Supervised Learning: Classification
Logistic regression, SVMs, decision trees, and classification.
Decision Trees
Recursive binary splitting that produces interpretable if-then rules – the building block of ensemble methods.
K-Nearest Neighbors
Classify by majority vote of the K closest training examples – no training phase, all computation at prediction time.
Kernel Methods
The kernel trick maps data to higher dimensions without explicit computation – making linear methods handle nonlinear boundaries.
Logistic Regression
Linear model with sigmoid output for probability estimation – the workhorse baseline for binary classification.
Multi-Class Classification
Extending binary classifiers to multiple classes via one-vs-rest, one-vs-one, and native multi-class approaches.
Naive Bayes
Applying Bayes’ theorem with a strong independence assumption – surprisingly effective despite being “wrong” in theory.
Support Vector Machines
Finding the maximum-margin hyperplane that separates classes – elegant geometry with strong theoretical guarantees.