🌲 Bagging & Ensemble Method 🎯 1. What is Bagging (Bootstrap Aggregation)? Bagging creates B bootstrap datasets from the original training data and trains B separate models. Each model: [\...

🎲 Bootstrapping 📌 1. What is Bootstrapping? Bootstrapping is a resampling technique used when we cannot sample additional data from the true distribution. The true distribution is usually u...

🌳 Classification Trees 📘 1. What is a Classification Tree? A classification tree is very similar to a regression tree, except: Regression → predicts continuous value Classification → pred...

🌳 Decision Trees — Overfitting and Pruning (Complete Reconstruction) This document reconstructs the provided slides with minimal summarization, preserving equations, algorithm flow, and visual int...

🌳 Decision Trees Decision trees are intuitive, interpretable models that split the feature space into simple regions. 1️⃣ Tree Terminology Review 🌲 General Tree Structure A general tree T is ...

📘 Regularization, Overfitting, Ridge & Lasso 🎯 Goal of Learning — Generalization The objective of machine learning is not to minimize training error, but to generalize well to unseen data. ...

📘 Training, Overfitting, Capacity & High‑Dimensional Learning 📉 Training vs Validation Curves 🎯 Overview Training and validation curves are core diagnostic tools for understanding how a ma...

🤖 Naive Bayes Complete mathematical reconstruction with intuition and practical fixes Covers Bayes rule, independence assumption, smoothing, and numerical stability. 1. 🧠 Bayes Optimal Clas...

Evaluating Classification Full mathematical notes with extended metrics and intuition Covers confusion matrix, threshold trade-off, ROC/AUC, and practical metrics. 1. 📊 Confusion Matrix (Cr...

📘 Why Discriminant Analysis? & LDA vs Logistic Regression — Mathematical Notes 🎯 Clean blog-ready markdown 📐 Full math preserved (no omission) 🧠 Focus: stability, small sample behavior, mul...