Supervised Learning
Learn from labeled data — algorithms that map inputs to known outputs for prediction and classification.
Topics
Supervised Learning Overview
Algorithms, formulas, bias–variance tradeoff & evaluation metrics — all on one page.
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Linear Regression
Predict continuous values by fitting a line that minimizes squared error.
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Logistic Regression
Binary classification using the sigmoid function to output probabilities.
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Naive Bayes
Probabilistic classifier assuming feature independence — fast and effective for text.
K-Nearest Neighbors (KNN)
Classify by majority vote of the k closest data points in feature space.
Decision Trees
Rule-based splits using Gini impurity or entropy to partition data at each node.
Random Forest
Ensemble of decision trees using bagging to reduce variance and improve accuracy.
Support Vector Machine (SVM)
Find the optimal hyperplane that maximizes the margin between classes.