Explain Any Machine Learning Model in Python, SHAP
A Comprehensive Guide to SHAP and Shapley Value; Explainable machine learning with a single function call
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This article is part of a series where we walk step by step through solving fintech problems with different Machine Learning techniques using the “All lending club loan” dataset. Here you can find the complete end-to-end data science project for beginners to learn data science.
In previous articles, we prepared a dataset and built a Logistic Regression model, and we discussed the most common “ML model evaluation metrics” for a classification problem in the fintech space. This article will try to “understand” how our model decision works and what packages can help us to answer this question.
Machine learning models are frequently named “black boxes”. They produce highly accurate predictions. However, we often fail to explain or understand what signal model relies on most to make the decision.
One way to understand and evaluate the model is to use metrics like accuracy, and another way to do it is to use model explainability. And it is an essential task in data science to build an ML model that can make high-quality predictions yet be able to interpret such predictions.
📌 What is the difference between interpretability and explainability?
Data scientists produce ML models that carry complex mathematical representations of world states. Data scientists, product managers, business people, or anyone involved in building and utilizing ML solutions eager to know what governs the ML output. People use ML interpretability or explainability terms interchangeably while discussing ML output. Let’s break down these definitions one by one.
Interpretability is responsible for answering what the fundamental mechanics behind the ML method are. For example, you may build a linear regression model to predict how many umbrellas you sell depending on the precipitation rate in a given region. Knowing model weights and features, you can easily calculate the number of sales. Then, you can answer precisely why and how the model predicted the…
