From the course: Building a Recommendation System with Python Machine Learning & AI
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Evaluating recommendation systems - Python Tutorial
From the course: Building a Recommendation System with Python Machine Learning & AI
Evaluating recommendation systems
- [Instructor] The last thing that I want to discuss for this course is Model Evaluation. To ascertain how reliable our models are we need to determine the quality of the predictions that they make. To do that in Python we can use scikit-learn's metrics module. Within this module you can find all sorts of functions for scoring your models and evaluating their predictive performance. Use these results to help you select the best model for your given situation. The first metric we'll look at is precision. Precision is a measure of a model's relevancy. To represent it algebraically, you can think of precision as the number of items that I liked that were also recommended to me divided by the number of items that were recommended to me. Or, in other words, how relevant were the recommendations that were made? So, for example, if a system recommended eight items and four of those items were items that you like, then the system would have achieved 50% precision. Another important metric is…
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