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</style><section id="inspection">
<span id="id1"></span><h1><span class="section-number">4. </span>Inspection<a class="headerlink" href="#inspection" title="Permalink to this heading">¶</a></h1>
<p>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
<a class="reference internal" href="modules/classes.html#module-sklearn.inspection" title="sklearn.inspection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.inspection</span></code></a> 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.</p>
<aside class="topic">
<p class="topic-title">Examples:</p>
<ul class="simple">
<li><p><a class="reference internal" href="auto_examples/inspection/plot_linear_model_coefficient_interpretation.html#sphx-glr-auto-examples-inspection-plot-linear-model-coefficient-interpretation-py"><span class="std std-ref">Common pitfalls in the interpretation of coefficients of linear models</span></a></p></li>
</ul>
</aside>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="modules/partial_dependence.html">4.1. Partial Dependence and Individual Conditional Expectation plots</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/partial_dependence.html#partial-dependence-plots">4.1.1. Partial dependence plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/partial_dependence.html#individual-conditional-expectation-ice-plot">4.1.2. Individual conditional expectation (ICE) plot</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/partial_dependence.html#mathematical-definition">4.1.3. Mathematical Definition</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/partial_dependence.html#computation-methods">4.1.4. Computation methods</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/permutation_importance.html">4.2. Permutation feature importance</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/permutation_importance.html#outline-of-the-permutation-importance-algorithm">4.2.1. Outline of the permutation importance algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/permutation_importance.html#relation-to-impurity-based-importance-in-trees">4.2.2. Relation to impurity-based importance in trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/permutation_importance.html#misleading-values-on-strongly-correlated-features">4.2.3. Misleading values on strongly correlated features</a></li>
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