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How to use Shap kernel Explainer for machine learning?
I am trying to use SHAP kernel explainer to understand my XGBOOST model. My data is the lending club data and I am trying to predict the Grade of each customer. The data contains different types of data (Objects, float) so I use one hot encoding for object features and scaling for numerical features.
How to use Shap Kernal explainer with pipeline models?
How to Use Shap Kernal Explainer with Pipeline models? I have a pandas DataFrame X. I would like to find the prediction explanation of a a particular model. Now for prediction explainer, I use Kernal Explainer from Shap.
How to use kernel Explainer for machine learning pipeline?
The data contains different types of data (Objects, float) so I use one hot encoding for object features and scaling for numerical features. When I try to use the kernel explainer with the below code I get the following error.
How does the kernelexplainer build a weighted linear regression?
The KernelExplainer builds a weighted linear regression by using your data, your predictions, and whatever function that predicts the predicted values. It computes the variable importance values based on the Shapley values from game theory, and the coefficients from a local linear regression.
Is there a way to use pipeline with Shap?
Please check the use of Pipeline with Shap following the link. Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. Provide details and share your research!
How to use kernelexplainer in Stack Overflow?
To show the features which drove the probability for classification in class 1: To show the features which drove the probability for classification in class 2: It would be great if someone could doublecheck this, but in any case this is what I think is correct now. Thanks for contributing an answer to Stack Overflow!