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scikit-learn 1.3.2
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# 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.

Examples:

- Common pitfalls in the interpretation of coefficients of linear models

- 4.1. Partial Dependence and Individual Conditional Expectation plots
  - 4.1.1. Partial dependence plots
  - 4.1.2. Individual conditional expectation (ICE) plot
  - 4.1.3. Mathematical Definition
  - 4.1.4. Computation methods
- 4.2. Permutation feature importance
  - 4.2.1. Outline of the permutation importance algorithm
  - 4.2.2. Relation to impurity-based importance in trees
  - 4.2.3. Misleading values on strongly correlated features