Interpreting Machine Learning Models With SHAP: A Guide With Python Examples And Theory On Shapley Values

Price: $35.00
(as of Dec 18,2024 00:31:47 UTC – Details)

ASIN ‏ : ‎ B0CHL7W1DL
Publisher ‏ : ‎ Independently published (September 7, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 208 pages
ISBN-13 ‏ : ‎ 979-8857734445
Item Weight ‏ : ‎ 1.07 pounds
Dimensions ‏ : ‎ 7.44 x 0.47 x 9.69 inches

Interpreting Machine Learning Models With SHAP: A Guide With Python Examples And Theory On Shapley Values

Machine learning models have become increasingly complex and accurate, making it difficult to understand how they arrive at their predictions. SHAP (SHapley Additive exPlanations) is a powerful tool that helps us interpret the output of these models by attributing the prediction to individual features.

In this post, we will delve into the theory behind SHAP and provide practical examples using Python to demonstrate how it can be used to interpret machine learning models. We will cover the concept of Shapley values, how they are calculated, and how they can be used to explain the contribution of each feature to the model’s prediction.

By the end of this guide, you will have a solid understanding of SHAP and be able to apply it to your own machine learning models to gain insights into how they work and make more informed decisions.

So, buckle up and get ready to dive into the fascinating world of SHAP and Shapley values!
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