From the course: Machine Learning and AI Foundations: Recommendations
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Measure recommendation accuracy
From the course: Machine Learning and AI Foundations: Recommendations
Measure recommendation accuracy
- [Narrator] How do we actually know that the movie ratings we predict with our recommendations system are correct? To measure the accuracy of our predictions, we're going to use a standard statistics metric called root-mean-square-error or RMSE. RMSE is a measurement of the difference between the user's real movie ratings and the ratings we predicted for the same movies. The lower the RMSE, the more accurate the model. An RMSE of zero means our model perfectly guesses user ratings. An RMSE of one, means we are off by about one star on average when predicting user ratings. When measuring the accuracy of our recommendation system, we need to make sure the data we are using to validate the system is data the system has never seen before. Otherwise, it's not a fair test. So, we'll randomly split our movie ratings data into two groups, the first 70% of data will be our training data set. We'll use the training data set to do matrix factorization and to create the U and M matrices. The…
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