",
    "    n_estimators=5,\n",
    "    max_depth=3,\n",
    "    random_state=42\n",
    ")\n",
    "rf_simple.fit(X_train_cancer, y_train_cancer)\n",
    "\n",
    "# Analyze the diversity of trees\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 12))\n",
    "axes = axes.ravel()\n",
    "\n",
    "print(\"Analyzing individual trees in the Random Forest...\")\n",
    "print(\"\\nTree Analysis:\")\n",
    "\n",
    "# Look at first 5 trees\n",
    "for i in range(5):\n",
    "    tree = rf_simple.estimators_[i]\n",
    "    \n",
    "    # Get predictions from individual tree\n",
    "    tree_pred = tree.predict(X_test_cancer)\n",
    "    tree_acc = accuracy_score(y_test_cancer, tree_pred)\n",
    "    \n",
    "    # Get feature importances for this tree\n",
    "    tree_importance = tree.feature_importances_\n",
    "    \n",
    "    # Plot feature importance for this tree\n",
    "    top_features_idx = np.argsort(tree_importance)[-10:]  # Top 10 features\n",
    "    axes[i].barh(range(10), tree_importance[top_features_idx], color=f'C{i}')\n",
    "    axes[i].set_yticks(range(10))\n",
    "    axes[i].set_yticklabels([feature_names[j] for j in top_features_idx])\n",
    "    axes[i].set_xlabel('Feature Importance')\n",
    "    axes[i].set_title(f'Tree {i+1}\\nAccuracy: {tree_acc:.3f}')\n",
    "    axes[i].grid(True, alpha=0.3)\n",
    "    \n",
    "    print(f\"Tree {i+1}: Accuracy = {tree_acc:.4f}, Top feature = {feature_names[np.argmax(tree_importance)]}\")\n",
    "\n",
    "# Plot ensemble performance\n",
    "ensemble_acc = rf_simple.score(X_test_cancer, y_test_cancer)\n",
    "axes[5].bar(['Tree 1', 'Tree 2', 'Tree 3', 'Tree 4', 'Tree 5', 'Ensemble'], \n",
    "           [accuracy_score(y_test_cancer, rf_simple.estimators_[i].predict(X_test_cancer)) for i in range(5)] + [ensemble_acc],\n",
    "           color=['C0', 'C1', 'C2', 'C3', 'C4', 'red'])\n",
    "axes[5].set_ylabel('Accuracy')\n",
    "axes[5].set_title('Individual Trees vs Ensemble')\n",
    "axes[5].grid(True, alpha=0.3)\n",
    "axes[5].tick_params(axis='x', rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"\\nEnsemble Accuracy: {ensemble_acc:.4f}\")\n",
    "individual_accuracies = [accuracy_score(y_test_cancer, rf_simple.estimators_[i].predict(X_test_cancer)) for i in range(5)]\n",
    "print(f\"Average Individual Tree Accuracy: {np.mean(individual_accuracies):.4f}\")\n",
    "print(f\"Ensemble Improvement: {ensemble_acc - np.mean(individual_accuracies):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f58a5a52",
   "metadata": {},
   "source": [
    "## 8. Out-of-Bag (OOB) Score\n",
    "\n",
    "One unique advantage of Random Forest is the Out-of-Bag (OOB) score, which provides a way to evaluate the model without needing a separate validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8c3e1ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Out-of-Bag (OOB) Analysis:\n",
      "Training Accuracy: 1.0000\n",
      "OOB Accuracy: 0.9573\n",
      "Test Accuracy: 0.9357\n",
      "\n",
      "OOB vs Test difference: 0.0216\n",
      "\n",
      "Analyzing OOB score vs number of trees...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between OOB and Test scores: 0.6988\n",
      "\n",
      "Key Insight: OOB score is a good proxy for test performance!\n",
      "This means you can tune hyperparameters using OOB score without overfitting to test data.\n"
     ]
    }
   ],
   "source": [
    "# Train Random Forest with OOB scoring enabled\n",
    "rf_oob = RandomForestClassifier(\n",
    "    n_estimators=100,\n",
    "    oob_score=True,\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")\n",
    "rf_oob.fit(X_train_cancer, y_train_cancer)\n",
    "\n",
    "# Get various scores\n",
    "train_score = rf_oob.score(X_train_cancer, y_train_cancer)\n",
    "test_score = rf_oob.score(X_test_cancer, y_test_cancer)\n",
    "oob_score = rf_oob.oob_score_\n",
    "\n",
    "print(\"Out-of-Bag (OOB) Analysis:\")\n",
    "print(f\"Training Accuracy: {train_score:.4f}\")\n",
    "print(f\"OOB Accuracy: {oob_score:.4f}\")\n",
    "print(f\"Test Accuracy: {test_score:.4f}\")\n",
    "print(f\"\\nOOB vs Test difference: {abs(oob_score - test_score):.4f}\")\n",
    "\n",
    "# Demonstrate how OOB score changes with number of trees\n",
    "n_trees_range = range(10, 201, 10)\n",
    "oob_scores = []\n",
    "test_scores = []\n",
    "\n",
    "print(\"\\nAnalyzing OOB score vs number of trees...\")\n",
    "for n_trees in n_trees_range:\n",
    "    rf_temp = RandomForestClassifier(\n",
    "        n_estimators=n_trees,\n",
    "        oob_score=True,\n",
    "        random_state=42,\n",
    "        n_jobs=-1\n",
    "    )\n",
    "    rf_temp.fit(X_train_cancer, y_train_cancer)\n",
    "    oob_scores.append(rf_temp.oob_score_)\n",
    "    test_scores.append(rf_temp.score(X_test_cancer, y_test_cancer))\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(n_trees_range, oob_scores, 'o-', label='OOB Score', color='blue')\n",
    "plt.plot(n_trees_range, test_scores, 's-', label='Test Score', color='red')\n",
    "plt.xlabel('Number of Trees')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('OOB Score vs Test Score\\n(How well does OOB predict test performance?)')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n",
    "\n",
    "# Calculate correlation between OOB and test scores\n",
    "correlation = np.corrcoef(oob_scores, test_scores)[0, 1]\n",
    "print(f\"Correlation between OOB and Test scores: {correlation:.4f}\")\n",
    "print(f\"\\nKey Insight: OOB score is a good proxy for test performance!\")\n",
    "print(f\"This means you can tune hyperparameters using OOB score without overfitting to test data.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58152d85",
   "metadata": {},
   "source": [
    "## 9. Practical Tips and Best Practices\n",
    "\n",
    "Let's wrap up with some practical guidelines for using Random Forest effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9d419c17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class Distribution Analysis:\n",
      "Training set class distribution: Counter({np.int64(0): 628, np.int64(1): 72})\n",
      "Test set class distribution: Counter({np.int64(0): 269, np.int64(1): 31})\n",
      "\n",
      "Training Random Forest models...\n"
     ]
    },
    {
     "data": {
      "image/png": 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rVi3X62yMMSdPnjRly5Y1jRo1cl03duxYI8k899xzbo9Zt25dI8nMnz/fdV12draJi4szXbp0ueTrlJSUZCTluYwaNcoYY8xrr72W73N++umnjSTz1Vdfua6TZGJiYszRo0fdjm3durWpUKGCOX78uNv1/fr1M8WLF3cd36FDB1O3bt2LjveZZ57J978jAAAuJrdDzr+EhYWZadOmXfS+OTk5Jjs72zzxxBMmNjbWnDt3znVbcnKySU5Ovqz7JiUlmeLFi5tffvnFdd3p06dN6dKlTZ8+fVzXPfTQQyYkJMRs3br1gt8nJSXFBAUF5WnAuXPnGknm888/N8YY88UXXxhJ5sUXX3Q77qmnnjKSzNixYy/6WuT24NNPP22ys7PNmTNnTFpammnYsKFJTEy86Ptz7mvxzjvvmODgYLdeSE5ONpLMt99+63afGjVqmNatW7u+Hj58uHE4HCYtLc3tuJYtW7r15LFjx0x4eLhp166d23F79+41YWFhplu3bq7rcvtu3rx5rutyW0qSWb9+vev6I0eOmODgYDNo0KCLvk7GmHz/e5Nkpk+fbowx5rHHHsv3OT/yyCPG4XCYbdu2GWP+7zW/5pprTFZWltuxBe34mjVrms6dO190vH379jX8X2EAKJrS09ONJHPvvfcW+D4Xel9PSkpy+9vKlbxHLFy40EgykydPdrt+zpw5RpL5z3/+4/Z9w8PDzb59+1zXpaWlGUkmMTHRZGRkuK5fsGCBkWQ+/vjji47r3LlzpnTp0qZVq1bGGGdrlCxZ0rz22mvGGGO6du1q7rrrLmOMMZmZmSY8PNx07drVGONsgmLFipn+/fu7PebJkydNQkKC6zhj/u/vMbm2bNliJJnhw4e73ff99983ktxe39wGffTRR92OnTx5spFkDhw44Lru+uuvz7crC/K3kvzQbXQb4EvYvhJ+zfxl66SdO3fqp59+0v333y9JOnv2rOvSrl07HThwQNu2bbvo4509e1YTJ05UjRo1FBoaqmLFiik0NFQ7duzIs8zc26ZNm6aQkBCFhoaqWrVq+uKLL/T+++/nOUPm559/Vrdu3ZSQkKDg4GCFhIQoOTlZkvI8B4fDkeczZ2rXrq1ffvnF9fWyZcsUHR3ttnWEpDxn4W/btk379+/Xgw8+qKCg//vVExUVpTvvvFNr167Vn3/+6XafDh06uH193XXXyeFwuJ3hXqxYMVWpUsVtTBdz6623KjU11e3y6KOPSpKWLl2qyMhI3XXXXW73yT2DZ8mSJW7X33777SpVqpTr6zNnzmjJkiW64447FBERkee/sTNnzmjt2rWSpJtuukkbN27Uo48+qi+//PKCZ5gBAHC53nnnHdd73RdffKEePXqob9++mjp1qttxS5cuVYsWLRQTE+NqgzFjxujIkSM6dOjQRb+HlfvWrVvXtaJOcm6nWa1aNbf38C+++ELNmjXTddddd8Hv+emnn6pmzZqqW7eu23tt69at3bZ6zt06J7f9cln9rN3hw4crJCRExYsXV926dfXDDz/ok08+cW2zlGvDhg3q1KmTYmNjXa9F9+7dlZOTk2fLzISEBN10001u1+XXWNdff73q1Klz0fGvWbNGp0+fzrN9VMWKFXX77bfn6ReHw6F27dq5vs5tqcTERNWrV891fenSpVW2bNkCN1bXrl3zNFbnzp0lOf87qVGjRp7n3LNnTxlj8uzW0KlTJ7dtVq10/E033aQvvvhCjz32mJYvX67Tp08XaPwAAP93Je8Rue9V57/f3n333YqMjMzzflu3bl2VL1/e9XVu2zRt2lQRERF5rr/U+63D4VBycrK++eYbZWdnKy0tTX/88Yfr406Sk5O1fPlyGWO0du1at8+T+/LLL3X27Fl1797d7T20ePHirvtdyIoVKyQ53+f/6q677nJ9tt35zv/7UO3atQv0HKUr/1sJ3Ua3Ab6ASTn4rYyMDB05ckTlypWT9H/7Rw8ZMkQhISFul9xJmcOHD1/0MQcNGqTRo0erc+fO+uSTT/Ttt98qNTVVderUsf2NI/cNdfXq1Xr99dcVHR2te++9Vzt27HAdc+rUKd1222369ttvNWHCBC1fvlypqamaP3++JOV5DhERESpevLjbdWFhYTpz5ozr6yNHjig+Pj7PeBISEty+zl16nt8S83LlyuncuXM6duyY2/WlS5d2+zo0NDTfMYWGhrqN6WJiYmLUoEEDt0vufyNHjhxRQkJCnv3Ty5Ytq2LFirmeQ67zn8uRI0d09uxZvfzyy3n+G8uNqNz/xkaMGKFnn31Wa9euVdu2bRUbG6vmzZsXaC95AAAK4rrrrnO917Vp00avv/66WrVqpWHDhrm2APruu+/UqlUrSdL06dP1zTffKDU1VaNGjZKUtw3+yup9Y2Nj8zxGWFiY23G///6767N9L+TgwYPatGlTnvfa6OhoGWNc77VHjhxRsWLF8nzf8xvlUv79738rNTVVq1at0rPPPqvs7Gz97W9/c+uCvXv36rbbbtNvv/2mF198UV9//bVSU1Ndn4FxOa9Fbpecz2pjnd8vF2qp87sr9/qCNlZcXFyexipTpoxrjBca31+fQ67zj7XS8S+99JKGDx+uBQsWqFmzZipdurQ6d+7s1sQAgKKrTJkyioiIcG07WJiu5D0ityvi4uLcrnc4HEpISMjzXpbf3zMudn1B3m+bNWumjIwMpaamatmyZYqPj3d9jEpycrIOHz6sLVu2uE5Myp2Uy30fvfHGG/O8j86ZM+eifwvLfV7n/90nv8bKdf71uVtTFuTvZlf6txK6jW4DfAGfKQe/9dlnnyknJ8d11lDum8uIESMu+Nljl/pMuP/+97/q3r27Jk6c6Hb94cOHVbJkSdfXuW+YmZmZeY7zlNw3VElq2LChrrvuOiUnJ2vgwIGuDxNeunSp9u/fr+XLl7tWx0m67L25JWecfPfdd3muT09Pz3OcJB04cCDPsfv371dQUJDbqjM7xMbG6ttvv5Uxxm1i7tChQzp79qzrv6Fc50/elSpVSsHBwXrwwQfVt2/ffL9H5cqVJTkDdtCgQRo0aJD++OMPLV68WCNHjlTr1q3166+/up05BwBAYaldu7a+/PJLbd++XTfddJNmz56tkJAQffrpp27/h3/BggWXfKwrue+FxMXFad++fRc9pkyZMgoPD8/zeR9/vV1yvq+fPXtWR44ccftjyvmNcikVKlRwNVbjxo2VkJCgBx54QGPHjnWtOlywYIEyMjI0f/58JSUlue6blpZm6Xv9VWxsbL5jtdpY5/eLHWJjYy84PkmXbCwrHR8ZGanx48dr/PjxOnjwoOvs644dO+qnn3664ucCAPCs4OBgNW/eXF988YX27dt3yZN1JOcEyfl/f5HyTh5cyXtEblf8/vvvbhNzxhilp6frxhtvLOAzvHy5k2zLly/XmjVr3P6uU6NGDZUpU0bLli3T8uXLlZiY6HpvzH0fnTt3rlunFERuZxw8eNBt5V9uYxW2K/1bCd125eg2wPNYKQe/tHfvXg0ZMkQxMTHq06ePJOcv/KpVq2rjxo15zgbJvURHR1/0cR0Oh9sH90rOyb/ffvvN7brcZfGbNm1yu/7jjz8u0PjPP+Pmctx2223q3r27PvvsM9eHoea+UZ7/HF5//fXL/j7NmjXTyZMn8zy3WbNmuX1dvXp1lS9fXrNmzXLbVjQjI0Pz5s1Tw4YNbZ+Iat68uU6dOpXnj4nvvPOO6/aLiYiIULNmzbRhwwbVrl073//G8jvDqmTJkrrrrrvUt29fHT16VHv27JFk7WwyAAAKIvePDbl/THI4HCpWrJiCg4Ndx5w+fVrvvvvuJR/rSu57IW3bttWyZcsuuqV4hw4dtGvXLsXGxub7XpvbYbl/uHrvvffc7n9+o1h1//33q2nTppo+fbpri6D8GssYo+nTp1/292nWrJm2bNmijRs3ul1//vgbNmyo8PBw/fe//3W7ft++fVq6dOkl+8Ubmjdvrq1bt2r9+vVu17/zzjtyOByuf1cXcrkdHx8fr549e+q+++7Ttm3bXFul01gAULSNGDFCxhj17t1bWVlZeW7Pzs7WJ5984vq6UqVKef7+snTpUp06deqC38Pqe0Tu++n577fz5s1TRkaGV95vr7/+esXFxWnp0qX6+uuvXSehS84WadKkiRYuXKi1a9e6vbe2bt1axYoV065duy74PnohTZo0kSTNmTPH7fq5c+fq7Nmzl/1cCvJ3rwv9rcQKus06ug3wPFbKwef98MMPrr2JDx06pK+//lozZsxQcHCwPvzwQ7czmF5//XW1bdtWrVu3Vs+ePVW+fHkdPXpUP/74o9avX68PPvjgot+rQ4cOmjlzpq699lrVrl1b69at0zPPPJPnzK0bb7xR1atX15AhQ3T27FmVKlVKH374oVatWlWg51SrVi0tX75cn3zyiRITExUdHX3JVXz5efLJJzVnzhyNHj1aixcvVqNGjVSqVCn985//1NixYxUSEqL33nsvTzRY0b17d02ZMkXdu3fXU089papVq+rzzz/Xl19+6XZcUFCQJk+erPvvv18dOnRQnz59lJmZqWeeeUZ//PGHJk2adNljKCzdu3fXK6+8oh49emjPnj2qVauWVq1apYkTJ6pdu3Zq0aLFJR/jxRdf1K233qrbbrtNjzzyiCpVqqSTJ09q586d+uSTT1x7b3fs2FE1a9ZUgwYNFBcXp19++UUvvPCCkpKSVLVqVUnO/w5yH7NHjx4KCQlR9erVLzl5DACA9H+NJDnPFJ8/f74WLVqkO+64w7Vyu3379nr++efVrVs3/eMf/9CRI0f07LPP5jmBJz9Xct8LeeKJJ/TFF1+oSZMmGjlypGrVqqU//vhDCxcu1KBBg3TttddqwIABmjdvnpo0aaKBAweqdu3aOnfunPbu3auvvvpKgwcP1s0336xWrVqpSZMmGjZsmDIyMtSgQQN98803VzRpmOvpp5/WzTffrCeffFJvvPGGWrZsqdDQUN13330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      "text/plain": [
       "<Figure size 1800x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Detailed Classification Reports:\n",
      "\n",
      "Default Random Forest:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Class 0       0.96      0.99      0.97       269\n",
      "     Class 1       0.84      0.68      0.75        31\n",
      "\n",
      "    accuracy                           0.95       300\n",
      "   macro avg       0.90      0.83      0.86       300\n",
      "weighted avg       0.95      0.95      0.95       300\n",
      "\n",
      "\n",
      "Balanced Random Forest:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Class 0       0.96      0.99      0.97       269\n",
      "     Class 1       0.84      0.68      0.75        31\n",
      "\n",
      "    accuracy                           0.95       300\n",
      "   macro avg       0.90      0.83      0.86       300\n",
      "weighted avg       0.95      0.95      0.95       300\n",
      "\n",
      "\n",
      "Custom Weights Random Forest:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Class 0       0.96      0.99      0.97       269\n",
      "     Class 1       0.84      0.68      0.75        31\n",
      "\n",
      "    accuracy                           0.95       300\n",
      "   macro avg       0.90      0.83      0.86       300\n",
      "weighted avg       0.95      0.95      0.95       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Demonstrate the effect of class imbalance and how to handle it\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "# Create an imbalanced dataset\n",
    "X_imb, y_imb = make_classification(\n",
    "    n_samples=1000,\n",
    "    n_features=10,\n",
    "    n_classes=2,\n",
    "    weights=[0.9, 0.1],  # 90% class 0, 10% class 1\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "X_train_imb, X_test_imb, y_train_imb, y_test_imb = train_test_split(\n",
    "    X_imb, y_imb, test_size=0.3, random_state=42, stratify=y_imb\n",
    ")\n",
    "\n",
    "print(\"Class Distribution Analysis:\")\n",
    "print(f\"Training set class distribution: {Counter(y_train_imb)}\")\n",
    "print(f\"Test set class distribution: {Counter(y_test_imb)}\")\n",
    "\n",
    "# Train models with different approaches\n",
    "print(\"\\nTraining Random Forest models...\")\n",
    "\n",
    "# 1. Default Random Forest\n",
    "rf_default = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf_default.fit(X_train_imb, y_train_imb)\n",
    "\n",
    "# 2. Balanced Random Forest\n",
    "rf_balanced = RandomForestClassifier(\n",
    "    n_estimators=100, \n",
    "    class_weight='balanced',\n",
    "    random_state=42\n",
    ")\n",
    "rf_balanced.fit(X_train_imb, y_train_imb)\n",
    "\n",
    "# 3. Custom class weights\n",
    "rf_custom = RandomForestClassifier(\n",
    "    n_estimators=100,\n",
    "    class_weight={0: 1, 1: 5},  # Give 5x weight to minority class\n",
    "    random_state=42\n",
    ")\n",
    "rf_custom.fit(X_train_imb, y_train_imb)\n",
    "\n",
    "# Make predictions\n",
    "pred_default = rf_default.predict(X_test_imb)\n",
    "pred_balanced = rf_balanced.predict(X_test_imb)\n",
    "pred_custom = rf_custom.predict(X_test_imb)\n",
    "\n",
    "# Evaluate models\n",
    "models = {\n",
    "    'Default': (rf_default, pred_default),\n",
    "    'Balanced': (rf_balanced, pred_balanced),\n",
    "    'Custom Weights': (rf_custom, pred_custom)\n",
    "}\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "\n",
    "for i, (name, (model, pred)) in enumerate(models.items()):\n",
    "    # Create confusion matrix\n",
    "    cm = confusion_matrix(y_test_imb, pred)\n",
    "    \n",
    "    # Plot confusion matrix\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', \n",
    "                xticklabels=['Class 0', 'Class 1'],\n",
    "                yticklabels=['Class 0', 'Class 1'],\n",
    "                ax=axes[i])\n",
    "    axes[i].set_title(f'{name} Random Forest')\n",
    "    axes[i].set_xlabel('Predicted')\n",
    "    axes[i].set_ylabel('Actual')\n",
    "    \n",
    "    # Calculate metrics\n",
    "    accuracy = accuracy_score(y_test_imb, pred)\n",
    "    axes[i].text(0.5, -0.1, f'Accuracy: {accuracy:.3f}', \n",
    "                transform=axes[i].transAxes, ha='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print detailed classification reports\n",
    "print(\"\\nDetailed Classification Reports:\")\n",
    "for name, (model, pred) in models.items():\n",
    "    print(f\"\\n{name} Random Forest:\")\n",
    "    print(classification_report(y_test_imb, pred, target_names=['Class 0', 'Class 1']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "428df8ca",
   "metadata": {},
   "source": [
    "## 10. Summary and Key Takeaways\n",
    "\n",
    "### What We've Learned About Random Forest\n",
    "\n",
    "1. **The Power of Ensemble**: Multiple weak learners create a strong learner\n",
    "2. **Variance Reduction**: Bootstrap sampling + averaging reduces overfitting\n",
    "3. **Feature Randomness**: Random feature selection increases diversity\n",
    "4. **Bias-Variance Trade-off**: RF reduces variance while maintaining low bias\n",
    "5. **Feature Importance**: Built-in feature ranking for interpretability\n",
    "6. **OOB Scoring**: Free validation without separate data\n",
    "7. **Robustness**: Handles missing values, outliers, and different data types well\n",
    "\n",
    "### When to Use Random Forest\n",
    "\n",
    "**✅ Good for:**\n",
    "- Tabular data with mixed feature types\n",
    "- When you need feature importance\n",
    "- Baseline model (often works well out-of-the-box)\n",
    "- When interpretability matters (more than neural networks)\n",
    "- Imbalanced datasets (with proper class weighting)\n",
    "\n",
    "**❌ Not ideal for:**\n",
    "- Very high-dimensional sparse data (like text)\n",
    "- When you need the absolute best performance (consider gradient boosting)\n",
    "- Real-time prediction with strict latency requirements\n",
    "- Small datasets (individual trees might be better)\n",
    "\n",
    "### Key Hyperparameters to Tune\n",
    "\n",
    "1. **n_estimators**: More trees = better performance (diminishing returns after ~100)\n",
    "2. **max_depth**: Controls individual tree complexity\n",
    "3. **max_features**: Controls randomness (√features for classification)\n",
    "4. **min_samples_split/leaf**: Controls overfitting\n",
    "5. **class_weight**: Handle imbalanced datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "181b1243",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Algorithm Comparison on Breast Cancer Dataset\n",
      "=======================================================\n",
      "Logistic Regression : 0.9807 ± 0.0065\n",
      "SVM                 : 0.9736 ± 0.0147\n",
      "K-Nearest Neighbors : 0.9649 ± 0.0096\n",
      "Decision Tree       : 0.9173 ± 0.0242\n",
      "Random Forest       : 0.9561 ± 0.0228\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best performing algorithm: Logistic Regression\n",
      "Best accuracy: 0.9807 ± 0.0065\n",
      "\n",
      "============================================================\n",
      "CONGRATULATIONS! You've mastered Random Forest!\n",
      "Key insights gained:\n",
      "✓ How ensemble methods reduce variance\n",
      "✓ The importance of bootstrap sampling and feature randomness\n",
      "✓ Feature importance and model interpretability\n",
      "✓ Hyperparameter tuning strategies\n",
      "✓ Handling imbalanced datasets\n",
      "✓ When to use Random Forest vs other algorithms\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "# Final comparison: Random Forest vs other common algorithms\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "print(\"Final Algorithm Comparison on Breast Cancer Dataset\")\n",
    "print(\"=\" * 55)\n",
    "\n",
    "# Define algorithms\n",
    "algorithms = {\n",
    "    'Logistic Regression': LogisticRegression(random_state=42, max_iter=1000),\n",
    "    'SVM': SVC(random_state=42),\n",
    "    'K-Nearest Neighbors': KNeighborsClassifier(),\n",
    "    'Decision Tree': DecisionTreeClassifier(random_state=42),\n",
    "    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "}\n",
    "\n",
    "# Standardize features for algorithms that need it\n",
    "scaler = StandardScaler()\n",
    "X_cancer_scaled = scaler.fit_transform(X_cancer)\n",
    "\n",
    "results = {}\n",
    "for name, model in algorithms.items():\n",
    "    if name in ['Logistic Regression', 'SVM', 'K-Nearest Neighbors']:\n",
    "        # Use scaled features\n",
    "        scores = cross_val_score(model, X_cancer_scaled, y_cancer, cv=5, scoring='accuracy')\n",
    "    else:\n",
    "        # Tree-based models don't need scaling\n",
    "        scores = cross_val_score(model, X_cancer, y_cancer, cv=5, scoring='accuracy')\n",
    "    \n",
    "    results[name] = scores\n",
    "    print(f\"{name:20}: {scores.mean():.4f} ± {scores.std():.4f}\")\n",
    "\n",
    "# Visualize results\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.boxplot([results[name] for name in algorithms.keys()], \n",
    "            labels=algorithms.keys())\n",
    "plt.ylabel('Cross-Validation Accuracy')\n",
    "plt.title('Algorithm Comparison\\n(5-Fold Cross-Validation on Breast Cancer Dataset)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Find best performing algorithm\n",
    "best_algorithm = max(results.items(), key=lambda x: x[1].mean())\n",
    "print(f\"\\nBest performing algorithm: {best_algorithm[0]}\")\n",
    "print(f\"Best accuracy: {best_algorithm[1].mean():.4f} ± {best_algorithm[1].std():.4f}\")\n",
    "\n",
    "print(\"\\n\" + \"=\"*60)\n",
    "print(\"CONGRATULATIONS! You've mastered Random Forest!\")\n",
    "print(\"Key insights gained:\")\n",
    "print(\"✓ How ensemble methods reduce variance\")\n",
    "print(\"✓ The importance of bootstrap sampling and feature randomness\")\n",
    "print(\"✓ Feature importance and model interpretability\")\n",
    "print(\"✓ Hyperparameter tuning strategies\")\n",
    "print(\"✓ Handling imbalanced datasets\")\n",
    "print(\"✓ When to use Random Forest vs other algorithms\")\n",
    "print(\"=\"*60)"
   ]
  }
 ],
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