MLOps Glossary

Explainability

Explainability:

Explainability provides us with algorithms that give insights into trained model predictions. It allows us to answer questions such as:

  • How does a prediction change dependent on feature inputs?
  • What features are or are not important for a given prediction to hold?
  • What set of features would you have to minimally change to obtain a new prediction of your choosing?
  • How does each feature contribute to a model’s prediction?

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