Supervised Learning: Classification

Train a logistic regression classifier and interpret the output probabilities.

Evaluate a classifier using accuracy, precision, recall, F1-score, and the confusion matrix.

Train a K-Nearest Neighbors (KNN) classifier and explain its core mechanism.

Train a decision tree classifier and visualize the resulting tree to understand its logic.

Apply stratified splitting for datasets with imbalanced classes.

Handle class imbalance during model training using techniques like SMOTE and class weights.