Machine Learning
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
Model Validation and Selection
Course details
Increasing Recommendation Diversity
Implement Coverage Function from Scratch
Visualize Model Coverage Effectively
Calculate Coverage with XGBoost
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