class xorbits.xgboost.XGBClassifier(*args, **kwargs)[source]#

Implementation of the scikit-learn API for XGBoost classification. See /python/sklearn_estimator for more information.

  • n_estimators (Optional[int]) – Number of boosting rounds.

  • max_depth (Optional[int]) – Maximum tree depth for base learners.

  • max_leaves – Maximum number of leaves; 0 indicates no limit.

  • max_bin – If using histogram-based algorithm, maximum number of bins per feature

  • grow_policy – Tree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change.

  • learning_rate (Optional[float]) – Boosting learning rate (xgb’s “eta”)

  • verbosity (Optional[int]) – The degree of verbosity. Valid values are 0 (silent) - 3 (debug).

  • objective (Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]], NoneType] (Not supported yet)) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).

  • booster (Optional[str]) – Specify which booster to use: gbtree, gblinear or dart.

  • tree_method (Optional[str]) – Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It’s recommended to study this option from the parameters document tree method

  • n_jobs (Optional[int]) – Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.

  • gamma (Optional[float]) – (min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree.

  • min_child_weight (Optional[float]) – Minimum sum of instance weight(hessian) needed in a child.

  • max_delta_step (Optional[float]) – Maximum delta step we allow each tree’s weight estimation to be.

  • subsample (Optional[float]) – Subsample ratio of the training instance.

  • sampling_method

    Sampling method. Used only by the GPU version of hist tree method.
    • uniform: select random training instances uniformly.

    • gradient_based select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)

  • colsample_bytree (Optional[float]) – Subsample ratio of columns when constructing each tree.

  • colsample_bylevel (Optional[float]) – Subsample ratio of columns for each level.

  • colsample_bynode (Optional[float]) – Subsample ratio of columns for each split.

  • reg_alpha (Optional[float]) – L1 regularization term on weights (xgb’s alpha).

  • reg_lambda (Optional[float]) – L2 regularization term on weights (xgb’s lambda).

  • scale_pos_weight (Optional[float]) – Balancing of positive and negative weights.

  • base_score (Optional[float]) – The initial prediction score of all instances, global bias.

  • random_state (Optional[Union[numpy.random.RandomState, int]]) –

    Random number seed.


    Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.

  • missing (float, default np.nan) – Value in the data which needs to be present as a missing value.

  • num_parallel_tree (Optional[int]) – Used for boosting random forest.

  • monotone_constraints (Optional[Union[Dict[str, int], str]]) – Constraint of variable monotonicity. See tutorial for more information.

  • interaction_constraints (Optional[Union[str, List[Tuple[str]]]]) – Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g. [[0, 1], [2, 3, 4]], where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more information

  • importance_type (Optional[str]) –

    The feature importance type for the feature_importances_ property:

    • For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.

    • For linear model, only “weight” is defined and it’s the normalized coefficients without bias.

  • device (Optional[str]) –

    New in version 2.0.0(xgboost).

    Device ordinal, available options are cpu, cuda, and gpu.

  • validate_parameters (Optional[bool]) – Give warnings for unknown parameter.

  • enable_categorical (bool) –

    New in version 1.5.0(xgboost).


    This parameter is experimental

    Experimental support for categorical data. When enabled, cudf/pandas.DataFrame should be used to specify categorical data type. Also, JSON/UBJSON serialization format is required.

  • feature_types (Optional[FeatureTypes]) –

    New in version 1.7.0(xgboost).

    Used for specifying feature types without constructing a dataframe. See DMatrix for details.

  • max_cat_to_onehot (Optional[int]) –

    New in version 1.6.0(xgboost).


    This parameter is experimental

    A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Also, enable_categorical needs to be set to have categorical feature support. See Categorical Data and cat-param for details.

  • max_cat_threshold (Optional[int]) –

    New in version 1.7.0(xgboost).


    This parameter is experimental

    Maximum number of categories considered for each split. Used only by partition-based splits for preventing over-fitting. Also, enable_categorical needs to be set to have categorical feature support. See Categorical Data and cat-param for details.

  • multi_strategy (Optional[str]) –

    New in version 2.0.0(xgboost).


    This parameter is working-in-progress.

    The strategy used for training multi-target models, including multi-target regression and multi-class classification. See /tutorials/multioutput for more information.

    • one_output_per_tree: One model for each target.

    • multi_output_tree: Use multi-target trees.

  • eval_metric (Optional[Union[str, List[str], Callable]]) –

    New in version 1.6.0(xgboost).

    Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in sklearn.metrics, or any other user defined metric that looks like sklearn.metrics.

    If custom objective is also provided, then custom metric should implement the corresponding reverse link function.

    Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.

    For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see xgboost.callback.EarlyStopping.

    See Custom Objective and Evaluation Metric for more.


    This parameter replaces eval_metric in fit() method. The old one receives un-transformed prediction regardless of whether custom objective is being used.

    from sklearn.datasets import load_diabetes
    from sklearn.metrics import mean_absolute_error
    X, y = load_diabetes(return_X_y=True)
    reg = xgb.XGBRegressor(
    reg.fit(X, y, eval_set=[(X, y)])

  • early_stopping_rounds (Optional[int]) –

    New in version 1.6.0(xgboost).

    • Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training. Requires at least one item in eval_set in fit().

    • If early stopping occurs, the model will have two additional attributes: best_score and best_iteration. These are used by the predict() and apply() methods to determine the optimal number of trees during inference. If users want to access the full model (including trees built after early stopping), they can specify the iteration_range in these inference methods. In addition, other utilities like model plotting can also use the entire model.

    • If you prefer to discard the trees after best_iteration, consider using the callback function xgboost.callback.EarlyStopping.

    • If there’s more than one item in eval_set, the last entry will be used for early stopping. If there’s more than one metric in eval_metric, the last metric will be used for early stopping.


    This parameter replaces early_stopping_rounds in fit() method.

  • callbacks (Optional[List[TrainingCallback]]) –

    List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API.


    States in callback are not preserved during training, which means callback objects can not be reused for multiple training sessions without reinitialization or deepcopy.

    for params in parameters_grid:
        # be sure to (re)initialize the callbacks before each run
        callbacks = [xgb.callback.LearningRateScheduler(custom_rates)]
        reg = xgboost.XGBRegressor(**params, callbacks=callbacks)
        reg.fit(X, y)

  • kwargs (dict, optional) –

    Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError.


    **kwargs unsupported by scikit-learn

    **kwargs is unsupported by scikit-learn. We do not guarantee that parameters passed via this argument will interact properly with scikit-learn.


    Custom objective function

    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:

    y_true: array_like of shape [n_samples]

    The target values

    y_pred: array_like of shape [n_samples]

    The predicted values

    grad: array_like of shape [n_samples]

    The value of the gradient for each sample point.

    hess: array_like of shape [n_samples]

    The value of the second derivative for each sample point

This docstring was copied from xgboost.

__init__(*args, **kwargs)#


__init__(*args, **kwargs)

apply(X[, iteration_range])

Return the predicted leaf every tree for each sample.


Return the evaluation results.

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

Fit gradient boosting classifier.


Get the underlying xgboost Booster of this model.


Get metadata routing of this object.


Gets the number of xgboost boosting rounds.


Get parameters.


Get xgboost specific parameters.


predict(data, **kw)

predict_proba(data[, ntree_limit])

Predict the probability of each X example being of a given class.


Save the model to a file.

score(X, y[, sample_weight])

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

set_fit_request(*[, base_margin, ...])

Request metadata passed to the fit method.


Set the parameters of this estimator.

set_predict_proba_request(*[, data, ntree_limit])

Request metadata passed to the predict_proba method.

set_predict_request(*[, data])

Request metadata passed to the predict method.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.