mars.learn.contrib.xgboost.XGBClassifier¶
- class mars.learn.contrib.xgboost.XGBClassifier(max_depth=None, learning_rate=None, n_estimators=100, verbosity=None, objective=None, booster=None, tree_method=None, n_jobs=None, gamma=None, min_child_weight=None, max_delta_step=None, subsample=None, colsample_bytree=None, colsample_bylevel=None, colsample_bynode=None, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, base_score=None, random_state=None, missing=nan, num_parallel_tree=None, monotone_constraints=None, interaction_constraints=None, importance_type='gain', gpu_id=None, validate_parameters=None, **kwargs)[source]¶
Implementation of the scikit-learn API for XGBoost classification.
- __init__(max_depth=None, learning_rate=None, n_estimators=100, verbosity=None, objective=None, booster=None, tree_method=None, n_jobs=None, gamma=None, min_child_weight=None, max_delta_step=None, subsample=None, colsample_bytree=None, colsample_bylevel=None, colsample_bynode=None, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, base_score=None, random_state=None, missing=nan, num_parallel_tree=None, monotone_constraints=None, interaction_constraints=None, importance_type='gain', gpu_id=None, validate_parameters=None, **kwargs)[source]¶
Methods
__init__
([max_depth, learning_rate, …])apply
(X[, ntree_limit, iteration_range])Return the predicted leaf every tree for each sample.
evals_result
()Return the evaluation results.
fit
(X, y[, sample_weights, eval_set, …])Fit the regressor. :param X: Feature matrix :type X: array_like :param y: Labels :type y: array_like :param sample_weight: instance weights :type sample_weight: array_like :param eval_set: A list of (X, y) tuple pairs to use as validation sets, for which metrics will be computed. Validation metrics will help us track the performance of the model. :type eval_set: list, optional :param sample_weight_eval_set: A list of the form [L_1, L_2, …, L_n], where each L_i is a list of group weights on the i-th validation set. :type sample_weight_eval_set: list, optional.
get_booster
()Get the underlying xgboost Booster of this model.
get_num_boosting_rounds
()Gets the number of xgboost boosting rounds.
get_params
([deep])Get parameters.
get_xgb_params
()Get xgboost specific parameters.
load_model
(fname)Load the model from a file.
predict
(data, **kw)Predict with data.
predict_proba
(data[, ntree_limit])save_model
(fname)Save the model to a file.
score
(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(**params)Set the parameters of this estimator.
Attributes
best_iteration
best_ntree_limit
best_score
coef_
Coefficients property
feature_importances_
Feature importances property
intercept_
Intercept (bias) property
n_features_in_