ML Tips Select Best Features for ML Model with shap Kernel Explainer
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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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