Methods for explaining machine learning models

Incorporating methods for interpreting and explaining machine learning models into your analytical toolkit can enhance transparency and provide insight into the decision-making process used by a machine learning model.

In some industries, explainability is an important aspect to consider; for example, in sensitive sectors, such as medicine and law, opaque “black-box” models are insufficient in scenarios where the reasoning behind how a machine learning model made a prediction is needed.

Let’s first look at a simple example, using coefficients to understand regression models.

Making sense of regression models – the power of coefficients

Imagine you’re using a regression model to predict future sales based on various factors such as marketing spend, seasonality, and product price. In this context, interpreting coefficients becomes akin to decoding the direct influence each factor has on your...