From the course: Machine Learning and AI Foundations: Recommendations
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Use regularization
From the course: Machine Learning and AI Foundations: Recommendations
Use regularization
- [Instructor] In our recommendation system, we take review data and, from there, extract user attributes and movie attributes to act as the model. Using that model, we can make recommendations. A common problem that can happen when building a model like this is called overfitting. Overfitting is when the model doesn't learn the overall pattern of the data, but instead picks up too much on specific data points. Let's explain with an example. Imagine that we have two movies. The first movie's a horror comedy. The second is a serious bloody horror movie with no comedy at all. Both movies have horror elements, but some viewers might prefer the funny movie and other viewers might prefer the serious movie. A good recommendation system will be able to separate those two movies and see that, while both have some similar elements, they are very different movies that appeal to different audiences. A bad recommendation system that's overfitting would focus entirely on the horror attribute and…
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