Logistic Regression

Summary

A close cousin of linear regression used for classification — outputs a probability rather than a raw number, and remains a strong, interpretable baseline for binary classification problems.

How to Learn This

1Fit a logistic regression model on a binary classification dataset.
2Learn to interpret coefficients in terms of odds ratios.
3Compare its performance to a simple baseline (always predicting the majority class).

Linear Regression

Decision Trees

More topics in Machine Learning Fundamentals

Supervised vs Unsupervised vs Reinforcement Learning Train / Test / Validation Split Overfitting & Underfitting Bias-Variance Tradeoff Cross-Validation Linear Regression Decision Trees Random Forests Gradient Boosting (XGBoost, LightGBM)K-Nearest Neighbors Support Vector Machines Naive Bayes

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