y, # array, Series 类型 eval_set=None, # 用于评估的数据集,例如:[(X_train, y_train), (X_test, y_test)] eval_metric=None, # 评估函数,字符串类型,例如:'l2', 'logloss' early_stopping_rounds=None, verbose=True # 设置为正整数表示间隔多少次迭代输出一次信息 ) 1. 2. 3. 4. 5. 6...
X.iloc[test_idx] y_train, y_test = y[train_idx], y[test_idx] # LGBM建模 model = lgbm.LGBMClassifier(objective="binary", **param_grid) model.fit( X_train, y_train, eval_set=[(X_test, y_test)], eval_metric="binary_logloss", early_stopping_rounds=100, callbacks=[ LightGBMPrun...
lgb_ranker.fit(train_final_df[lgb_cols], train_final_df['label'], group=g_train, eval_set=[(val_final_df[lgb_cols], val_final_df['label'])], eval_group=[g_val], eval_at=[50], eval_metric=['auc',], early_stopping_rounds=50,) else: lgb_ranker.fit(train_final_df[lgb_col...
eval_set[i] = valid_x, _y else: eval_set[i] = valid_x, self._le.transform(valid_y) super(LGBMClassifier, self).fit(X, _y, sample_weight=sample_weight, init_score=init_score, eval_set=eval_set, eval_names=eval_names, eval_sample_weight=eval_sample_weight, eval_class_weight=eva...
gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], eval_metric='l1', 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_) ...
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 else: eval_set[i] = valid_x, self._le.transform(valid_y) ...
lgb_cv =lgbm.cv(params, d_train, num_boost_round=10000, nfold=3, shuffle=True, stratified=True, verbose_eval(params, d_train, num_boost_round=nround)preds = model.predict(test) predictions.append(np.argm 浏览5提问于2017-11-18得票数 16 ...
在kaggle机器学习竞赛赛中有一个调参神器组合非常热门，在很多个top方案中频频出现LightGBM+Optuna。知道...
=0 else None lgb_train = lgb.Dataset(X_, y_) lgb_eval = lgb.Dataset(x_test, y_test, reference=lgb_train) gbm= lgb.train(params,lgb_train, num_boost_round=10, init_model=temp, valid_sets=lgb_eval, callbacks= [lgb.reset_parameter(learning_rate=lambda x: 0.05)]) score_train = ...
( trn_x, trn_y, eval_set=[(val_x, val_y)], # categorical_feature=cate_cols, eval_metric='auc', early_stopping_rounds=200, verbose=200 ) # feat_imp_df['imp'] += clf.feature_importances_ / skf.n_splits oof[val_idx] = clf.predict_proba(val_x)[:, 1] test_df['prob'] ...