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 :mod:`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

• :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`

.. toctree::

    modules/partial_dependence
    modules/permutation_importance