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SHAP Machine Learning Interpretability

June 21, 2023

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Explainable AI (XAI) is a branch of artificial intelligence that focuses on making automated decisions transparently. XAI seeks to explain the rationale behind machine learning models’ predictions, which can increase trust and help people determine whether to use them or not. One of the most powerful methods for explaining machine learning models is SHAP (Shapley Additive exPlanations).

Learn more about explainable AI and SHAP machine learning from experts.

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Why is Explainable AI Important?

There are a number of reasons why explainable AI is important. First, it can help to build trust between humans and machines. If people understand how and why a machine made a particular decision, they are more likely to trust it. 

Second, explainable AI can help to improve the usability of machine learning models. By understanding the logic behind the predictions, users can better understand how to use the model and make better decisions. 

Finally, explainable AI can help to improve the accuracy of machine learning models by providing insights into areas where the model may be failing.

About SHAP

SHAP uses Shapley values, an approach from game theory originally developed by Nobel Prize-winning economist Lloyd Shapley, to quantify the contributions of each feature towards the overall prediction. The Shapley value considers all possible combinations of features and assigns a score based on how much each combination affects the predictor’s outcome.

This helps to explain how each feature contributes to the prediction and which features are most important. The SHAP values can be visualized as a bar chart or summary plot, making the results intuitively more understandable.

Why Use SHAP?

SHAP provides many advantages over other methods of explanation. 

First, SHAP is model-agnostic, meaning it can be used with any machine learning model. Second, SHAP values have an intuitive interpretation—they represent the contribution of each feature to the model’s output. Finally, SHAP values are additive, meaning that they can be easily summed up to provide an overall explanation for the model’s output.

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