4.eval_set: 5.eval_names: 6.eval_sample_weight: 7.eval_class_weight: 8.eval_init_score: 9.eval_metric: 10.verbose: 11.callbacks: 12.init_model: 13.pre_partition: LGBMRegressor.predict参数 1. X 2. num_iteration (n_iter_no_change) 3. raw_score 4. pred_leaf 5. pred_contrib 6...
clf.fit(trn_x, trn_y, eval_set= [(trn_x, trn_y), (val_x, val_y)], eval_metric='auc', verbose=100, early_stopping_rounds=40 #30 ) oof_preds[val_idx] = clf.predict_proba(val_x, num_iteration=clf.best_iteration_)[:, 1] del clf, trn_x, trn_y, val_x, val_y gc.co...
eval_set=eval_set, eval_metric=lambda ytrue, yprob: [eval_auc(ytrue, yprob), eval_ks(ytrue, yprob)], early_stopping_rounds=50, verbose=20) yprob = model.predict_proba(df_cv_train[self.feature_list])[:, 1] _, train_auc, _ = eval_auc(df_cv_train[self.label], yprob) _,...
fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5) # 模型存储 joblib.dump(gbm, 'loan_model.pkl') # 模型加载 gbm = joblib.load('loan_model.pkl') # 模型预测 y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_) # 模型评估 print('The ...
eval_metric = 'binary_error' if eval_set is not None: if isinstance(eval_set, tuple): eval_set = [eval_set] for i, (valid_x, valid_y) in enumerate(eval_set): if valid_x is X and valid_y is y: eval_set[i] = valid_x, _y ...
eval_set: 用于评估模型性能的额外数据集列表。 eval_names: 与 eval_set 对应的评估数据集名称列表。 eval_sample_weight: 与 eval_set 对应的评估数据集样本权重列表。 eval_class_weight: 仅适用于分类任务，用于指定评估数据集中各类别的权重。 eval_init_score: 用于初始化模型在每个...
# if you want to re-use data, remember to set free_raw_data=False lgb_train = lgb.Dataset(X_train, y_train, weight=W_train, free_raw_data=False) lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, weight=W_test, free_raw_data=False) ...
.eval(data,name,feval=None): 对指定的数据集evaluate 参数: data: 一个Dataset对象,代表被评估的数据集 name: 一个字符串,表示被评估的数据集的名字。不同的验证集通过名字来区分 feval: 一个可调用对象或者None, 它表示自定义的evaluation函数。默认为None。它的输入为(y_true, y_pred)、或者( y_true,...
gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5)# 模型存储joblib.dump(gbm,'loan_model.pkl')# 模型加载gbm = joblib.load('loan_model.pkl')# 模型预测y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)# 模型评估print('The accuracy of ...
fit(X, y, sample_weight=None, init_score=None, eval_set=None, eval_names=None, eval_sample_weight=None, eval_init_score=None, eval_metric='logloss', early_stopping_rounds=None, verbose=True, feature_name='auto', categorical_feature='auto', callbacks=None) ...