class mars.learn.contrib.lightgbm.LGBMClassifier(*args, **kwargs)[source]
__init__(*args, **kwargs)

Construct a gradient boosting model.

  • boosting_type (str, optional (default='gbdt')) – ‘gbdt’, traditional Gradient Boosting Decision Tree. ‘dart’, Dropouts meet Multiple Additive Regression Trees. ‘goss’, Gradient-based One-Side Sampling. ‘rf’, Random Forest.

  • num_leaves (int, optional (default=31)) – Maximum tree leaves for base learners.

  • max_depth (int, optional (default=-1)) – Maximum tree depth for base learners, <=0 means no limit.

  • learning_rate (float, optional (default=0.1)) – Boosting learning rate. You can use callbacks parameter of fit method to shrink/adapt learning rate in training using reset_parameter callback. Note, that this will ignore the learning_rate argument in training.

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

  • subsample_for_bin (int, optional (default=200000)) – Number of samples for constructing bins.

  • objective (str, callable or None, optional (default=None)) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below). Default: ‘regression’ for LGBMRegressor, ‘binary’ or ‘multiclass’ for LGBMClassifier, ‘lambdarank’ for LGBMRanker.

  • class_weight (dict, 'balanced' or None, optional (default=None)) – Weights associated with classes in the form {class_label: weight}. Use this parameter only for multi-class classification task; for binary classification task you may use is_unbalance or scale_pos_weight parameters. Note, that the usage of all these parameters will result in poor estimates of the individual class probabilities. You may want to consider performing probability calibration ( of your model. The ‘balanced’ mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)). If None, all classes are supposed to have weight one. Note, that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.

  • min_split_gain (float, optional (default=0.)) – Minimum loss reduction required to make a further partition on a leaf node of the tree.

  • min_child_weight (float, optional (default=1e-3)) – Minimum sum of instance weight (hessian) needed in a child (leaf).

  • min_child_samples (int, optional (default=20)) – Minimum number of data needed in a child (leaf).

  • subsample (float, optional (default=1.)) – Subsample ratio of the training instance.

  • subsample_freq (int, optional (default=0)) – Frequency of subsample, <=0 means no enable.

  • colsample_bytree (float, optional (default=1.)) – Subsample ratio of columns when constructing each tree.

  • reg_alpha (float, optional (default=0.)) – L1 regularization term on weights.

  • reg_lambda (float, optional (default=0.)) – L2 regularization term on weights.

  • random_state (int, RandomState object or None, optional (default=None)) – Random number seed. If int, this number is used to seed the C++ code. If RandomState object (numpy), a random integer is picked based on its state to seed the C++ code. If None, default seeds in C++ code are used.

  • n_jobs (int, optional (default=-1)) – Number of parallel threads.

  • silent (bool, optional (default=True)) – Whether to print messages while running boosting.

  • importance_type (str, optional (default='split')) – The type of feature importance to be filled into feature_importances_. If ‘split’, result contains numbers of times the feature is used in a model. If ‘gain’, result contains total gains of splits which use the feature.

  • **kwargs

    Other parameters for the model. Check for more parameters.


    **kwargs is not supported in sklearn, it may cause unexpected issues.


A custom objective function can be provided for the objective parameter. In this case, it should have the signature objective(y_true, y_pred) -> grad, hess or objective(y_true, y_pred, group) -> grad, hess:

y_truearray-like of shape = [n_samples]

The target values.

y_predarray-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)

The predicted values. Predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task.


Group/query data. Only used in the learning-to-rank task. sum(group) = n_samples. For example, if you have a 100-document dataset with group = [10, 20, 40, 10, 10, 10], that means that you have 6 groups, where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.

gradarray-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)

The value of the first order derivative (gradient) of the loss with respect to the elements of y_pred for each sample point.

hessarray-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)

The value of the second order derivative (Hessian) of the loss with respect to the elements of y_pred for each sample point.

For multi-class task, the y_pred is group by class_id first, then group by row_id. If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i] and you should group grad and hess in this way as well.


__init__(*args, **kwargs)

Construct a gradient boosting model.

fit(X, y[, sample_weight, init_score, ...])

Build a gradient boosting model from the training set (X, y).


Get parameters for this estimator.


predict(X, **kwargs)

Return the predicted value for each sample.

predict_proba(X, **kwargs)

Return the predicted probability for each class for each sample.

score(X, y[, sample_weight])

Return the mean accuracy on the given test data and labels.


Set the parameters of this estimator.




The best iteration of fitted model if early_stopping() callback has been specified.


The best score of fitted model.


The underlying Booster of this model.


The class label array.


The evaluation results if validation sets have been specified.


The feature importances (the higher, the more important).


The names of features.


The number of classes.


The number of features of fitted model.


The number of features of fitted model.


The concrete objective used while fitting this model.