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

Return the predicted probability for each class for each sample.

  • 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.


    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.


  • 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.