Decision Trees

Summary

A model that splits data into branches based on feature thresholds, forming a tree of if/else decisions — highly interpretable, but prone to overfitting without constraints.

How to Learn This

1Fit and visualize a decision tree on a simple dataset.
2Learn how max_depth and min_samples_split control overfitting.
3Practice reading a tree's splits and translating them into plain-language rules.

Logistic Regression

Random Forests

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 Logistic Regression Random Forests Gradient Boosting (XGBoost, LightGBM)K-Nearest Neighbors Support Vector Machines Naive Bayes

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