Random Forests

Summary

An ensemble of many decision trees trained on random subsets of data and features, with predictions averaged (or voted) together — usually far more accurate and stable than any single tree.

How to Learn This

1Fit a random forest and compare its performance to a single decision tree.
2Learn how feature importance scores work in a random forest.
3Understand why averaging many 'weak' trees reduces overfitting (the wisdom of crowds effect).

Decision Trees

Gradient Boosting (XGBoost, LightGBM)

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 Decision Trees Gradient Boosting (XGBoost, LightGBM)K-Nearest Neighbors Support Vector Machines Naive Bayes

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