Machine Learning Interpretability in Finance: Investigating SHAP and LIME
Hey there, enthusiasts of machine learning in finance! If you’ve ever wondered what goes on behind the scenes in those complex ML models, this tutorial is here to satisfy your curiosity.
So, Imagine this: the finance industry, where data flows like a river and decisions have a real impact. Understanding why a machine learning model makes a particular choice isn’t as straightforward as we’d like it to be. That’s where we come in.
One major drawback of ML models is their lack of interpretability. It is often difficult to understand why a model made a particular decision, which can be a significant concern in finance where transparency is crucial.
In this tutorial, we will explore advanced methods for interpreting machine learning models in finance. Specifically, we will focus on two popular techniques: SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations). These methods provide insights into the decision-making process of machine learning models and help us understand the factors that contribute to their predictions.
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