Shap's Kernel Explainer to Select the Best Features for ML Model
Most Impactful Features with Kernel Explainer
In this free Python Machine Learning Lesson, we will discuss how we can use a better understanding of our model to improve its performance. We can use the Explainer modulos of the shap library to get a better understanding of which features are impacting our predictions. This understanding is useful in many business situations, we can also use this knowledge to improve our predictions by using only the features that are truly impactful to the final prediction. This will also allow us to simplify the model reducing the chance of overfitting in a real-world situation.
After we've done everything we can to produce the best machine-learning model. Tuned the Hyperparameters and completed Error Analysis to find ways to engineer data, what next? What can we do to further improve our ML model? Feature Selection, we have engineered many features during our model-building process but which ones are actually helping our model, and which are hurting? That's a tough question to answer with just the feature importances attribute from sklearn. A better way to discover which on the best features to use in your ML is to use Kernel Explainer from the shap library and use what impact each feature has on final out to determine which features to use in our Final Model.
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Summary of Shap Kernel Explainer
The Kernel SHAP (SHapley Additive exPlanations) explainer is a method used in interpretability for machine learning models. It helps explain the output of a model by attributing the contribution of each feature to the model's prediction for a specific instance.
The key idea behind SHAP values is rooted in cooperative game theory's Shapley values. In the context of machine learning, SHAP values allocate contributions to each feature based on how it impacts the difference between the model's prediction for a given instance and the average prediction. The "kernel" in Kernel SHAP refers to the use of a weighted average of SHAP values over many possible orderings of feature importance.
The Kernel SHAP explainer works by considering subsets of features and computing their Shapley values, and then averaging these values across all possible feature subsets. This process involves sampling subsets of features, calculating the model's output for each subset, and determining the impact of adding or removing a feature. The "kernel" part involves using a weighted average based on a kernel function to approximate the Shapley values.
In summary, the SHAP explainer provides a way to understand how each feature contributes to the model's prediction for a specific instance, offering insights into the importance of individual features in the model's decision-making process.
