A random forest aggregates deeply grown decision trees fitted to bootstrap samples, while restricting each split to a random subset of predictor coordinates. Classification uses a majority vote over the trees.
The out-of-bag error predicts each training observation using only trees whose bootstrap samples omitted it, then averages the resulting losses.
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Random forest is a popular machine-learning algorithm that belongs to the family of ensemble methods. It is primarily used for classification and regression tasks. The key idea behind random forests is to combine multiple decision trees to create a more robust and accurate model. Here’s how it works: 1. **Ensemble Learning**: Random forest builds multiple decision trees (hence the term "forest") during training and merges their outputs to improve predictive accuracy and control overfitting.