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983 B
ReStructuredText
26 lines
983 B
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.. _inspection:
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Inspection
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----------
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Predictive performance is often the main goal of developing machine learning
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models. Yet summarizing performance with an evaluation metric is often
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insufficient: it assumes that the evaluation metric and test dataset
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perfectly reflect the target domain, which is rarely true. In certain domains,
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a model needs a certain level of interpretability before it can be deployed.
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A model that is exhibiting performance issues needs to be debugged for one to
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understand the model's underlying issue. The
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:mod:`sklearn.inspection` module provides tools to help understand the
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predictions from a model and what affects them. This can be used to
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evaluate assumptions and biases of a model, design a better model, or
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to diagnose issues with model performance.
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.. rubric:: Examples
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* :ref:`sphx_glr_auto_examples_inspection_plot_linear_model_coefficient_interpretation.py`
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.. toctree::
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modules/partial_dependence
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modules/permutation_importance
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