100 Facts About Random Forest

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Here’s a list of 100 technical facts about Random Forest:

1. Random Forest is an ensemble learning method that combines multiple Decision Trees for improved accuracy.
2. It reduces overfitting by averaging or voting on the predictions of individual trees.
3. The “Random” in Random Forest comes from training each tree on a random subset of the training data.
4. Random Forest introduces randomness in feature selection by considering a random subset of features at each split.
5. The ensemble nature of Random Forest makes it more robust and less sensitive to outliers and noise.
6. Bagging (Bootstrap Aggregating) is the fundamental concept behind Random Forest, involving training each tree on a bootstrap sample of the data.
7. Random Forest can be used for both classification and regression tasks.
8. The number of trees in a Random Forest, often denoted as n_estimators, is a hyperparameter that affects model performance.
9. Random Forest can handle missing values in the dataset during training and prediction.
10. It can provide an estimate of feature importance based on how much each feature contributes to reducing impurity.
11. Out-of-bag (OOB) samples, not included in the…