уличение SVR Sk-learn

In[] 
    param_dist = {'kernel':('linear', 'poly', 'rbf', 'sigmoid'),
                  'degree': sp_randint(1, 1000),
                  'gamma': ('scale', 'auto'),
                  'coef0': sp_randint(0, 1000),
                  'tol': sp_randint(1e-10, 1000),
                  'C' :sp_randint(1, 1000),
                  'epsilon' :sp_randint(0.001, 1000),
                  'cache_size' :sp_randint(1, 1000),
                  'max_iter' :sp_randint(1, 1000)
                 }
    n_iter_search = 100
    model = RandomizedSearchCV(SVR(), param_distributions=param_dist, cv=5, verbose=1, n_iter=n_iter_search)                           
    model.fit(inputs_train, outputs_train.ravel())
    train_predict = model.predict(inputs_train)
    test_predict = model.predict(inputs_test)
    MSE = mse(train_predict, outputs_train)
    print('Training mse', MSE ) 
    MSE = mse(test_predict, outputs_test)
    print('Test mse', MSE)
    MAE = mae(train_predict, outputs_train)
    print('Training mae', MAE )
    MAE = mae(test_predict, outputs_test)
    print('Test mae', MAE)
    R2 = r2_score(train_predict, outputs_train)
    print('Train R2', R2)    
    R2 = r2_score(test_predict, outputs_test)
    print('Test R2', R2)
    print('----------------------')
Out[] 
    Fitting 5 folds for each of 100 candidates, totalling 500 fits
    Training mse 11.932919123583382
    Test mse 11.736441902934569
    Training mae 3.412496159509839
    Test mae 3.378874618048341
    Train R2 0.0
    Test R2 0.0

R^2 равен нулю. можете помочь найти где я ошибся.