From the course: Machine Learning with scikit-learn

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Rapidly build models with random forest

Rapidly build models with random forest - scikit-learn Tutorial

From the course: Machine Learning with scikit-learn

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Rapidly build models with random forest

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Decision trees are powerful because they're easy to interpret, but that strength can also be a weakness. A single tree has a tendency to memorize its training data. If you let it grow too deep, it will start carving out overly specific boundaries that perfectly classify the training set, but fail to generalize to new data. This problem is called overfitting, it's one of the most common pitfalls in machine learning. The solution? Combine many imperfect trees into one strong model. This is called an ensemble. Instead of trusting a single decision tree, you train a collection of trees and let them vote on the final prediction. Each tree sees a slightly different slice of the data in a random subset of the features, So they learn different patterns and make different mistakes. When you aggregate their predictions, the errors tend to cancel out and the shared signal becomes stronger. That idea forms the foundation of the Random Forest, one of the most reliable and high-performing…

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