<liclass="toctree-l1 has-children"><aclass="reference internal" href="model_selection.html">3. Model selection and evaluation</a><details><summary><spanclass="toctree-toggle" role="presentation"><iclass="fa-solid fa-chevron-down"></i></span></summary><ul>
<liclass="toctree-l2"><aclass="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<liclass="toctree-l2"><aclass="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<liclass="toctree-l2"><aclass="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<liclass="toctree-l2"><aclass="reference internal" href="modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
<spanid="id1"></span><h1><spanclass="section-number">2. </span>Unsupervised learning<aclass="headerlink" href="#unsupervised-learning" title="Link to this heading">#</a></h1>
<liclass="toctree-l2"><aclass="reference internal" href="modules/manifold.html#local-tangent-space-alignment">2.2.7. Local Tangent Space Alignment</a></li>
<liclass="toctree-l2"><aclass="reference internal" href="modules/outlier_detection.html#novelty-detection-with-local-outlier-factor">2.7.4. Novelty detection with Local Outlier Factor</a></li>
</ul>
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<liclass="toctree-l1"><aclass="reference internal" href="modules/density.html">2.8. Density Estimation</a><ul>
<liclass="toctree-l2"><aclass="reference internal" href="modules/density.html#density-estimation-histograms">2.8.1. Density Estimation: Histograms</a></li>
<liclass="toctree-l2"><aclass="reference internal" href="modules/density.html#kernel-density-estimation">2.8.2. Kernel Density Estimation</a></li>