ayush1695 / shap

Implementation of SHAPly values for Black box model interpretation on Diabetes multi-class classification Dataset

SHAP

!

Pic Credit: Aiden Cooper

Introduction to SHAP

Suppose you have a dataset with information about patients, including their age, BMI, blood pressure, and other health metrics, as well as whether or not they have been diagnosed with diabetes. You want to build a machine learning model that can predict whether or not a new patient will have diabetes, based on their health metrics.

To help you understand how your model is making these predictions, you can use SHAP to compute feature importance scores. These scores can tell you which health metrics are most important for predicting diabetes, and how they contribute to each individual prediction.

SHAP uses a technique called Shapley values to compute feature importance scores. These values are based on game theory, and provide a way to fairly distribute the contribution of each feature to a given prediction. SHAP can compute Shapley values for any machine learning model that can predict a numerical output, including classifiers like Random Forests, Decision Trees, and Neural Networks.

To use SHAP to explain your diabetes prediction model, you can start by loading your model and data into a SHAP explainer object. This will allow you to compute Shapley values for individual predictions and visualize how each feature contributes to the prediction.

Overall, SHAP is a powerful tool for interpreting machine learning models and understanding how they make their predictions. By using Shapley values to attribute feature importance, SHAP can provide valuable insights into the inner workings of complex models, such as those used for predicting diabetes.

In the code link here you will get an idea how we can leverage power of SHAP to unterpret black box model on multiclass classification dataset

Dataset Link : click here

Kaggle Notebook Link : click here

References

• https://arxiv.org/abs/1705.07874
• https://www.kaggle.com/code/bextuychiev/model-explainability-with-shap-only-guide-u-need
• https://h2o.ai/blog/shapley-values-a-gentle-introduction/
• https://www.analyticsvidhya.com/blog/2019/11/shapley-value-machine-learning-interpretability-game-theory/
• SHAP package
• Basic Sklearn, Xgboost, Lightgbm, and other general packages that a data scientist uses.