# lightgbm.LGBMRegressor

class lightgbm.LGBMRegressor(boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, subsample_for_bin=200000, objective=None, class_weight=None, min_split_gain=0.0, min_child_weight=0.001, min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0, reg_lambda=0.0, random_state=None, n_jobs=None, importance_type='split', **kwargs)[source]

Bases: RegressorMixin, LGBMModel

LightGBM regressor.

__init__(boosting_type='gbdt', num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=100, subsample_for_bin=200000, objective=None, class_weight=None, min_split_gain=0.0, min_child_weight=0.001, min_child_samples=20, subsample=1.0, subsample_freq=0, colsample_bytree=1.0, reg_alpha=0.0, reg_lambda=0.0, random_state=None, n_jobs=None, importance_type='split', **kwargs)

Parameters
• 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 (https://scikit-learn.org/stable/modules/calibration.html) 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 or None, optional (default=None)) –

Number of parallel threads to use for training (can be changed at prediction time by passing it as an extra keyword argument).

For better performance, it is recommended to set this to the number of physical cores in the CPU.

Negative integers are interpreted as following joblib’s formula (n_cpus + 1 + n_jobs), just like scikit-learn (so e.g. -1 means using all threads). A value of zero corresponds the default number of threads configured for OpenMP in the system. A value of None (the default) corresponds to using the number of physical cores in the system (its correct detection requires either the joblib or the psutil util libraries to be installed).

• 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 http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters.

Warning

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

Note

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, objective(y_true, y_pred, weight) -> grad, hess or objective(y_true, y_pred, weight, group) -> grad, hess:

y_truenumpy 1-D array of shape = [n_samples]

The target values.

y_prednumpy 1-D array of shape = [n_samples] or numpy 2-D array of 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.

weightnumpy 1-D array of shape = [n_samples]

The weight of samples. Weights should be non-negative.

groupnumpy 1-D array

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.

gradnumpy 1-D array of shape = [n_samples] or numpy 2-D array of 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.

hessnumpy 1-D array of shape = [n_samples] or numpy 2-D array of 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, y_pred is a numpy 2-D array of shape = [n_samples, n_classes], and grad and hess should be returned in the same format.

Methods

 __init__([boosting_type, num_leaves, ...]) Construct a gradient boosting model. fit(X, y[, sample_weight, init_score, ...]) Build a gradient boosting model from the training set (X, y). get_params([deep]) Get parameters for this estimator. predict(X[, raw_score, start_iteration, ...]) Return the predicted value for each sample. score(X, y[, sample_weight]) Return the coefficient of determination of the prediction. set_params(**params) Set the parameters of this estimator.

Attributes

 best_iteration_ The best iteration of fitted model if early_stopping() callback has been specified. best_score_ The best score of fitted model. booster_ The underlying Booster of this model. evals_result_ The evaluation results if validation sets have been specified. feature_importances_ The feature importances (the higher, the more important). feature_name_ The names of features. n_estimators_ True number of boosting iterations performed. n_features_ The number of features of fitted model. n_features_in_ The number of features of fitted model. n_iter_ True number of boosting iterations performed. objective_ The concrete objective used while fitting this model.
property best_iteration_

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

Type

int

property best_score_

The best score of fitted model.

Type

dict

property booster_

The underlying Booster of this model.

Type

Booster

property evals_result_

The evaluation results if validation sets have been specified.

Type

dict

property feature_importances_

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

Note

importance_type attribute is passed to the function to configure the type of importance values to be extracted.

Type

array of shape = [n_features]

property feature_name_

The names of features.

Type

array of shape = [n_features]

fit(X, y, sample_weight=None, init_score=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_init_score=None, eval_metric=None, feature_name='auto', categorical_feature='auto', callbacks=None, init_model=None)[source]

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

Parameters
• X (array-like or sparse matrix of shape = [n_samples, n_features]) – Input feature matrix.

• y (array-like of shape = [n_samples]) – The target values (class labels in classification, real numbers in regression).

• sample_weight (array-like of shape = [n_samples] or None, optional (default=None)) – Weights of training data. Weights should be non-negative.

• init_score (array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task) or shape = [n_samples, n_classes] (for multi-class task) or None, optional (default=None)) – Init score of training data.

• eval_set (list or None, optional (default=None)) – A list of (X, y) tuple pairs to use as validation sets.

• eval_names (list of str, or None, optional (default=None)) – Names of eval_set.

• eval_sample_weight (list of array, or None, optional (default=None)) – Weights of eval data. Weights should be non-negative.

• eval_init_score (list of array, or None, optional (default=None)) – Init score of eval data.

• eval_metric (str, callable, list or None, optional (default=None)) – If str, it should be a built-in evaluation metric to use. If callable, it should be a custom evaluation metric, see note below for more details. If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both. In either case, the metric from the model parameters will be evaluated and used as well. Default: ‘l2’ for LGBMRegressor, ‘logloss’ for LGBMClassifier, ‘ndcg’ for LGBMRanker.

• feature_name (list of str, or 'auto', optional (default='auto')) – Feature names. If ‘auto’ and data is pandas DataFrame, data columns names are used.

• categorical_feature (list of str or int, or 'auto', optional (default='auto')) – Categorical features. If list of int, interpreted as indices. If list of str, interpreted as feature names (need to specify feature_name as well). If ‘auto’ and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647). Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values. The output cannot be monotonically constrained with respect to a categorical feature. Floating point numbers in categorical features will be rounded towards 0.

• callbacks (list of callable, or None, optional (default=None)) – List of callback functions that are applied at each iteration. See Callbacks in Python API for more information.

• init_model (str, pathlib.Path, Booster, LGBMModel or None, optional (default=None)) – Filename of LightGBM model, Booster instance or LGBMModel instance used for continue training.

Returns

self – Returns self.

Return type

LGBMRegressor

Note

Custom eval function expects a callable with following signatures: func(y_true, y_pred), func(y_true, y_pred, weight) or func(y_true, y_pred, weight, group) and returns (eval_name, eval_result, is_higher_better) or list of (eval_name, eval_result, is_higher_better):

y_truenumpy 1-D array of shape = [n_samples]

The target values.

y_prednumpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)

The predicted values. In case of custom objective, predicted values are returned before any transformation, e.g. they are raw margin instead of probability of positive class for binary task in this case.

weightnumpy 1-D array of shape = [n_samples]

The weight of samples. Weights should be non-negative.

groupnumpy 1-D array

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.

eval_namestr

The name of evaluation function (without whitespace).

eval_resultfloat

The eval result.

is_higher_betterbool

Is eval result higher better, e.g. AUC is is_higher_better.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, optional (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

property n_estimators_

True number of boosting iterations performed.

This might be less than parameter n_estimators if early stopping was enabled or if boosting stopped early due to limits on complexity like min_gain_to_split.

Type

int

property n_features_

The number of features of fitted model.

Type

int

property n_features_in_

The number of features of fitted model.

Type

int

property n_iter_

True number of boosting iterations performed.

This might be less than parameter n_estimators if early stopping was enabled or if boosting stopped early due to limits on complexity like min_gain_to_split.

Type

int

property objective_

The concrete objective used while fitting this model.

Type

str or callable

predict(X, raw_score=False, start_iteration=0, num_iteration=None, pred_leaf=False, pred_contrib=False, validate_features=False, **kwargs)

Return the predicted value for each sample.

Parameters
• X (array-like or sparse matrix of shape = [n_samples, n_features]) – Input features matrix.

• raw_score (bool, optional (default=False)) – Whether to predict raw scores.

• start_iteration (int, optional (default=0)) – Start index of the iteration to predict. If <= 0, starts from the first iteration.

• num_iteration (int or None, optional (default=None)) – 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)) – Whether to predict leaf index.

• pred_contrib (bool, optional (default=False)) –

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 (https://github.com/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)) – 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_result (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.

score(X, y, sample_weight=None)

Return the coefficient of determination of the prediction.

The coefficient of determination $$R^2$$ is defined as $$(1 - \frac{u}{v})$$, where $$u$$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $$v$$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $$R^2$$ score of 0.0.

Parameters
• X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

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

Returns

score$$R^2$$ of self.predict(X) wrt. y.

Return type

float

Notes

The $$R^2$$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**params)

Set the parameters of this estimator.

Parameters

**params – Parameter names with their new values.

Returns

self – Returns self.

Return type

object