autoemulate.emulators.random_forest

autoemulate.emulators.random_forest#

class RandomForest(n_estimators=100, criterion='squared_error', max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features=1.0, bootstrap=True, oob_score=False, max_samples=None, random_state=None)[source]#

Bases: BaseEstimator, RegressorMixin

Random forest Emulator.

Implements Random Forests regression from scikit-learn.

fit(X, y)[source]#

Fits the emulator to the data.

Parameters:

  • X ({array-like, sparse matrix}, shape (n_samples, n_features)) – The training input samples.

  • y (array-like, shape (n_samples,) or (n_samples, n_outputs)) – The target values (class labels in classification, real numbers in regression).

Returns:

self – Returns self.

Return type:

object

predict(X)[source]#

Predicts the output of the simulator for a given input.

Parameters:

  • X ({array-like, sparse matrix}, shape (n_samples, n_features)) – The training input samples.

  • return_std (bool) – If True, returns a touple with two ndarrays, one with the mean and one with the standard deviations of the prediction.

Returns:

y – Model predictions.

Return type:

ndarray, shape (n_samples,)

get_grid_params(search_type='random')[source]#

Returns the grid parameters of the emulator.

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