title: How to calculate the RMSE on Ridge regression model
body: <p>I have performed a ridge regression model on a data set
  (link to the dataset: <a href="https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data" rel="nofollow noreferrer">https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data</a>)
  as below:</p>
  
  <pre><code>from sklearn.linear_model import Ridge
  from sklearn.model_selection import train_test_split
  
  y = train['SalePrice']
  X = train.drop("SalePrice", axis = 1)
  
  X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.30)
  ridge = Ridge(alpha=0.1, normalize=True)
  ridge.fit(X_train,y_train)
  pred = ridge.predict(X_test)
  </code></pre>
  
  <p>I calculated the MSE using the metrics library from sklearn as </p>
  
  <pre><code>from sklearn.metrics import mean_squared_error
  mean = mean_squared_error(y_test, pred) 
  rmse = np.sqrt(mean_squared_error(y_test,pred)
  </code></pre>
  
  <p>I am getting a very large value of MSE = <code>554084039.54321</code> and RMSE = <code>21821.8</code>, I am trying to understand if my implementation is correct.</p>