The scikit-learn library implements random forests by providing two estimators: RandomForestClassifier and RandomForestRegressor. They take various parameters, some of which are explained as follows:
- n_estimators: This parameter is the number of trees the algorithm builds before taking a maximum vote or the average prediction. In general, the higher the number of trees the better the performance and the accuracy of the predictions, but it also costs more in terms of computation.
- max_features: This parameter is the maximum number of features that the random forest is allowed to try in an individual tree.
- min_sample_leaf: This parameter determines the minimum number of leaves that are required to split an internal node.
- n_jobs: This hyperparameter tells the engine how many jobs to run in parallel...
