Predictive performance is often the main goal of developing machine learning
models. Yet summarizing performance with an evaluation metric is often
insufficient: it assumes that the evaluation metric and test dataset
perfectly reflect the target domain, which is rarely true. In certain domains,
a model needs a certain level of interpretability before it can be deployed.
A model that is exhibiting performance issues needs to be debugged for one to
understand the model’s underlying issue. The
sklearn.inspection module provides tools to help understand the
predictions from a model and what affects them. This can be used to
evaluate assumptions and biases of a model, design a better model, or
to diagnose issues with model performance.
- 4.1. Partial Dependence and Individual Conditional Expectation plots
- 4.2. Permutation feature importance