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explainable_ai's Introduction

Explainable AI

Opening the "black box" of machine learning models has been huge in not only understanding the models we create, but then also communicating the insights to others. As I encounter different use cases for explainable AI, I'm distilling the insights into manageable chunks and sharing them openly.

Explainability with Multi-Output Regression Models

  • Demonstrate a way to explore the explainability of multi-output regrssion models with SHAP.
  • View ipynb here (Recommend downloading and running the entire notebook in Google Colab)

SHAP Values for Multi-Output Regression Models

Apply Shapely values to a multi-output regression model to explore how the features effects each of the outputs/labels independently.


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explainable_ai's Issues

SHAP dependence plots with histograms and regression lines from Kernalexplainer and multioutput regression, wrapper tree based ensemble models. #2825

shap/shap#2825

I want to draw SHAP partial dependence plots with regression lines + and histograms.

I am using kernal explainer for multioutput regression as a wrapper models for xgboost / lgbm/ xgbr/ RF/ etc.

Representing SHAP partial dependence plots (scatter plot and a regression line represented with line and shade) + histogram on the right and top are the distribution of the SHAP and values of variables.

Reference Article : https://www.nature.com/articles/s41598-021-99920-7

Here are the visual graphs.

Reference codes for shap multioutput plots : https://shap.readthedocs.io/en/latest/example_notebooks/tabular_examples/model_agnostic/Multioutput%20Regression%20SHAP.html

But I am unable to draw these kinds of graphs. Because i couldn't develop code for masker and shap values for these kid of plots.
210651286-057f43b0-8a85-4bca-aca4-e8f138bebc5c
210651282-57d39f44-864e-49fd-8093-abde610c5bfd
210651276-4711c86a-66d3-4721-bd42-d2392629888e

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