title: Is there a change I can make to avoid negative R2 scores in SVR?
body: <p>I'm using Support Vector Regression to predict an Extroversion Score based on the rest of the features in the dataset. However, the R2 scores I keep generating are negative.</p>
  
  <p>I tried using different kernels for the SVR function and using only some of the features in the dataset to predict the Score but the R2 scores remain negative.</p>
  
  <pre><code>df = pd.read_csv('Training Dataset.csv')
  
  X = df.iloc[:, 1:11]
  y = df.iloc[:, 0]
  
  scaler = MinMaxScaler(feature_range=(0, 1))
  X = scaler.fit_transform(X)
  
  best_svr = SVR()
  r2score = cross_val_score(best_svr, X, y, cv=5)
  
  print(np.mean(r2score))
  </code></pre>
  
  <p>There are no error messages, but the R2 scores remain negative. I'm not sure if it is because it is a poor model or if I can alter my code to correct this. My dataset is about 14000 x 12 and the scores I want to predict range from -0.3 to 0.5.</p>
  
  <p>Thanks in advance!</p>