rf_regressor#

Random Forest Regressor.

Module Contents#

Classes Summary#

RandomForestRegressor

Random Forest Regressor.

Contents#

class evalml.pipelines.components.estimators.regressors.rf_regressor.RandomForestRegressor(n_estimators: int = 100, max_depth: int = 6, n_jobs: int = - 1, random_seed: Union[int, float] = 0, **kwargs)[source]#

Random Forest Regressor.

Parameters

n_estimators (float) – The number of trees in the forest. Defaults to 100.
max_depth (int) – Maximum tree depth for base learners. Defaults to 6.
n_jobs (int or None) – Number of jobs to run in parallel. -1 uses all processes. Defaults to -1.
random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges	{ “n_estimators”: Integer(10, 1000), “max_depth”: Integer(1, 32),}
model_family	ModelFamily.RANDOM_FOREST
modifies_features	True
modifies_target	False
name	Random Forest Regressor
supported_problem_types	[ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION,]
training_only	False

Methods

`clone`	Constructs a new component with the same parameters and random state.
`default_parameters`	Returns the default parameters for this component.
`describe`	Describe a component and its parameters.
`feature_importance`	Returns importance associated with each feature.
`fit`	Fits estimator to data.
`get_prediction_intervals`	Find the prediction intervals using the fitted RandomForestRegressor.
`load`	Loads component at file path.
`needs_fitting`	Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
`parameters`	Returns the parameters which were used to initialize the component.
`predict`	Make predictions using selected features.
`predict_proba`	Make probability estimates for labels.
`save`	Saves component at file path.

clone(self)#

Constructs a new component with the same parameters and random state.

Returns: A new instance of this component with identical parameters and random state.

default_parameters(cls)#

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns: Default parameters for this component.
Return type: dict

describe(self, print_name=False, return_dict=False)#

Describe a component and its parameters.

Parameters

print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

Returns dictionary if return_dict is True, else None.

Return type

None or dict

property feature_importance(self) → pandas.Series#

Returns importance associated with each feature.

Returns: Importance associated with each feature.
Return type: np.ndarray
Raises: MethodPropertyNotFoundError – If estimator does not have a feature_importance method or a component_obj that implements feature_importance.

fit(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None)#

Fits estimator to data.

Parameters

X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].

Returns

self

get_prediction_intervals(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None, coverage: List[float] = None) → Dict[str, pandas.Series][source]#

Find the prediction intervals using the fitted RandomForestRegressor.

Parameters

X (pd.DataFrame) – Data of shape [n_samples, n_features].
y (pd.Series) – Target data. Optional.
coverage (list[float]) – A list of floats between the values 0 and 1 that the upper and lower bounds of the prediction interval should be calculated for.

Returns

Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper.

Return type

dict

static load(file_path)#

Loads component at file path.

Parameters: file_path (str) – Location to load file.
Returns: ComponentBase object

needs_fitting(self)#

Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.

This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

Returns: True.

property parameters(self)#: Returns the parameters which were used to initialize the component.

predict(self, X: pandas.DataFrame) → pandas.Series#

Make predictions using selected features.

Parameters: X (pd.DataFrame) – Data of shape [n_samples, n_features].
Returns: Predicted values.
Return type: pd.Series
Raises: MethodPropertyNotFoundError – If estimator does not have a predict method or a component_obj that implements predict.

predict_proba(self, X: pandas.DataFrame) → pandas.Series#

Make probability estimates for labels.

Parameters: X (pd.DataFrame) – Features.
Returns: Probability estimates.
Return type: pd.Series
Raises: MethodPropertyNotFoundError – If estimator does not have a predict_proba method or a component_obj that implements predict_proba.

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#

Saves component at file path.

Parameters

file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.