title: Mean squared error returning unreasonably high numbers
body: <p>I'm trying to predict the profit each film made on IMDb.</p>
  
  <p>My dataframe and features are as follows:</p>
  
  <pre><code>   Actor1  Actor2  Actor3  Actor4   Day  Director  Genre1  Genre2  Genre3  \
  0       0       0       0       0  19.0         0       0       0       0   
  1       1       1       1       1   6.0         1       1       1       1   
  2       2       2       2       2  20.0         2       0       2       2   
  3       3       3       3       3   9.0         3       2       0      -1   
  4       4       4       4       4   9.0         4       3       3       3   
  
     Language  Month  Production  Rated  Runtime  Writer    Year    BoxOffice  
  
  0         1      0           0      0    118.0       0  2007.0   37500000.0  
  
  1         2      1           1      0    151.0       1  2006.0  132300000.0  
  
  2         1      1           2      1    130.0       2  2006.0   53100000.0  
  
  3         1      2           1      0    117.0       3  2007.0  210500000.0  
  
  4         4      3           3      2    117.0       4  2006.0  244052771.0 
  </code></pre>
  
  <p>and the value I'm trying to predict (target) is the BoxOffice.</p>
  
  <p>I'm following documentation for sklearn exactly as it is (<a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error" rel="nofollow noreferrer">http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error</a>)</p>
  
  <pre><code>from sklearn import preprocessing, linear_model
  from sklearn.metrics import mean_squared_error, r2_score
  from sklearn.model_selection import train_test_split, cross_val_score
  
  X = dataset[:,0:16] # Features
  Y = dataset[:,16] #Target
  
  X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.33)
  
  regr = linear_model.LinearRegression()
  regr.fit(X_train,Y_train)
  mean_squared_error(Y_test, regr.predict(X_test))
  </code></pre>
  
  <p>and the output is always something along the lines of:  <strong>11385650623660550</strong> ($11,385,650,623,660,500.00)</p>
  
  <p>While the mean of the BoxOffice is:
  107989121</p>
  
  <p>etc. </p>
  
  <p>Ive tried multiple different approaches, cross-validation as well as other models (keras) and feel like I've tried everything.</p>
  
  <p>The returning sum is extremely high which makes me question that the problem is not in the model or the data, but something else that I'm missing.</p>