finished lab

Classification Lab/inclass_lab.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dffa60e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3340eabf",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "Inclass-Lab\n",
+    "\n",
+    "Automatically generated by Colab.\n",
+    "\n",
+    "Original file is located at\n",
+    "    https://colab.research.google.com/drive/13N7lQHvv4_LxKgcerumJ-18e5GfdBggm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34271feb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9491847c",
+   "metadata": {
+    "cell_marker": "\"\"\""
+   },
+   "source": [
+    "In-Class Lab: Comparing Classification Models (Breast Cancer Dataset)\n",
+    "\n",
+    "Learning Objectives\n",
+    "\n",
+    "---\n",
+    "\n",
+    "\n",
+    "By the end of this lab, you should be able to:\n",
+    "\n",
+    "\n",
+    "\n",
+    "*   Train multiple models (KNN, Logistic Regression, Decision Tree)\n",
+    "*   Compare performance across various scenarios\n",
+    "*   Understand the effect of scaling and evaluation methods\n",
+    "*   Interpret confusion matrix & classification report\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cceb43dc",
+   "metadata": {
+    "cell_marker": "####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 1:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "818bfdad",
+   "metadata": {
+    "cell_marker": "'''",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "Load dataset\n",
+    "Explain features/target\n",
+    "Do train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6de94b5d",
+   "metadata": {
+    "cell_marker": "####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8899ca0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_breast_cancer\n",
+    "data = load_breast_cancer()\n",
+    "X = data.data\n",
+    "y = data.target\n",
+    "\n",
+    "print(data.target_names)\n",
+    "print(X.shape)\n",
+    "#0 - malignant\n",
+    "#1 - benigh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae1b0d61",
+   "metadata": {
+    "cell_marker": "###############################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 2: IMPORT LIBRARIES FOR CLASSIFIERS\n",
+    "##############################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8db6dfd6",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2301b40",
+   "metadata": {
+    "cell_marker": "#########################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 3: IMPORT MODUL FOR DATA SPLIT\n",
+    "########################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75fe2532",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb69921d",
+   "metadata": {
+    "cell_marker": "####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 4: SPLIT THE DATASET\n",
+    "###################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6eb66fc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b182e930",
+   "metadata": {
+    "cell_marker": "####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 5\n",
+    "TASK 1: Train 3 models (no scaling)\n",
+    "###################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f31f0262",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "knn = KNeighborsClassifier(n_neighbors=5)\n",
+    "logreg = LogisticRegression(max_iter=5000)\n",
+    "tree = DecisionTreeClassifier()\n",
+    "\n",
+    "knn.fit(X_train, y_train)\n",
+    "logreg.fit(X_train, y_train)\n",
+    "tree.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3e0088e",
+   "metadata": {
+    "cell_marker": "####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 6\n",
+    "Task 2: Evaluate accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a8845a1",
+   "metadata": {
+    "cell_marker": "'''",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "* Which model performs best?\n",
+    "* Are the results very different?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d49b62b3",
+   "metadata": {
+    "cell_marker": "####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "395d887c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"KNN:\", knn.score(X_test, y_test))\n",
+    "print(\"LogReg:\", logreg.score(X_test, y_test))\n",
+    "print(\"Tree:\", tree.score(X_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9de74c34",
+   "metadata": {
+    "cell_marker": "####################################"
+   },
+   "source": [
+    "BLOCK 7\n",
+    "Part 2: Evaluation Metrics\n",
+    "Task 3: Confusion Matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "deec64c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "print(confusion_matrix(y_test, knn.predict(X_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18bc0c0e",
+   "metadata": {
+    "cell_marker": "#####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 8: CLASSIFICATION FREPORT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2e8747f",
+   "metadata": {
+    "cell_marker": "'''",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "* Which model has better recall?\n",
+    "* Which is better for detecting cancer?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88303af7",
+   "metadata": {
+    "cell_marker": "#####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9ef7bea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "print(classification_report(y_test, knn.predict(X_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1d8978a",
+   "metadata": {
+    "cell_marker": "########################################"
+   },
+   "source": [
+    "BLOCK 9:\n",
+    "Part 3: Scaling Effect\n",
+    "Task 5: Apply scaling using pipeline\n",
+    "#######################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84e1f5d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+    "\n",
+    "knn_scaled = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('knn', KNeighborsClassifier(n_neighbors=5))\n",
+    "])\n",
+    "\n",
+    "logreg_scaled = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('logreg', LogisticRegression(max_iter=5000))\n",
+    "])\n",
+    "\n",
+    "tree_scaled = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('tree', DecisionTreeClassifier(random_state=42))\n",
+    "])\n",
+    "\n",
+    "knn_scaled.fit(X_train, y_train)\n",
+    "logreg_scaled.fit(X_train, y_train)\n",
+    "tree_scaled.fit(X_train, y_train)\n",
+    "\n",
+    "knn_pred_scaled = knn_scaled.predict(X_test)\n",
+    "logreg_pred_scaled = logreg_scaled.predict(X_test)\n",
+    "tree_pred_scaled = tree_scaled.predict(X_test)\n",
+    "\n",
+    "print(\"\\n=== RESULTS WITH SCALING ===\")\n",
+    "print(\"KNN Accuracy:\", accuracy_score(y_test, knn_pred_scaled))\n",
+    "print(\"Logistic Regression Accuracy:\", accuracy_score(y_test, logreg_pred_scaled))\n",
+    "print(\"Decision Tree Accuracy:\", accuracy_score(y_test, tree_pred_scaled))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5caa8ec",
+   "metadata": {
+    "cell_marker": "#####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 10: CLASSIFICATION REPORT\n",
+    "####################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6643f0af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\n=== CLASSIFICATION REPORTS (WITH SCALING) ===\")\n",
+    "\n",
+    "print(\"\\nKNN Report:\")\n",
+    "print(classification_report(y_test, knn_pred_scaled))\n",
+    "\n",
+    "print(\"\\nLogistic Regression Report:\")\n",
+    "print(classification_report(y_test, logreg_pred_scaled))\n",
+    "\n",
+    "print(\"\\nDecision Tree Report:\")\n",
+    "print(classification_report(y_test, tree_pred_scaled))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c38c51d0",
+   "metadata": {
+    "cell_marker": "####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 11: CONFUSION MATRIX\n",
+    "###################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "444fbb13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\n=== CONFUSION MATRICES (WITH SCALING) ===\")\n",
+    "\n",
+    "print(\"\\nKNN Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, knn_pred_scaled))\n",
+    "\n",
+    "print(\"\\nLogistic Regression Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, logreg_pred_scaled))\n",
+    "\n",
+    "print(\"\\nDecision Tree Confusion Matrix:\")\n",
+    "print(confusion_matrix(y_test, tree_pred_scaled))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5d82a08",
+   "metadata": {
+    "cell_marker": "###############################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 12: MODWLS WITH CROSS-VALIDATION\n",
+    "##############################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17493cc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "knn_pipeline = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('knn', KNeighborsClassifier(n_neighbors=5))\n",
+    "])\n",
+    "\n",
+    "logreg_pipeline = Pipeline([\n",
+    "    ('scaler', StandardScaler()),\n",
+    "    ('logreg', LogisticRegression(max_iter=5000))\n",
+    "])\n",
+    "\n",
+    "tree_model = DecisionTreeClassifier(random_state=42)\n",
+    "\n",
+    "knn_cv_scores = cross_val_score(knn_pipeline, X, y, cv=5, scoring='accuracy')\n",
+    "logreg_cv_scores = cross_val_score(logreg_pipeline, X, y, cv=5, scoring='accuracy')\n",
+    "tree_cv_scores = cross_val_score(tree_model, X, y, cv=5, scoring='accuracy')\n",
+    "\n",
+    "print(\"=== Cross-Validation Results ===\")\n",
+    "\n",
+    "print(\"\\nKNN\")\n",
+    "print(\"CV Scores:\", knn_cv_scores)\n",
+    "print(\"Mean Accuracy:\", np.mean(knn_cv_scores))\n",
+    "print(\"Standard Deviation:\", np.std(knn_cv_scores))\n",
+    "\n",
+    "print(\"\\nLogistic Regression\")\n",
+    "print(\"CV Scores:\", logreg_cv_scores)\n",
+    "print(\"Mean Accuracy:\", np.mean(logreg_cv_scores))\n",
+    "print(\"Standard Deviation:\", np.std(logreg_cv_scores))\n",
+    "\n",
+    "print(\"\\nDecision Tree\")\n",
+    "print(\"CV Scores:\", tree_cv_scores)\n",
+    "print(\"Mean Accuracy:\", np.mean(tree_cv_scores))\n",
+    "print(\"Standard Deviation:\", np.std(tree_cv_scores))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94d94dd2",
+   "metadata": {
+    "cell_marker": "####################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 13: CONFUSION MATRIX\n",
+    "###################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c706abc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_predict\n",
+    "from sklearn.metrics import confusion_matrix, classification_report\n",
+    "\n",
+    "knn_pred = cross_val_predict(knn_pipeline, X, y, cv=5)\n",
+    "logreg_pred = cross_val_predict(logreg_pipeline, X, y, cv=5)\n",
+    "tree_pred = cross_val_predict(tree_model, X, y, cv=5)\n",
+    "\n",
+    "print(\"\\n=== Confusion Matrices ===\")\n",
+    "print(\"\\nKNN\")\n",
+    "print(confusion_matrix(y, knn_pred))\n",
+    "\n",
+    "print(\"\\nLogistic Regression\")\n",
+    "print(confusion_matrix(y, logreg_pred))\n",
+    "\n",
+    "print(\"\\nDecision Tree\")\n",
+    "print(confusion_matrix(y, tree_pred))\n",
+    "\n",
+    "print(\"\\n=== Classification Reports ===\")\n",
+    "print(\"\\nKNN\")\n",
+    "print(classification_report(y, knn_pred))\n",
+    "\n",
+    "print(\"\\nLogistic Regression\")\n",
+    "print(classification_report(y, logreg_pred))\n",
+    "\n",
+    "print(\"\\nDecision Tree\")\n",
+    "print(classification_report(y, tree_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34354ec9",
+   "metadata": {
+    "cell_marker": "###########################################",
+    "lines_to_next_cell": 0
+   },
+   "source": [
+    "BLOCK 14: LETS GET THE BEST VALUE FOR K\n",
+    "task: use the k value to recompute the previous KNN model and evaluate the performance\n",
+    "#########################################"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93fa45e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "import numpy as np\n",
+    "\n",
+    "k_values = range(1, 21)\n",
+    "scores = []\n",
+    "\n",
+    "for k in k_values:\n",
+    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
+    "    cv_scores = cross_val_score(knn, X, y, cv=5)\n",
+    "    scores.append(np.mean(cv_scores))\n",
+    "\n",
+    "best_k = k_values[np.argmax(scores)]\n",
+    "\n",
+    "print(\"Best k:\", best_k)\n",
+    "print(\"Best CV Accuracy:\", max(scores))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "encoding": "# -*- coding: utf-8 -*-",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}