为什么我的 SVR 模型给出的 r2_score 为 0，而线性回归和 xgboost 在相同数据上给出 0.7 和 0.8

如何解决为什么我的 SVR 模型给出的 r2_score 为 0，而线性回归和 xgboost 在相同数据上给出 0.7 和 0.8

我已经预处理了我的数据并在我的数据上拟合了各种模型，以查看每个模型的性能。我的代码：

# standardize the data using Robust Scaler

rc = RobustScaler()
X_train = rc.fit_transform(X_train)
X_test = rc.transform(X_test)

使用线性回归模型

regr = LinearRegression() 
regr.fit(X_train,y_train)

train_score = regr.score(X_train,y_train)
#print training score
print("training score: ",np.round(train_score,2))

# making predictions on test data
y_pred_test=regr.predict(X_test)  
test_score = regr.score(X_test,y_test)
print("test score:",np.round(test_score,2))

给出输出

training score:  0.73
test score: 0.74

XGBoost

from xgboost import XGBRegressor

model = XGBRegressor()
model.fit(X_train,y_train)

train_score = model.score(X_train,y_train)
print("training score: ",2))

# making predictions on test data
y_pred_test=model.predict(X_test)  
test_score = model.score(X_test,2))

给出输出

training score:  0.89
test score: 0.82

当我尝试使用 SVR 模型时

# fitting SVR model

svr = SVR(kernel = 'rbf')
svr.fit(X_train,y_train)

y_pred_train = svr.predict(X_train)
train_score = svr.score(X_train,y_train)

#print training score
print("training score: ",2))

# making predictions on test data
y_pred_test=svr.predict(X_test)  
test_score = svr.score(X_test,2))

我的输出

training score:  -0.0
test score: -0.0

我不明白为什么我的 svr 模型给出了 0 分。即使它在一定程度上表现不佳，我也希望分值会下降但不会是 0。我可能哪里出错了？