dask_ml.xgboost.XGBClassifier
dask_ml.xgboost.XGBClassifier¶
- class dask_ml.xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, silent=None, objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)¶
- Attributes
coef_Coefficients property
feature_importances_Feature importances property
intercept_Intercept (bias) property
Methods
apply(X[, ntree_limit])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 type parameters.
load_model(fname)Load the model from a file.
predict(X)Predict with data.
predict_proba(data[, ntree_limit])Predict the probability of each data 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__(max_depth=3, learning_rate=0.1, n_estimators=100, verbosity=1, silent=None, objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, colsample_bynode=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)¶