labs/Lab-5/Lab-5-Practical/05_Tree_Interpretation_Overfitting_Lab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cell-001",
   "metadata": {},
   "source": [
    "# Module 5 — Lab 5: Interpreting Trees and Controlling Overfitting\n",
    "\n",
    "\n",
    "**Topics:** tree rules, complexity, feature importance, train-test gap\n",
    "\n",
    "> **Lab version:** Complete every `TODO`, run the cells in order, and use the self-check before opening the Solution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-002",
   "metadata": {},
   "source": [
    "## Learning objectives\n",
    "\n",
    "After completing this lab, you will be able to:\n",
    "\n",
    "- compare bounded and unrestricted trees;\n",
    "- quantify depth, number of leaves, and train-test performance gaps;\n",
    "- visualize an explainable tree;\n",
    "- export root-to-leaf logic as text rules;\n",
    "- interpret feature importance cautiously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-003",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "DATA_PATH = Path(\"data/malware_features.csv\")\n",
    "\n",
    "if not DATA_PATH.exists():\n",
    "    raise FileNotFoundError(\n",
    "        \"Missing data/malware_features.csv. Follow data/README.md, place the three \"\n",
    "        \"official UCI CSV files under data/raw/, and run: python data/prepare_dataset.py\"\n",
    "    )\n",
    "\n",
    "\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "\n",
    "from sklearn.tree import export_text, plot_tree\n",
    "\n",
    "\n",
    "def security_metrics(y_true, y_pred):\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=[0, 1]).ravel()\n",
    "    return {\n",
    "        \"accuracy\": accuracy_score(y_true, y_pred),\n",
    "        \"malware_precision\": precision_score(y_true, y_pred, zero_division=0),\n",
    "        \"malware_recall\": recall_score(y_true, y_pred, zero_division=0),\n",
    "        \"malware_f1\": f1_score(y_true, y_pred, zero_division=0),\n",
    "        \"false_positives\": int(fp),\n",
    "        \"false_negatives\": int(fn),\n",
    "        \"false_positive_rate\": fp / (fp + tn) if fp + tn else 0.0,\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-004",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 1: load data, define X/y, and make an 80/20 stratified split.\n",
    "df = None\n",
    "feature_cols = None\n",
    "X = None\n",
    "y = None\n",
    "X_train, X_test, y_train, y_test = None, None, None, None\n",
    "\n",
    "# TODO 2: implement make_tree and fit a bounded tree (depth 4, leaf 10)\n",
    "# and an unrestricted tree (no maximum depth, leaf 1).\n",
    "def make_tree(max_depth, min_samples_leaf):\n",
    "    pass\n",
    "\n",
    "bounded_tree = None\n",
    "unrestricted_tree = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-005",
   "metadata": {},
   "source": [
    "## 1. Compare model complexity and generalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-006",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 3: create complexity_comparison with depth, leaves, train/test F1,\n",
    "# F1 gap, test recall, and test false-positive rate for both trees.\n",
    "complexity_comparison = None\n",
    "display(complexity_comparison)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-007",
   "metadata": {},
   "source": [
    "## 2. Visualize and export the bounded tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-008",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 4: obtain transformed feature names, plot the bounded tree, and\n",
    "# export its rules as text with max_depth=4.\n",
    "imputer = None\n",
    "estimator = None\n",
    "transformed_names = None\n",
    "rule_text = None\n",
    "print(rule_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-009",
   "metadata": {},
   "source": [
    "## 3. Inspect impurity-based feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-010",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 5: create a descending importance Series using transformed names,\n",
    "# display the top 12, and plot a horizontal bar chart.\n",
    "importance = None\n",
    "top_importance = None\n",
    "display(top_importance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-011",
   "metadata": {},
   "source": [
    "## Interpretation warning\n",
    "\n",
    "A rule describes this fitted tree, not a universal malware signature. Correlated features can substitute for one another, high-cardinality features may receive inflated impurity importance, and attackers may deliberately modify static properties. Treat rules as analyst evidence that requires validation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-012",
   "metadata": {},
   "source": [
    "## Self-check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-013",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert bounded_tree.named_steps[\"tree\"].get_depth() <= 4\n",
    "assert unrestricted_tree.named_steps[\"tree\"].get_depth() >= bounded_tree.named_steps[\"tree\"].get_depth()\n",
    "assert complexity_comparison.loc[\"unrestricted\", \"leaves\"] >= complexity_comparison.loc[\"bounded\", \"leaves\"]\n",
    "assert isinstance(rule_text, str) and \"class:\" in rule_text\n",
    "assert np.isclose(importance.sum(), 1.0)\n",
    "assert len(top_importance) <= 12\n",
    "print(\"✓ Lab 5 completed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-014",
   "metadata": {},
   "source": [
    "## Reflection questions\n",
    "\n",
    "1. Which values in the comparison table indicate overfitting?\n",
    "2. Translate two root-to-malware paths into `IF ... AND ... THEN malware` rules.\n",
    "3. Why must feature importance not be interpreted as causal evidence?"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}