Hyperparameters

Hyperparameter	Type/Values	Default	Meaning
estimator	<sklearn.BaseEstimator>	Stree()	Base estimator used to build each element of the ensemble.
n_jobs	<int>	-1	Specifies the number of threads used to build the ensemble (-1 equals to all cores available)
random_state	<int>	None	Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls
max_features	<int>, <float> or {“auto”, “sqrt”, “log2”}	None	The number of features to consider in each tree: <int> max_features features for each tree. <float> max_features is a fraction and int(max_features * n_features) features are considered for each tree. “auto” max_features=sqrt(n_features) “sqrt” max_features=sqrt(n_features) “log2” max_features=log2(n_features) None max_features=n_features
max_samples	<int>, <float>	None	The number of samples to consider for bootstrap: <int> max_samples samples for each tree. <float> max_samples is a fraction and int(max_samples * n_samples) samples for each tree.
n_estimators	<int>	100	The number of trees the ensemble is going to build
be_hyperparams	<str>	"{}"	Hyperparameteres passed to the base estimator, i.e. "{\"C\": 17, \"kernel\": \"rbf\"}"