我在学习支持向量回归时遇到了一个问题：我的R2分数变成了负值。这正常吗？还是我的代码中有可以修改的地方来解决这个问题？

import pandas as pdimport numpy as np import matplotlib.pyplot as pltfrom sklearn.svm import SVRdf = pd.read_csv('Position_Salaries.csv')df.head()X = df.iloc[:, 1:2].valuesy = df.iloc[:, -1].valuesfrom sklearn.preprocessing import StandardScalery = y.reshape(len(y),1)x_scaler = StandardScaler()y_scaler = StandardScaler()X = x_scaler.fit_transform(X)y = y_scaler.fit_transform(y)from sklearn.model_selection import train_test_splitx_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 42)regressor = SVR(kernel="rbf")regressor.fit(x_train,y_train.ravel())y_pred = y_scaler.inverse_transform(regressor.predict(x_scaler.transform(x_test)))from sklearn.metrics import r2_scorer2_score(y_scaler.inverse_transform(y_test), y_pred)

我的输出是 -0.5313206322807349

回答：

在这一部分，你的X是经过缩放的版本

X = x_scaler.fit_transform(X)

在这一部分，你的x_test也是经过缩放的版本

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 42)

在创建预测时，你不应该再次变换你的输入，因为你的x_test已经是缩放后的版本了

y_pred = y_scaler.inverse_transform(regressor.predict(x_scaler.transform(x_test)))