Decision Trees

Summary

A model that splits data on feature thresholds to form a tree of decisions — interpretable, handles non-linear relationships, and the building block of Random Forests and XGBoost.

How to Learn This

1Train a decision tree and visualize it to see exactly how it splits data.
2Learn how splits are chosen (Gini impurity, entropy).
3Experiment with max depth to see overfitting happen in real time.

Logistic Regression

Random Forests

More topics in Machine Learning Fundamentals

Supervised vs Unsupervised Learning Linear Regression Logistic Regression Random Forests K-Nearest Neighbors K-Means Clustering Support Vector Machines Naive Bayes scikit-learn Basics Train/Test Split Cross-Validation

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