“Enhancing Model Transparency: Simple Insights with SHAP and Feature Engineering”
Apr 27, 2024
SHAP (SHapley Additive exPlanations) is a tool used in the field of machine learning to explain the output of models. It helps to understand how each feature in the dataset contributes to the prediction of a model, making the model’s decisions transparent and interpretable.
What is SHAP Used for
1.Model Explanation
2.Feature Importance
3.Model Debugging and Validation
4.Enhancing Trust and Compliance
Assuming you have a simple table data with features
Age, income education, purchased.
SHAP will help us to identify the importance of each feature vs target purchased
Graph depicts the Income has highest relation or impact on the target feature purchased.
Below is the simple cheat code to use SHAP explainer in your models.
Happy learning.
