Learn how to judge the accuracy of a recommendation using using precision and recall.
- [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 other 30% of data we'll hold back…
Author
Adam Geitgey
Released
4/10/2017Recommendation systems are a key part of almost every modern consumer website. The systems help drive customer interaction and sales by helping customers discover products and services they might not ever find themselves. The course uses the free, open source tools Python 3.5, pandas, and numpy. By the end of the course, you'll be equipped to use machine learning yourself to solve recommendation problems. What you learn can then be directly applied to your own projects.
- Building a machine learning system
- Training a machine learning system
- Refining the accuracy of the machine learning system
- Evaluating the recommendations received
Skill Level Intermediate
Duration
Views
-
Introduction
-
Welcome1m 1s
-
Set up environment2m 15s
-
-
1. The Basics of Making Recommendations
-
2. Ways of Making Recommendations
-
3. Getting to Know Our Tools
-
4. Building the Framework for Our Recommendation System
-
5. Collaborative Filtering with Matrix Factorization
-
6. Testing Our System
-
Use regularization1m 52s
-
7. Using the Recommendation System in a Real World Program
-
Find similar products1m 59s
-
Conclusion
-
Wrap up47s
-
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.
Share this video
Embed this video
Video: Measure recommendation accuracy