What are some methods to explain machine learning model decisions?
Machine learning models can perform complex tasks and generate predictions, but they are often seen as black boxes that are hard to understand and trust. How can you explain what your model is doing and why it is making certain decisions? In this article, you will learn about some methods to make your machine learning model more interpretable and transparent.
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Feature importance analysis:Understanding which inputs impact your model most can clarify its decisions. Methods like permutation and SHAP give a data-backed peek into your model's 'thought process.'
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Visualize with PDPs:Partial dependence plots illustrate how features affect outcomes, offering insights into the model's logic. They're like a roadmap, highlighting the routes your data takes to reach predictions.