boosters.sklearn.GBLinearRegressor#

class boosters.sklearn.GBLinearRegressor[source]#

Bases: _GBLinearEstimatorBase, RegressorMixin

Gradient Boosted Linear Regressor.

A sklearn-compatible wrapper around GBLinearModel for linear regression.

Parameters:

  • n_estimators (int, default=100) – Number of boosting rounds.

  • learning_rate (float, default=0.5) – Step size for weight updates.

  • l1 (float, default=0.0) – L1 regularization (alpha).

  • l2 (float, default=1.0) – L2 regularization (lambda).

  • early_stopping_rounds (int or None, default=None) – Stop if no improvement for this many rounds.

  • seed (int, default=42) – Random seed.

  • objective (Objective or None, default=None) – Loss function. Must be a regression objective. If None, uses Objective.squared().

  • metric (Metric or None, default=None) – Evaluation metric. If None, uses Metric.rmse().

  • Attributes

  • ----------

  • model (GBLinearModel) – The fitted core model.

  • coef (ndarray of shape (n_features,)) – Coefficient weights.

  • intercept (ndarray of shape (1,)) – Intercept (bias) term.

  • n_features_in (int) – Number of features seen during fit.

__init__(n_estimators=100, learning_rate=0.5, l1=0.0, l2=1.0, early_stopping_rounds=None, seed=42, n_threads=0, verbose=1, objective=None, metric=None)#
Parameters:
Return type:

None

property coef_: ndarray[tuple[Any, ...], dtype[float32]]#

Coefficient weights.

fit(X, y, eval_set=None, sample_weight=None)#

Fit the estimator.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Training input samples.

  • y (array-like of shape (n_samples,)) – Target values.

  • eval_set (tuple of (X, y), optional) – Validation set as (X_val, y_val) tuple.

  • sample_weight (array-like of shape (n_samples,), optional) – Sample weights.

  • Returns

  • -------

  • self – Fitted estimator.

Return type:

Self

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

property intercept_: ndarray[tuple[Any, ...], dtype[float32]]#

Intercept (bias) term.

predict(X)#

Predict using the fitted model.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Input samples.

  • Returns

  • -------

  • y_pred (ndarray of shape (n_samples,)) – Predicted values.

Return type:

ndarray[tuple[Any, ...], dtype[float32]]

score(X, y, sample_weight=None)#

Return coefficient of determination on test data.

The coefficient of determination, \(R^2\), is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:

  • X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

  • sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – \(R^2\) of self.predict(X) w.r.t. y.

Return type:

float

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_fit_request(*, eval_set='$UNCHANGED$', sample_weight='$UNCHANGED$')#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

  • eval_set (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for eval_set parameter in fit.

  • sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

  • self (GBLinearRegressor)

Returns:

self – The updated object.

Return type:

object

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

set_score_request(*, sample_weight='$UNCHANGED$')#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

  • sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

  • self (GBLinearRegressor)

Returns:

self – The updated object.

Return type:

object

model_: GBLinearModel#
n_features_in_: int#