dask_ml.xgboost.XGBClassifier

`dask_ml.xgboost`.XGBClassifier¶

class dask_ml.xgboost.XGBClassifier(*, objective: Optional[Union[str, xgboost.sklearn._SklObjWProto, Callable[[Any, Any], Tuple[numpy.ndarray, numpy.ndarray]]]] = 'binary:logistic', **kwargs: Any)¶

Attributes

best_iteration: The best iteration obtained by early stopping.
best_score: The best score obtained by early stopping.
classes_
coef_: Coefficients property
feature_importances_: Feature importances property, return depends on importance_type parameter.
feature_names_in_: Names of features seen during fit().
intercept_: Intercept (bias) property
n_features_in_: Number of features seen during fit().

Methods

`apply`(X[, iteration_range])	Return the predicted leaf every tree for each sample.
`evals_result`()	Return the evaluation results.
`fit`(X[, y, classes, eval_set, ...])	Fit a gradient boosting classifier
`get_booster`()	Get the underlying xgboost Booster of this model.
`get_metadata_routing`()	Get metadata routing of this object.
`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 or a bytearray.
`predict`(X)	Predict with X.
`predict_proba`(data[, ntree_limit])	Predict the probability of each X example being of a given class.
`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_fit_request`(*[, classes, ...])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_proba_request`(*[, data, ntree_limit])	Request metadata passed to the `predict_proba` method.
`set_predict_request`(*[, base_margin, ...])	Request metadata passed to the `predict` method.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

__init__(*, objective: Optional[Union[str, xgboost.sklearn._SklObjWProto, Callable[[Any, Any], Tuple[numpy.ndarray, numpy.ndarray]]]] = 'binary:logistic', **kwargs: Any) → None¶

dask_ml.metrics.log_loss

dask_ml.xgboost.XGBRegressor

dask_ml.xgboost.XGBClassifier

dask_ml.xgboost.XGBClassifier¶

`dask_ml.xgboost`.XGBClassifier¶