modeva.models.MoGLMTreeBoostClassifier#

class modeva.models.MoGLMTreeBoostClassifier(name: str = None, n_estimators=100, learning_rate=1.0, n_epoch_no_change=5, max_depth=1, min_samples_leaf=50, min_impurity_decrease=0, split_custom=None, n_screen_grid=1, n_feature_search=5, n_split_grid=20, reg_lambda=0.1, clip_predict=False, simplified=True, verbose=False, val_ratio=0.2, random_state=0)#

GLMTree Boosting classifier using residual-based boosting.

A gradient boosting classifier that uses GLMTree base models to iteratively fit residuals. Each tree has both linear and non-linear components to capture complex relationships while maintaining interpretability.

Parameters:

  • name (str, default=None) – Model identifier name for reference.

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

  • max_depth (int, default=1) – Maximum tree depth. Model is most interpretable when depth=1.

  • learning_rate (float, default=1.0) – Shrinkage rate applied to each tree’s contribution.

  • n_epoch_no_change (int, default=5) – Early stopping rounds - training stops if validation loss doesn’t improve.

  • min_samples_leaf (int, default=50) – Minimum samples required in a leaf node.

  • min_impurity_decrease (float, default=0) – Minimum required decrease in impurity to split a node.

  • split_custom (dict, default=None) – Custom split points specified per feature.

  • n_screen_grid (int, default=1) – Grid size for initial split point screening.

  • n_feature_search (int, default=5) – Number of features to consider after screening.

  • n_split_grid (int, default=20) – Grid size for fine-grained split point search.

  • reg_lambda (float, default=0.1) – L1 regularization strength parameter.

  • clip_predict (bool, default=False) – Whether to clip predictions to training data range.

  • simplified (bool, default=True) – Use simplified partial linear regression for splits.

  • val_ratio (float, default=0.2) – Proportion of data used for validation.

  • verbose (bool, default=False) – Whether to print training progress.

  • random_state (int, default=0) – Random seed for reproducibility.

estimators_#

Fitted GLMTree models.

Type:

list

n_features_in_#

Number of input features.

Type:

int

calibrate_interval(X, y, alpha=0.1)#

Fit a conformal prediction model to the given data.

This method computes the model’s prediction interval calibrated to the given data.

It computes the calibration quantile based on predicted probabilities for the positive class.

Parameters:

  • X (X : np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.

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

  • alpha (float, default=0.1) – Expected miscoverage for the conformal prediction.

Raises:

ValueError – If the model is neither a regressor nor a classifier.:

calibrate_proba(X, y, sample_weight=None, method='sigmoid')#

Fit the calibration method on the model’s predictions.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.

  • y (np.ndarray of shape (n_samples, )) – Ground truth labels.

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

  • method ({'sigmoid', 'isotonic'}, default='sigmoid') –

    The calibration method.

    • ’sigmoid’: Platt’s method, i.e., fit a logistic regression on predicted probabilities and y

    • ’isotonic’: Fit an isotonic regression on predicted probabilities and y.

Returns:

self

Return type:

Calibrated estimator

decision_function(X, calibration: bool = True)#

Computes the decision function for the given input data.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.

  • calibration (bool, default=True) – If True, will use calibrated probability if calibration is done. Otherwise, will use raw probability.

Returns:

logit_prediction – Array of (calibrated) logit predictions.

Return type:

array, shape (n_samples,) or (n_samples, n_classes)

fit(X, y, sample_weight=None)#

fit the GLMTree Boosting model

Parameters:

  • X (array-like of shape (n_samples, n_features)) – containing the input dataset

  • y (array-like of shape (n_samples,)) – containing target values

  • sample_weight (array-like of shape (n_samples,)) – containing the weight of each sample

Returns:

self : Estimator instance.

Return type:

object

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

load(file_name: str)#

Load the model into memory from file system.

Parameters:

file_name (str) – The path and name of the file.

Return type:

estimator object

predict(X, calibration: bool = True)#

Model predictions, calling the child class’s ‘_predict’ method.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.

  • calibration (bool, default=True) – If True, will use calibrated probability if calibration is done. Otherwise, will use raw probability.

Returns:

np.ndarray

Return type:

The (calibrated) final prediction

predict_interval(X)#

Predict the prediction set for the given data based on the conformal prediction model.

This method computes the model prediction interval (regression) or prediction sets (classification) using conformal prediction.

Parameters:

X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.

Returns:

np.ndarray – in the format [n_samples, 2] for regressors or a flattened array for classifiers.

Return type:

The lower and upper bounds of the prediction intervals for each sample

Raises:

ValueError – If fit_conformal has not been called to fit the conformal prediction model: before calling this method.

predict_proba(X, calibration: bool = True)#

Predict (calibrated) probabilities for X.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – Feature matrix for prediction.

  • calibration (bool, default=True) – If True, will return calibrated probability if calibration is done. Otherwise, will return raw probability.

Returns:

np.ndarray

Return type:

The (calibrated) predicted probabilities

save(file_name: str)#

Save the model into file system.

Parameters:

file_name (str) – The path and name of the file.

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