LGBMRegressor.fit参数 1.X: 2.y: 3.sample_weight: 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_chang...
gbm.fit(X_train,y_train,eval_set=[(X_test,y_test)],eval_metric='l1',early_stopping_rounds=5) # 预测测试集 y_pred=gbm.predict(X_test,num_iteration=gbm.best_iteration_) # 模型评估 print(mean_squared_error(y_test,y_pred)**0.5) # 查看特征重要性 print(list(gbm.feature_importances_...
model.fit(X_train, y_train, eval_set=(X_valid, y_valid), cat_features=[], use_best_model=True, verbose=False) y_pred_valid = model.predict(X_valid) y_pred = model.predict(X_test) oof[valid_index] = y_pred_valid.reshape(-1,) if eval_metric != 'group_mae': scores.append(...
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=[ LightGBMPruningCallback(trial,"binary_logloss") ], ) preds = model.predict_proba(X_test) p...
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_) ...
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 ...
gbm=LGBMRegressor(objective='regression',num_leaves=31,learning_rate=0.05,n_estimators=20)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 ...
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...
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 ...
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_)