boosters.sklearn.GBLinearClassifier

class boosters.sklearn.GBLinearClassifier

Bases: _GBLinearEstimatorBase, ClassifierMixin

Gradient Boosted Linear Classifier.

A sklearn-compatible wrapper around GBLinearModel for classification.

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.

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

• 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 classification objective. If None, auto-detects: Objective.logistic() for binary, Objective.softmax() for multiclass.

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

• Attributes

• ----------

• model (GBLinearModel) – The fitted core model.

• classes (ndarray) – Unique class labels.

• coef (ndarray) – Coefficient weights.

• intercept (ndarray) – Intercept terms.

classes_: ndarray[tuple[Any, ...], dtype[Any]]

n_classes_: int

predict(X)

Predict class labels.

Parameters:

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

• Returns

• -------

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

Return type:

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

predict_proba(X)

Predict class probabilities.

Parameters:

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

• Returns

• -------

• proba (ndarray of shape (n_samples, n_classes)) – Class probability estimates.

Return type:

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

__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:

• n_estimators (int)

• learning_rate (float)

• l1 (float)

• l2 (float)

• early_stopping_rounds (int | None)

• seed (int)

• n_threads (int)

• verbose (int)

• objective (Objective | None)

• metric (Metric | None)

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.

score(X, y, sample_weight=None)

Return accuracy on provided data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

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

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

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

Returns:

score – Mean accuracy of self.predict(X) w.r.t. y.

Return type:

float

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 (GBLinearClassifier)

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 (GBLinearClassifier)

Returns:

self – The updated object.

Return type:

object

model_: GBLinearModel

n_features_in_: int