Machine Learning
167 learners
Recommendation Systems Quality Evaluation
This course focuses on metrics specific to recommendation systems, crucial for evaluating and optimizing model performance. You'll delve into recommendation-specific metrics such as Coverage, Serendipity, Novelty, and Diversity. Each metric is presented with theoretical insights and practical coding examples to illustrate their application.
Python
4 lessons
16 practices
3 hours
Badge for Model Validation and Selection,
Model Validation and Selection
Course details
Understanding and Calculating Coverage in Recommendation Systems
Increasing Recommendation Diversity
Implement Coverage Function from Scratch
Visualize Model Coverage Effectively
Calculate Coverage with XGBoost
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