Скачать или смотреть ML Tips shap summary and kernel explainer to understand what features are being used in final pred

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In this Python Machine Learning lesson, we will focus on understanding our models in the hope of explaining how they make predictions. In simple linear regression, this is easy and the coefficient values are easy to interpret as we get into an advanced ML like with a Random Forest Model understanding exactly how our models decide to vote one way or another becomes very time-consuming and in most cases too time-consuming to justify going through each tree in our random forest. And as we move into deep learning this understanding simply isn't possible and we need a solution to understand how our models are making predictions.


The Shap Library's shap summary plot is an amazing tool to help us understand how our model is making decisions. With a better understanding of which features are important, we can better understand which features simply to leave out which could improve our scores we can also use this understanding to engineer new features creating compositions of features that could support our model in its decision-making process.


If we can better understand our model we can better understand actions we can take to improve its overall predictiveness as a machine learning model.