RandomForestRegressor

RandomForestRegressor

A model type for constructing a random forest regressor, based on MLJScikitLearnInterface.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

RandomForestRegressor = @load RandomForestRegressor pkg=MLJScikitLearnInterface

Do model = RandomForestRegressor() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in RandomForestRegressor(n_estimators=...).

A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree.