This is documentation for an old release of Scikit-learn (version 1.2). Try the latest stable release (version 1.6) or development (unstable) versions.
4. Inspection¶
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.