Case Study: Explore Feature Impact with Partial Dependence Plots

So far, we’ve explored the relative importance of different features. In this lesson, we will embark on a new journey where we’ll discover how a specific feature interacts with the target variable.

More specifically, we’ll study the partial dependence plot (PDP)—a powerful visual tool in machine learning that unveils the influence of a particular feature on the model’s predictions, while keeping all other features constant. By examining the isolated impact of a single variable across a range of values, PDPs help us understand the complex inner workings of the model.

PDPs provide a global perspective, focusing on the average effect of a feature rather than specific instances. This technique offers a range of benefits:

• It’s easy to compute and explain in simple terms, making it accessible to everyone.
• It helps us uncover the relationship between a feature (or a combination of features) and the target variable.
• Unlike other techniques, PDPs provide a causal interpretation, giving us valuable insights into how the feature impacts the model’s output.

In this lesson, we’ll analyze a loan dataset and apply the partial dependence plot to gain a deeper understanding of the model’s explainability.