Stop 06 · Machine Learning FundamentalsTopics · 9/13

Random Forests

In short

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).

More topics at this stop

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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