tensorflow / tensorflow/recommenders
Question about hyperparameters' tuning and model comparison.
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- Python
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Description
I'm a little confused about the practical difference between loss and metrics.
I have developed a model for predicting the next item a visitor will click in an e-shop, so I'm primarily interested in the actual click being in the top 5 results.
- During hyperparameter tuning, should I minimize the model's
lossor maximise thetop_5_categorical_accuracy? - When comparing different model variations, should I look for a lower
lossor a highertop_5_categorical_accuracy?
I appreciate any explanation you can provide.
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Research direction
No files, tests, or entry points are mentioned. Start by reviewing the distinction between loss and top_5_categorical_accuracy in the model-tuning guidance, then clarify which objective to use for hyperparameter tuning and model comparison based on the user's top-five recommendation goal.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 1/5
- Estimated time
- Under an hour
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100