xorbits.lightgbm.LGBMClassifier.predict_proba#

LGBMClassifier.predict_proba(X, **kwargs)[source]#

Return the predicted probability for each class for each sample.

Parameters

  • X (numpy array, pandas DataFrame, H2O DataTable's Frame , scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]) – Input features matrix.

  • raw_score (bool, optional (default=False) (Not supported yet)) – Whether to predict raw scores.

  • start_iteration (int, optional (default=0) (Not supported yet)) – Start index of the iteration to predict. If <= 0, starts from the first iteration.

  • num_iteration (int or None, optional (default=None) (Not supported yet)) – Total number of iterations used in the prediction. If None, if the best iteration exists and start_iteration <= 0, the best iteration is used; otherwise, all iterations from start_iteration are used (no limits). If <= 0, all iterations from start_iteration are used (no limits).

  • pred_leaf (bool, optional (default=False) (Not supported yet)) – Whether to predict leaf index.

  • pred_contrib (bool, optional (default=False) (Not supported yet)) –

    Whether to predict feature contributions.

    Note

    If you want to get more explanations for your model’s predictions using SHAP values, like SHAP interaction values, you can install the shap package (slundberg/shap). Note that unlike the shap package, with pred_contrib we return a matrix with an extra column, where the last column is the expected value.

  • validate_features (bool, optional (default=False) (Not supported yet)) – If True, ensure that the features used to predict match the ones used to train. Used only if data is pandas DataFrame.

  • **kwargs – Other parameters for the prediction.

Returns

  • predicted_probability (array-like of shape = [n_samples] or shape = [n_samples, n_classes]) – The predicted values.

  • X_leaves (array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]) – If pred_leaf=True, the predicted leaf of every tree for each sample.

  • X_SHAP_values (array-like of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or list with n_classes length of such objects) – If pred_contrib=True, the feature contributions for each sample.

This docstring was copied from lightgbm.sklearn.LGBMClassifier.