xgboost_regressor

XGBoost Regressor.

Module Contents

Classes Summary

XGBoostRegressor

XGBoost Regressor.

Contents

class evalml.pipelines.components.estimators.regressors.xgboost_regressor.XGBoostRegressor(eta=0.1, max_depth=6, min_child_weight=1, n_estimators=100, random_seed=0, n_jobs=12, **kwargs)[source]

XGBoost Regressor.

Parameters
  • eta (float) – Boosting learning rate. Defaults to 0.1.

  • max_depth (int) – Maximum tree depth for base learners. Defaults to 6.

  • min_child_weight (float) – Minimum sum of instance weight (hessian) needed in a child. Defaults to 1.0

  • n_estimators (int) – Number of gradient boosted trees. Equivalent to number of boosting rounds. Defaults to 100.

  • random_seed (int) – Seed for the random number generator. Defaults to 0.

  • n_jobs (int) – Number of parallel threads used to run xgboost. Note that creating thread contention will significantly slow down the algorithm. Defaults to 12.

Attributes

hyperparameter_ranges

{ “eta”: Real(0.000001, 1), “max_depth”: Integer(1, 20), “min_child_weight”: Real(1, 10), “n_estimators”: Integer(1, 1000),}

model_family

ModelFamily.XGBOOST

modifies_features

True

modifies_target

False

name

XGBoost Regressor

SEED_MAX

None

SEED_MIN

None

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

Feature importance of fitted XGBoost regressor.

fit

Fits XGBoost regressor component to data.

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 fitted XGBoost regressor.

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)

Feature importance of fitted XGBoost regressor.

fit(self, X, y=None)[source]

Fits XGBoost regressor component 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

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)[source]

Make predictions using fitted XGBoost regressor.

Parameters

X (pd.DataFrame) – 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) – 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.