Decision Trees and Random Forests
A decision tree is just a flowchart. But a forest of them is one of the most powerful tools in ML. Language: Python Implement Gini impurity, entropy, and information gain calculations to find optimal decision tree splits. Build a decision tree classifier from scratch with pre-pruning controls (max depth, min samples). Construct a random forest using bootstrap sampling and feature randomization, and explain why it reduces variance. Compare MDI feature importance with permutation importance and identify when MDI is biased. You have tabular data. Rows are samples, columns are features, and there is a target column you want to predict. You could throw a neural network at it. But for tabular data, tree-based models (decision trees, random forests, gradient boosted trees) consistently outperform deep learning. Kaggle competitions on structured data are dominated by XGBoost and LightGBM, not transformers. Why? Trees handle mixed feature types (numeric and categorical) without preprocessing. They handle nonlinear relationships without feature engineering. They are interpretable: you can look at the tree and see exactly why a prediction was made. And random forests, which average many trees, are highly resistant to overfitting on moderate-sized datasets. This lesson builds decision trees from scratch using recursive splitting, then builds a random forest on top. You will implement the math behind split criteria (Gini impurity, entropy, information gain) and understand…
Decision Trees and Random Forests: A decision tree is just a flowchart. But a forest of them is one of the most powerful tools in ML.
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
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