dask_ml.xgboost.XGBClassifier

dask_ml.xgboost.XGBClassifier

class dask_ml.xgboost.XGBClassifier(*, objective: Optional[Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]]]] = 'binary:logistic', use_label_encoder: bool = True, **kwargs: Any)
Attributes
best_iteration
best_ntree_limit
best_score
coef_

Coefficients property

feature_importances_

Feature importances property, return depends on importance_type parameter.

intercept_

Intercept (bias) property

n_features_in_

Methods

apply(X[, ntree_limit, 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_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 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_params(**params)

Set the parameters of this estimator.

__init__(*, objective: Optional[Union[str, Callable[[numpy.ndarray, numpy.ndarray], Tuple[numpy.ndarray, numpy.ndarray]]]] = 'binary:logistic', use_label_encoder: bool = True, **kwargs: Any) None