Understanding and diagnosing your machine-learning models
Achieving a good prediction is often only half of the job. Questions immediately arise: How to improve this prediction? What drives the prediction? Can we operate changes to the system based on the predictions? All these questions require understanding how good is the model prediction, and how do the model predict.
This tutorial assumes basic knowledge of scikit-learn. It will focus on statistics, tests, and interpretation rather than improving the prediction.
- 1. Measuring how well a model predicts
- 2. Understanding why a classifier predicts
- 3. Appendix: auxiliary figures
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