Helpful examples of using Extra Trees machine learning algorithms in scikit-learn.
The Extra Trees (Extremely Randomized Trees) algorithm is an ensemble learning method used for both classification and regression tasks. It is similar to the Random Forest algorithm but introduces more randomness in the construction of the trees to enhance model diversity and robustness.
In Extra Trees, both the selection of the feature to split on and the split point are chosen randomly, as opposed to Random Forests where the best split among a random subset of features is chosen. This increased randomness helps reduce variance and overfitting, particularly in noisy datasets.
Like Random Forests, Extra Trees builds multiple trees in parallel on different bootstrap samples of the training data and aggregates their predictions by majority voting for classification or averaging for regression.
The result is a robust, high-variance reduction model that is computationally efficient and often achieves high predictive performance.