Comparing recommendation methods

In this course, you have predicted how you believe a user would rate movies they have not seen using multiple different methods (basic average ratings, KNN, matrix factorization). In this final exercise, you'll work through a comparison of the averaged ratings and matrix factorization using the mean_squared_error() as the measure of how well they are performing. The predictions based on averages have been loaded as avg_pred_ratings_df while the calculated predictions have been loaded as calc_pred_ratings_df. The ground truth values have been loaded as act_ratings_df.

Finally, the mean_squared_error() function has been imported for your use from sklearn.metrics.

Extract rows 0-20 and columns 0-100 (the areas that you want to compare) in the act_ratings_df, avg_pred_ratings_df, and calc_pred_ratings_df DataFrames.
Create a mask of the actual_values DataFrame that targets only non-empty cells.
Find the mean squared error between the two predictions and the ground truth values.

Exercise

Comparing recommendation methods

Instructions