CSAI2026 labs: Lab-2..Lab-6 (Practical + Solution, data), Module-2..Module-6 notebooks and datasets
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mikrotik
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "cell-001",
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"metadata": {},
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"source": [
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"# Module 5 — Lab 3: Building a Decision Tree Baseline\n",
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"\n",
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"\n",
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"**Topics:** stratified split, dummy baseline, bounded tree, leakage-safe pipeline\n",
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"\n",
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"> **Lab version:** Complete every `TODO`, run the cells in order, and use the self-check before opening the Solution."
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-002",
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"metadata": {},
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"source": [
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"## Learning objectives\n",
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"\n",
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"After completing this lab, you will be able to:\n",
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"\n",
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"- create a reproducible stratified train-test split;\n",
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"- fit missing-value imputation on training data only;\n",
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"- compare a bounded Decision Tree with a dummy baseline;\n",
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"- evaluate malware precision, recall, F1, false positives, and false negatives;\n",
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"- explain why trees do not require feature scaling."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-003",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"DATA_PATH = Path(\"data/malware_features.csv\")\n",
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"\n",
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"if not DATA_PATH.exists():\n",
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" raise FileNotFoundError(\n",
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" \"Missing data/malware_features.csv. Follow data/README.md, place the three \"\n",
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" \"official UCI CSV files under data/raw/, and run: python data/prepare_dataset.py\"\n",
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" )\n",
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"\n",
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"\n",
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"from sklearn.dummy import DummyClassifier\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"\n",
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"\n",
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"def security_metrics(y_true, y_pred):\n",
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" tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=[0, 1]).ravel()\n",
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" return {\n",
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" \"accuracy\": accuracy_score(y_true, y_pred),\n",
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" \"malware_precision\": precision_score(y_true, y_pred, zero_division=0),\n",
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" \"malware_recall\": recall_score(y_true, y_pred, zero_division=0),\n",
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" \"malware_f1\": f1_score(y_true, y_pred, zero_division=0),\n",
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" \"false_positives\": int(fp),\n",
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" \"false_negatives\": int(fn),\n",
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" \"false_positive_rate\": fp / (fp + tn) if fp + tn else 0.0,\n",
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" }"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-004",
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO 1: load the data, exclude source and label from X, and create integer y.\n",
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"df = None\n",
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"feature_cols = None\n",
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"X = None\n",
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"y = None\n",
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"\n",
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"# TODO 2: make an 80/20 stratified split with random_state=42.\n",
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"X_train, X_test, y_train, y_test = None, None, None, None\n",
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"print(\"Train shape:\", None, \"Test shape:\", None)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-005",
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"metadata": {},
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"source": [
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"## 1. Establish a non-learning baseline\n",
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"\n",
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"Because malware is the majority class, a most-frequent classifier can achieve high accuracy while providing little operational value."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-006",
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO 3: fit a most-frequent DummyClassifier and calculate security metrics.\n",
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"dummy = None\n",
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"dummy_predictions = None\n",
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"dummy_metrics = None\n",
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"pd.Series(dummy_metrics, name=\"dummy\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-007",
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"metadata": {},
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"source": [
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"## 2. Train a bounded Decision Tree\n",
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"\n",
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"The imputer is inside the pipeline, so its medians are learned from training rows only. A shallow tree is intentionally chosen for readability and to reduce overfitting."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-008",
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO 4: create a pipeline with median imputation and a Decision Tree.\n",
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"# Use max_depth=4, min_samples_leaf=10, and random_state=42.\n",
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"tree_pipeline = None\n",
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"\n",
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"# TODO 5: fit the pipeline, predict the test set, and calculate security metrics.\n",
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"tree_predictions = None\n",
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"tree_metrics = None\n",
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"\n",
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"comparison = None\n",
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"display(comparison)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-009",
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"metadata": {},
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"source": [
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"## Self-check"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-010",
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"metadata": {},
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"outputs": [],
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"source": [
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"assert X_train.shape[0] + X_test.shape[0] == len(df)\n",
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"assert abs(y_train.mean() - y_test.mean()) < 0.01\n",
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"assert set(tree_predictions).issubset({0, 1})\n",
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"assert tree_pipeline.named_steps[\"tree\"].get_depth() <= 4\n",
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"assert set(comparison.index) == {\"Dummy\", \"Decision Tree\"}\n",
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"assert comparison.loc[\"Decision Tree\", \"malware_f1\"] >= comparison.loc[\"Dummy\", \"malware_f1\"]\n",
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"print(\"✓ Lab 3 completed\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-011",
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"metadata": {},
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"source": [
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"## Reflection questions\n",
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"\n",
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"1. Why can the dummy model have deceptively high accuracy?\n",
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"2. Why does a Decision Tree usually not require feature scaling?\n",
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"3. What is the security and interpretability benefit of limiting tree depth?"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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