如果你想要评估模型的性能,eval_metric 参数应该在 XGBClassifier 的初始化时设置,或者在调用 xgb.train()(用于更底层的训练接口)时设置,而不是在 fit() 方法中。 对于XGBClassifier,你可以在初始化时设置 eval_metric: python model = XGBClassifier(eval_metric='logloss') model.fit(X_train, y_train) 但...
-1 * eval_metric : logloss Training === Training for binary problems. Objective to optimize binary classification pipeline thresholds for: <evalml.objectives.standard_metrics.F1 object at 0x7f36d7d6ce50> Total training time (including CV): 3.1 seconds Cross Validation --- F1 MCC Binary Log ...
‘f1’, ‘f1_macro’, ‘f1_micro’, ‘f1_weighted’, ‘roc_auc’, ‘roc_auc_ovo_macro’, ‘average_precision’, ‘precision’, ‘precision_macro’, ‘precision_micro’, ‘precision_weighted’, ‘recall’, ‘recall_macro’, ‘recall_micro’, ‘recall_weighted’, ‘log_loss’, ‘pac_...
-1 * eval_metric : logloss Training === Training for binary problems. Objective to optimize binary classification pipeline thresholds for: <evalml.objectives.standard_metrics.F1 object at 0x7f628103edf0> Total training time (including CV): 2.9 seconds Cross Validation --- F1 MCC Binary Log L...
optimizer.zero_grad()loss_t.backward()optimizer.step()ifbatch_idx%args.log_interval==0:train_loss=loss_t.item()train_accuracy=get_correct_count(output,target)*100.0/len(target)experiment.add_metric(LOSS_METRIC,train_loss)experiment.add_metric(ACC_METRIC,train_accuracy)print('Train Epoch: {}...
我正在尝试组合一个由一个自定义eval函数和多个内置eval函数组成的eval_metrics列表。当我使用一个内置函数列表时,一切正常: model.fit( y_train_inner,eval_metric= ["error", "logloss", "map"],eval_set=[(X_test 浏览188提问于2019-04-16得票数1 ...
{ # General Parameters 'booster': 'gbtree' # Booster Parameters , 'eta': 0.3 , 'gamma': 0 , 'max_depth': 6 # Task Parameters , 'objective': 'binary:logistic' , 'eval_metric': 'logloss' } res = {} xgb_model = xgboost.train(params=params, dtrain=dtrain, num_boost_round=10...
(reg_lambda=0.001, iterations=450, depth=5, learning_rate=0.01, loss_function='MultiLogloss', eval_metric='HammingLoss', cat_features=feature_idx[-cat_feature_idx:], verbose=0, random_seed=42) chains = [ClassifierChain(clf, order="random", random_state=i) for i in range(10)] ...
return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix) File "/root/miniconda3/lib/python3.12/site-packages/transformers/trainer.py", line 4042, in predict output = eval_loop( File "/root/miniconda3/lib/python3.12/site-packages/transformers/trainer.py...
params = { 'objective': 'multiclass', 'num_class': 3, 'metric': 'multi_logloss' } 创建Light GBM的训练数据集和验证数据集: 代码语言:txt 复制 train_data = lgb.Dataset(X_train, label=y_train) valid_data = lgb.Dataset(X_valid, label=y_valid) 训练模型并使用eval_result方法获取评估...