rf_regressor¶

Module Contents¶

Classes Summary¶

RandomForestRegressor

Random Forest Regressor.

Contents¶

class evalml.pipelines.components.estimators.regressors.rf_regressor.RandomForestRegressor(n_estimators=100, max_depth=6, n_jobs=- 1, random_seed=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
predict_uses_y	False
supported_problem_types	[ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION,]

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 component to data
`load`	Loads component at file path
`needs_fitting`	Returns boolean determining if component needs fitting before
`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

prints and returns dictionary

Return type

None or dict

property feature_importance(self)¶

Returns importance associated with each feature.

Returns: Importance associated with each feature
Return type: np.ndarray

fit(self, X, y=None)¶

Fits component to data

Parameters

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

Returns

self

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.

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

predict(self, X)¶

Make predictions using selected features.

Parameters: X (pd.DataFrame, np.ndarray) – Data of shape [n_samples, n_features]
Returns: Predicted values
Return type: pd.Series

predict_proba(self, X)¶

Make probability estimates for labels.

Parameters: X (pd.DataFrame, or np.ndarray) – Features
Returns: Probability estimates
Return type: pd.Series

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.

Returns

None

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