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2.7 KiB
2.7 KiB
Hyperparameters
Hyperparameter | Type/Values | Default | ||
---|---|---|---|---|
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\"}" |