成功解决ValueError: Shape of passed values is (1, 332), indices imply (1, 1)
目录
ValueError: Shape of passed values is (1, 332), indices imply (1, 1)
- def XGBR_train(X, y):
- train_x, test_x, train_y, test_y = train_test_split(X, y, test_size=0.3, random_state=0)
- test_preds = pd.DataFrame({"label": test_y}, index=[1,332])
- XGBR_model = XGBRegressor(
- learning_rate=0.03, 默认0.3
- n_estimators=100, 树的个数
- max_depth=4 )
-
- XGBR_model.fit(train_x, train_y)
- test_preds['y_pred'] = XGBR_model.predict(test_x)
- XGBR_model_score = metrics.r2_score(test_preds['label'], test_preds['y_pred'])
-
- GridSearchCV和cross_val_score的结果一样
- scores = cross_val_score(XGBR_model, X, y, scoring='r2')
- print(scores)
- gs = GridSearchCV(XGBR_model, {}, cv=3, verbose=3).fit(X, y)
-
- return XGBR_model, XGBR_model_score
值错误:传递值的形状为(1,332),索引表示(1,1)
可知,形状为一维数据,所以索引只能在数据的维数范围内,不可超出!
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