I wasn't aware that you had to specify eval_set when specifying eval_metric. I have not seen that in the documentation. When I add eval_set, my custom function is called. However, regardless of what my function returns (i.e., "F1 score" or "1 - F1 score" or even just "0"),...
# 需要导入模块: from xgboost import XGBClassifier [as 别名]# 或者: from xgboost.XGBClassifier importfit[as 别名]deffeature_selection(model, X_train, X_test, y_train, y_test, eval_metric='auc'):thresholds = [thresforthresinsorted(model.feature_importances_)ifthres !=0]# Use feat. with >...
model = dc.models.XGBoostModel(xgb_model, verbose=False, **esr)# Fit trained modelmodel.fit(train_dataset) model.save()# Eval model on testscores = model.evaluate(test_dataset, [classification_metric])assertscores[classification_metric.name] >.9 开发者ID:deepchem,项目名称:deepchem,代码行数:2...
F1/F0.5 score as eval_metric in XGBClassifier I'm performing a classification task using XGBClassifier - I want to reuse sklearn's functionalities as much as possible. Especially I'm interested in defining my custom scorer using f_beta function ... ...
只需要将Anaconda3的安装目录选在D:\Anaconda2\envs子目录下即可。详细安装教程请看这篇博文:http://...
'max_depth': 10, 'silent': True, 'tree_method':'gpu_hist','n_estimators': 5000, 'verbose_eval': 250} model = xgb.XGBClassifier(**params) eval_set = [(X_test, y_test), (X_train, y_train)] model.fit(X_train, y_train, eval_set=eval_set) prediction = model.predict(test) ...
early_stopping_rounds, eval_metric=xgb_metric, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=self.verbose) # Since test size is 20%, when retrain model to whole data, expect # n_estimator increased to 1/0.8 = 1.25 time. estimated_best_round = np.round(self.model_...
# 需要导入模块: from xgboost import XGBClassifier [as 别名]# 或者: from xgboost.XGBClassifier importpredict[as 别名]deffeature_selection(model, X_train, X_test, y_train, y_test, eval_metric='auc'):thresholds = [thresforthresinsorted(model.feature_importances_)ifthres !=0]# Use feat. with...
clf_XG.fit(os_X, os_y,eval_set=[(os_X, os_y), (X_test, y_test)],eval_metric='auc',verbose=False) evals_result = clf_XG.evals_result() y_true, y_pred = y_test, clf_XG.predict(X_test)#F1_score, precision, recall, specifity, G scoreprint"F1_score : %.4g"% metrics....
clf.fit(x0, y0, eval_metric="logloss", eval_set=[(x1, y1)],early_stopping_rounds=25) ll = -log_loss(y1, clf.predict_proba(x1))returnll 开发者ID:mpearmain,项目名称:bnp,代码行数:26,代码来源:xgb_autotune.py 示例3: xgboostcv ...