title: rmse cross validation using sklearn
body: <pre><code>from sklearn.model_selection import KFold
  from sklearn.model_selection import cross_val_score
  
  cv = KFold(n_splits=10, random_state=1, shuffle=True)
  
  scores = cross_val_score(regressor, X, y, scoring='neg_mean_absolute_error',
                           cv=cv, n_jobs=-1)
  np.mean(np.abs(scores))
  </code></pre>
  <p>regressor is the fitted model, X is the independent features and y is the dependent feature. Is the code right? Also I'm confused can rmse be bigger than 100? I'm getting values such as 121 from some regression models. Is rmse used to tell you how good your model is in general or only to tell you how good your model is compared to other models?</p>
  <p>rmse = 121
  <a href="https://i.stack.imgur.com/E5H0q.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/E5H0q.png" alt="enter image description here" /></a></p>