title: What factors will lead to extremely high RMSE value in a regression model?
body: <p>I was trying to build a regression model to predict movie box office. The dataset was acquired from Kaggle-TMDB 5000 Movie Dataset along with another dataset contains some social media related attributes. </p>
  
  <p>After merging and cleaning, the final dataset consists of 183 observations and 53 features. Two categorical features, genre, and production_countries were expanded. For example, new columns like 'Action', 'Drama', 'Comedy'...etc. And the value is 1 if that movie belongs to that genre. Same as production_countries. </p>
  
  <p>I used the dataset to build my regression model, but I encountered a problem. No matter I use train test split or cross-validation method or trying different regression model, the RMSE I got were insanely high.</p>
  
  <pre><code>from sklearn.model_selection import cross_val_predict
  X=movie.drop('Gross',axis=1)
  y=movie['Gross']
  print 'R2:',r2_score(y,cross_val_predict(RandomForestRegressor(),X,y,cv=10))
  print 'RMSE:',np.sqrt(metrics.mean_squared_error(y,cross_val_predict(method,X,y,cv=10)))
  
  output:
  R2: 0.344831741145
  RMSE: 76169019.1588 
  </code></pre>
  
  <p>I don't know what factors are resulting in this kind of situation. Can anyone help me out here? Many thanks.</p>