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<li class="toctree-l1 has-children"><a class="reference internal" href="supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/linear_model.html">1.1. Linear Models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/compose.html">6.1. Pipelines and composite estimators</a></li>
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