y_train) xgb_test = xgb.DMatrix(X_test, y_test) xgb_params = {'objective': 'multi:softmax', 'eval_metric': 'mlogloss', 'num_class': 3, 'verbosity': 0} xgb_model
If callable, a custom evaluation metric. The call signature is func(y_predicted, y_true) where y_true will be a DMatrix object such that you may need to call the get_label method. It must return a str, value pair where the str is a name for the evaluation and value is the value ...
(cv_model)#> Summary for CrossValidatorModel#> <cross_validator__13c346ec_bc09_4b8a_952d_92f9711299d7>#>#> Tuned Pipeline#> with metric f1#> over 4 hyperparameter sets#> via 3-fold cross validation#>#> Estimator: Pipeline#> <pipeline__bf0a05c1_6f0e_4875_ac1a_c77fbd6635f3>#...
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