boosters.GBLinearModel#

class boosters.GBLinearModel#

Bases: object

Gradient Boosted Linear model.

GBLinear uses gradient boosting to train a linear model via coordinate descent. Simpler than GBDT but can be effective for linear relationships.

coef_#: Model coefficients after fitting.

intercept_#: Model intercept after fitting.

is_fitted#: Whether the model has been trained.

n_features_in_#: Number of features seen during fit.

Examples

>>> config = GBLinearConfig(n_estimators=50, learning_rate=0.3)
>>> model = GBLinearModel(config=config).fit(train)
>>> predictions = model.predict(X_test)

coef_#

Model coefficients (weights).

Returns:: Array with shape (n_features,) for single-output models or (n_features, n_outputs) for multi-output models.

feature_names#: Feature names from training dataset (if provided).

static from_bytes(data)#

Load model from binary bytes.

Parameters:: data – Binary bytes in .bstr format.
Returns:: Loaded GBLinearModel instance.
Raises:: ValueError – If bytes are invalid or corrupted.

static from_json_bytes(data)#

Load model from JSON bytes.

Parameters:: data – UTF-8 JSON bytes in .bstr.json format.
Returns:: Loaded GBLinearModel instance.
Raises:: ValueError – If JSON is invalid.

intercept_#

Model intercept (bias).

Returns:: Array of shape (n_outputs,).

n_features_in_#: Number of features the model was trained on.

predict(data, n_threads=0)#

Make predictions on features.

Returns transformed predictions (e.g., probabilities for classification). Output shape is (n_samples, n_outputs) - sklearn convention.

Parameters:

data – Dataset containing features.
n_threads – Number of threads (unused, for API consistency).

Returns:

Predictions array with shape (n_samples, n_outputs).

predict_raw(data, n_threads=0)#

Make raw (untransformed) predictions on features.

Returns raw margin scores without transformation. Output shape is (n_samples, n_outputs) - sklearn convention.

Parameters:

data – Dataset containing features.
n_threads – Number of threads (unused, for API consistency).

Returns:

Raw scores array with shape (n_samples, n_outputs).

to_bytes()#

Serialize model to binary bytes.

Returns:: Binary representation of the model (.bstr format).
Raises:: RuntimeError – If model is not fitted or serialization fails.

to_json_bytes()#

Serialize model to JSON bytes.

Returns:: UTF-8 JSON representation of the model (.bstr.json format).
Raises:: RuntimeError – If model is not fitted or serialization fails.

static train(train, config=None, val_set=None, n_threads=0)#

Train a new GBLinear model.

This matches the Rust API style: training is a class-level constructor.

Parameters:

train – Training dataset containing features and labels.
config – Optional GBLinearConfig. If not provided, uses default config.
val_set – Optional validation dataset for early stopping and evaluation.
n_threads – Number of threads for parallel training (0 = auto).

Returns:

Trained GBLinearModel.