Interpretable Machine Learning

Learn more about the importance of Interpretable Machine Learning, what it means to have an interpretable machine learning model & examples of approaches used to provide interpretability.

Sophisticated machine learning models have the potential for high predictive accuracy but their complexity may sometimes result in black box models, which, in some cases, may appear to be a trade-off between accuracy and interpretability.  For the actuary, the ability to articulate the outputs of a model is important and becomes crucial where models are used to inform important business decisions or where stakeholders need to understand the underlying dynamics of a system, and the impact of the results.

The presentation covers

  1. The importance of interpretability
  2. What it means to have an interpretable machine learning model
  3. Examples of approaches that have been used to provide interpretability
  4. A practical case study showing an example approach to explain model predictions.
  5. Q&A
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