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<li class="toctree-l1 current active has-children"><a class="current reference internal" href="#">sklearn.metrics</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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  <section id="module-sklearn.metrics">
<span id="sklearn-metrics"></span><span id="metrics-ref"></span><h1>sklearn.metrics<a class="headerlink" href="#module-sklearn.metrics" title="Link to this heading">#</a></h1>
<p>Score functions, performance metrics, pairwise metrics and distance computations.</p>
<p><strong>User guide.</strong> See the <a class="reference internal" href="../modules/model_evaluation.html#model-evaluation"><span class="std std-ref">Metrics and scoring: quantifying the quality of predictions</span></a> and <a class="reference internal" href="../modules/metrics.html#metrics"><span class="std std-ref">Pairwise metrics, Affinities and Kernels</span></a> sections for further details.</p>
<section id="model-selection-interface">
<span id="metrics-ref-model-selection-interface"></span><h2>Model selection interface<a class="headerlink" href="#model-selection-interface" title="Link to this heading">#</a></h2>
<p><strong>User guide.</strong> See the <a class="reference internal" href="../modules/model_evaluation.html#scoring-parameter"><span class="std std-ref">The scoring parameter: defining model evaluation rules</span></a> section for further details.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.check_scoring.html#sklearn.metrics.check_scoring" title="sklearn.metrics.check_scoring"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_scoring</span></code></a></p></td>
<td><p>Determine scorer from user options.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.get_scorer.html#sklearn.metrics.get_scorer" title="sklearn.metrics.get_scorer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_scorer</span></code></a></p></td>
<td><p>Get a scorer from string.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.get_scorer_names.html#sklearn.metrics.get_scorer_names" title="sklearn.metrics.get_scorer_names"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_scorer_names</span></code></a></p></td>
<td><p>Get the names of all available scorers.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-obj docutils literal notranslate"><span class="pre">make_scorer</span></code></a></p></td>
<td><p>Make a scorer from a performance metric or loss function.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="classification-metrics">
<span id="metrics-ref-classification-metrics"></span><h2>Classification metrics<a class="headerlink" href="#classification-metrics" title="Link to this heading">#</a></h2>
<p><strong>User guide.</strong> See the <a class="reference internal" href="../modules/model_evaluation.html#classification-metrics"><span class="std std-ref">Classification metrics</span></a> section for further details.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score" title="sklearn.metrics.accuracy_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">accuracy_score</span></code></a></p></td>
<td><p>Accuracy classification score.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.auc.html#sklearn.metrics.auc" title="sklearn.metrics.auc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">auc</span></code></a></p></td>
<td><p>Compute Area Under the Curve (AUC) using the trapezoidal rule.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.average_precision_score.html#sklearn.metrics.average_precision_score" title="sklearn.metrics.average_precision_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">average_precision_score</span></code></a></p></td>
<td><p>Compute average precision (AP) from prediction scores.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.balanced_accuracy_score.html#sklearn.metrics.balanced_accuracy_score" title="sklearn.metrics.balanced_accuracy_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">balanced_accuracy_score</span></code></a></p></td>
<td><p>Compute the balanced accuracy.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.brier_score_loss.html#sklearn.metrics.brier_score_loss" title="sklearn.metrics.brier_score_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">brier_score_loss</span></code></a></p></td>
<td><p>Compute the Brier score loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.class_likelihood_ratios.html#sklearn.metrics.class_likelihood_ratios" title="sklearn.metrics.class_likelihood_ratios"><code class="xref py py-obj docutils literal notranslate"><span class="pre">class_likelihood_ratios</span></code></a></p></td>
<td><p>Compute binary classification positive and negative likelihood ratios.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="sklearn.metrics.classification_report"><code class="xref py py-obj docutils literal notranslate"><span class="pre">classification_report</span></code></a></p></td>
<td><p>Build a text report showing the main classification metrics.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.cohen_kappa_score.html#sklearn.metrics.cohen_kappa_score" title="sklearn.metrics.cohen_kappa_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cohen_kappa_score</span></code></a></p></td>
<td><p>Compute Cohen's kappa: a statistic that measures inter-annotator agreement.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="sklearn.metrics.confusion_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">confusion_matrix</span></code></a></p></td>
<td><p>Compute confusion matrix to evaluate the accuracy of a classification.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.d2_log_loss_score.html#sklearn.metrics.d2_log_loss_score" title="sklearn.metrics.d2_log_loss_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">d2_log_loss_score</span></code></a></p></td>
<td><p><span class="math notranslate nohighlight">\(D^2\)</span> score function, fraction of log loss explained.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.dcg_score.html#sklearn.metrics.dcg_score" title="sklearn.metrics.dcg_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dcg_score</span></code></a></p></td>
<td><p>Compute Discounted Cumulative Gain.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.det_curve.html#sklearn.metrics.det_curve" title="sklearn.metrics.det_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">det_curve</span></code></a></p></td>
<td><p>Compute error rates for different probability thresholds.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score" title="sklearn.metrics.f1_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">f1_score</span></code></a></p></td>
<td><p>Compute the F1 score, also known as balanced F-score or F-measure.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.fbeta_score.html#sklearn.metrics.fbeta_score" title="sklearn.metrics.fbeta_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fbeta_score</span></code></a></p></td>
<td><p>Compute the F-beta score.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.hamming_loss.html#sklearn.metrics.hamming_loss" title="sklearn.metrics.hamming_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hamming_loss</span></code></a></p></td>
<td><p>Compute the average Hamming loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.hinge_loss.html#sklearn.metrics.hinge_loss" title="sklearn.metrics.hinge_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hinge_loss</span></code></a></p></td>
<td><p>Average hinge loss (non-regularized).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.jaccard_score.html#sklearn.metrics.jaccard_score" title="sklearn.metrics.jaccard_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">jaccard_score</span></code></a></p></td>
<td><p>Jaccard similarity coefficient score.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.log_loss.html#sklearn.metrics.log_loss" title="sklearn.metrics.log_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log_loss</span></code></a></p></td>
<td><p>Log loss, aka logistic loss or cross-entropy loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.matthews_corrcoef.html#sklearn.metrics.matthews_corrcoef" title="sklearn.metrics.matthews_corrcoef"><code class="xref py py-obj docutils literal notranslate"><span class="pre">matthews_corrcoef</span></code></a></p></td>
<td><p>Compute the Matthews correlation coefficient (MCC).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.multilabel_confusion_matrix.html#sklearn.metrics.multilabel_confusion_matrix" title="sklearn.metrics.multilabel_confusion_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multilabel_confusion_matrix</span></code></a></p></td>
<td><p>Compute a confusion matrix for each class or sample.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.ndcg_score.html#sklearn.metrics.ndcg_score" title="sklearn.metrics.ndcg_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndcg_score</span></code></a></p></td>
<td><p>Compute Normalized Discounted Cumulative Gain.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.precision_recall_curve.html#sklearn.metrics.precision_recall_curve" title="sklearn.metrics.precision_recall_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">precision_recall_curve</span></code></a></p></td>
<td><p>Compute precision-recall pairs for different probability thresholds.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.precision_recall_fscore_support.html#sklearn.metrics.precision_recall_fscore_support" title="sklearn.metrics.precision_recall_fscore_support"><code class="xref py py-obj docutils literal notranslate"><span class="pre">precision_recall_fscore_support</span></code></a></p></td>
<td><p>Compute precision, recall, F-measure and support for each class.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score" title="sklearn.metrics.precision_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">precision_score</span></code></a></p></td>
<td><p>Compute the precision.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score" title="sklearn.metrics.recall_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">recall_score</span></code></a></p></td>
<td><p>Compute the recall.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score" title="sklearn.metrics.roc_auc_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">roc_auc_score</span></code></a></p></td>
<td><p>Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC)     from prediction scores.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve" title="sklearn.metrics.roc_curve"><code class="xref py py-obj docutils literal notranslate"><span class="pre">roc_curve</span></code></a></p></td>
<td><p>Compute Receiver operating characteristic (ROC).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.top_k_accuracy_score.html#sklearn.metrics.top_k_accuracy_score" title="sklearn.metrics.top_k_accuracy_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">top_k_accuracy_score</span></code></a></p></td>
<td><p>Top-k Accuracy classification score.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.zero_one_loss.html#sklearn.metrics.zero_one_loss" title="sklearn.metrics.zero_one_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_one_loss</span></code></a></p></td>
<td><p>Zero-one classification loss.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="regression-metrics">
<span id="metrics-ref-regression-metrics"></span><h2>Regression metrics<a class="headerlink" href="#regression-metrics" title="Link to this heading">#</a></h2>
<p><strong>User guide.</strong> See the <a class="reference internal" href="../modules/model_evaluation.html#regression-metrics"><span class="std std-ref">Regression metrics</span></a> section for further details.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.d2_absolute_error_score.html#sklearn.metrics.d2_absolute_error_score" title="sklearn.metrics.d2_absolute_error_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">d2_absolute_error_score</span></code></a></p></td>
<td><p><span class="math notranslate nohighlight">\(D^2\)</span> regression score function, fraction of absolute error explained.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.d2_pinball_score.html#sklearn.metrics.d2_pinball_score" title="sklearn.metrics.d2_pinball_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">d2_pinball_score</span></code></a></p></td>
<td><p><span class="math notranslate nohighlight">\(D^2\)</span> regression score function, fraction of pinball loss explained.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.d2_tweedie_score.html#sklearn.metrics.d2_tweedie_score" title="sklearn.metrics.d2_tweedie_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">d2_tweedie_score</span></code></a></p></td>
<td><p><span class="math notranslate nohighlight">\(D^2\)</span> regression score function, fraction of Tweedie deviance explained.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.explained_variance_score.html#sklearn.metrics.explained_variance_score" title="sklearn.metrics.explained_variance_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">explained_variance_score</span></code></a></p></td>
<td><p>Explained variance regression score function.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.max_error.html#sklearn.metrics.max_error" title="sklearn.metrics.max_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max_error</span></code></a></p></td>
<td><p>The max_error metric calculates the maximum residual error.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.mean_absolute_error.html#sklearn.metrics.mean_absolute_error" title="sklearn.metrics.mean_absolute_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_absolute_error</span></code></a></p></td>
<td><p>Mean absolute error regression loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.mean_absolute_percentage_error.html#sklearn.metrics.mean_absolute_percentage_error" title="sklearn.metrics.mean_absolute_percentage_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_absolute_percentage_error</span></code></a></p></td>
<td><p>Mean absolute percentage error (MAPE) regression loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.mean_gamma_deviance.html#sklearn.metrics.mean_gamma_deviance" title="sklearn.metrics.mean_gamma_deviance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_gamma_deviance</span></code></a></p></td>
<td><p>Mean Gamma deviance regression loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.mean_pinball_loss.html#sklearn.metrics.mean_pinball_loss" title="sklearn.metrics.mean_pinball_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_pinball_loss</span></code></a></p></td>
<td><p>Pinball loss for quantile regression.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.mean_poisson_deviance.html#sklearn.metrics.mean_poisson_deviance" title="sklearn.metrics.mean_poisson_deviance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_poisson_deviance</span></code></a></p></td>
<td><p>Mean Poisson deviance regression loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.mean_squared_error.html#sklearn.metrics.mean_squared_error" title="sklearn.metrics.mean_squared_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_squared_error</span></code></a></p></td>
<td><p>Mean squared error regression loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.mean_squared_log_error.html#sklearn.metrics.mean_squared_log_error" title="sklearn.metrics.mean_squared_log_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_squared_log_error</span></code></a></p></td>
<td><p>Mean squared logarithmic error regression loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.mean_tweedie_deviance.html#sklearn.metrics.mean_tweedie_deviance" title="sklearn.metrics.mean_tweedie_deviance"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean_tweedie_deviance</span></code></a></p></td>
<td><p>Mean Tweedie deviance regression loss.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.median_absolute_error.html#sklearn.metrics.median_absolute_error" title="sklearn.metrics.median_absolute_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">median_absolute_error</span></code></a></p></td>
<td><p>Median absolute error regression loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.r2_score.html#sklearn.metrics.r2_score" title="sklearn.metrics.r2_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">r2_score</span></code></a></p></td>
<td><p><span class="math notranslate nohighlight">\(R^2\)</span> (coefficient of determination) regression score function.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.root_mean_squared_error.html#sklearn.metrics.root_mean_squared_error" title="sklearn.metrics.root_mean_squared_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">root_mean_squared_error</span></code></a></p></td>
<td><p>Root mean squared error regression loss.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.root_mean_squared_log_error.html#sklearn.metrics.root_mean_squared_log_error" title="sklearn.metrics.root_mean_squared_log_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">root_mean_squared_log_error</span></code></a></p></td>
<td><p>Root mean squared logarithmic error regression loss.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="multilabel-ranking-metrics">
<span id="metrics-ref-multilabel-ranking-metrics"></span><h2>Multilabel ranking metrics<a class="headerlink" href="#multilabel-ranking-metrics" title="Link to this heading">#</a></h2>
<p><strong>User guide.</strong> See the <a class="reference internal" href="../modules/model_evaluation.html#multilabel-ranking-metrics"><span class="std std-ref">Multilabel ranking metrics</span></a> section for further details.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.coverage_error.html#sklearn.metrics.coverage_error" title="sklearn.metrics.coverage_error"><code class="xref py py-obj docutils literal notranslate"><span class="pre">coverage_error</span></code></a></p></td>
<td><p>Coverage error measure.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.label_ranking_average_precision_score.html#sklearn.metrics.label_ranking_average_precision_score" title="sklearn.metrics.label_ranking_average_precision_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">label_ranking_average_precision_score</span></code></a></p></td>
<td><p>Compute ranking-based average precision.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.label_ranking_loss.html#sklearn.metrics.label_ranking_loss" title="sklearn.metrics.label_ranking_loss"><code class="xref py py-obj docutils literal notranslate"><span class="pre">label_ranking_loss</span></code></a></p></td>
<td><p>Compute Ranking loss measure.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="module-sklearn.metrics.cluster">
<span id="clustering-metrics"></span><span id="metrics-ref-clustering-metrics"></span><h2>Clustering metrics<a class="headerlink" href="#module-sklearn.metrics.cluster" title="Link to this heading">#</a></h2>
<p>Evaluation metrics for cluster analysis results.</p>
<ul class="simple">
<li><p>Supervised evaluation uses a ground truth class values for each sample.</p></li>
<li><p>Unsupervised evaluation does use ground truths and measures the “quality” of the
model itself.</p></li>
</ul>
<p><strong>User guide.</strong> See the <a class="reference internal" href="../modules/clustering.html#clustering-evaluation"><span class="std std-ref">Clustering performance evaluation</span></a> section for further details.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.adjusted_mutual_info_score.html#sklearn.metrics.adjusted_mutual_info_score" title="sklearn.metrics.adjusted_mutual_info_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">adjusted_mutual_info_score</span></code></a></p></td>
<td><p>Adjusted Mutual Information between two clusterings.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.adjusted_rand_score.html#sklearn.metrics.adjusted_rand_score" title="sklearn.metrics.adjusted_rand_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">adjusted_rand_score</span></code></a></p></td>
<td><p>Rand index adjusted for chance.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.calinski_harabasz_score.html#sklearn.metrics.calinski_harabasz_score" title="sklearn.metrics.calinski_harabasz_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calinski_harabasz_score</span></code></a></p></td>
<td><p>Compute the Calinski and Harabasz score.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.cluster.contingency_matrix.html#sklearn.metrics.cluster.contingency_matrix" title="sklearn.metrics.cluster.contingency_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.contingency_matrix</span></code></a></p></td>
<td><p>Build a contingency matrix describing the relationship between labels.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html#sklearn.metrics.cluster.pair_confusion_matrix" title="sklearn.metrics.cluster.pair_confusion_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cluster.pair_confusion_matrix</span></code></a></p></td>
<td><p>Pair confusion matrix arising from two clusterings.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.completeness_score.html#sklearn.metrics.completeness_score" title="sklearn.metrics.completeness_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">completeness_score</span></code></a></p></td>
<td><p>Compute completeness metric of a cluster labeling given a ground truth.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.davies_bouldin_score.html#sklearn.metrics.davies_bouldin_score" title="sklearn.metrics.davies_bouldin_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">davies_bouldin_score</span></code></a></p></td>
<td><p>Compute the Davies-Bouldin score.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.fowlkes_mallows_score.html#sklearn.metrics.fowlkes_mallows_score" title="sklearn.metrics.fowlkes_mallows_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fowlkes_mallows_score</span></code></a></p></td>
<td><p>Measure the similarity of two clusterings of a set of points.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.html#sklearn.metrics.homogeneity_completeness_v_measure" title="sklearn.metrics.homogeneity_completeness_v_measure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">homogeneity_completeness_v_measure</span></code></a></p></td>
<td><p>Compute the homogeneity and completeness and V-Measure scores at once.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.homogeneity_score.html#sklearn.metrics.homogeneity_score" title="sklearn.metrics.homogeneity_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">homogeneity_score</span></code></a></p></td>
<td><p>Homogeneity metric of a cluster labeling given a ground truth.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.mutual_info_score.html#sklearn.metrics.mutual_info_score" title="sklearn.metrics.mutual_info_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mutual_info_score</span></code></a></p></td>
<td><p>Mutual Information between two clusterings.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.normalized_mutual_info_score.html#sklearn.metrics.normalized_mutual_info_score" title="sklearn.metrics.normalized_mutual_info_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normalized_mutual_info_score</span></code></a></p></td>
<td><p>Normalized Mutual Information between two clusterings.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.rand_score.html#sklearn.metrics.rand_score" title="sklearn.metrics.rand_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rand_score</span></code></a></p></td>
<td><p>Rand index.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.silhouette_samples.html#sklearn.metrics.silhouette_samples" title="sklearn.metrics.silhouette_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">silhouette_samples</span></code></a></p></td>
<td><p>Compute the Silhouette Coefficient for each sample.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score" title="sklearn.metrics.silhouette_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">silhouette_score</span></code></a></p></td>
<td><p>Compute the mean Silhouette Coefficient of all samples.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.v_measure_score.html#sklearn.metrics.v_measure_score" title="sklearn.metrics.v_measure_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">v_measure_score</span></code></a></p></td>
<td><p>V-measure cluster labeling given a ground truth.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="biclustering-metrics">
<span id="metrics-ref-biclustering-metrics"></span><h2>Biclustering metrics<a class="headerlink" href="#biclustering-metrics" title="Link to this heading">#</a></h2>
<p><strong>User guide.</strong> See the <a class="reference internal" href="../modules/biclustering.html#biclustering-evaluation"><span class="std std-ref">Biclustering evaluation</span></a> section for further details.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.consensus_score.html#sklearn.metrics.consensus_score" title="sklearn.metrics.consensus_score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">consensus_score</span></code></a></p></td>
<td><p>The similarity of two sets of biclusters.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="distance-metrics">
<span id="metrics-ref-distance-metrics"></span><h2>Distance metrics<a class="headerlink" href="#distance-metrics" title="Link to this heading">#</a></h2>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.DistanceMetric.html#sklearn.metrics.DistanceMetric" title="sklearn.metrics.DistanceMetric"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DistanceMetric</span></code></a></p></td>
<td><p>Uniform interface for fast distance metric functions.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="module-sklearn.metrics.pairwise">
<span id="pairwise-metrics"></span><span id="metrics-ref-pairwise-metrics"></span><h2>Pairwise metrics<a class="headerlink" href="#module-sklearn.metrics.pairwise" title="Link to this heading">#</a></h2>
<p>Metrics for pairwise distances and affinity of sets of samples.</p>
<p><strong>User guide.</strong> See the <a class="reference internal" href="../modules/metrics.html#metrics"><span class="std std-ref">Pairwise metrics, Affinities and Kernels</span></a> section for further details.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.additive_chi2_kernel.html#sklearn.metrics.pairwise.additive_chi2_kernel" title="sklearn.metrics.pairwise.additive_chi2_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.additive_chi2_kernel</span></code></a></p></td>
<td><p>Compute the additive chi-squared kernel between observations in X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.chi2_kernel.html#sklearn.metrics.pairwise.chi2_kernel" title="sklearn.metrics.pairwise.chi2_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.chi2_kernel</span></code></a></p></td>
<td><p>Compute the exponential chi-squared kernel between X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.cosine_distances.html#sklearn.metrics.pairwise.cosine_distances" title="sklearn.metrics.pairwise.cosine_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.cosine_distances</span></code></a></p></td>
<td><p>Compute cosine distance between samples in X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.cosine_similarity.html#sklearn.metrics.pairwise.cosine_similarity" title="sklearn.metrics.pairwise.cosine_similarity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.cosine_similarity</span></code></a></p></td>
<td><p>Compute cosine similarity between samples in X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.distance_metrics.html#sklearn.metrics.pairwise.distance_metrics" title="sklearn.metrics.pairwise.distance_metrics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.distance_metrics</span></code></a></p></td>
<td><p>Valid metrics for pairwise_distances.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.euclidean_distances.html#sklearn.metrics.pairwise.euclidean_distances" title="sklearn.metrics.pairwise.euclidean_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.euclidean_distances</span></code></a></p></td>
<td><p>Compute the distance matrix between each pair from a vector array X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.haversine_distances.html#sklearn.metrics.pairwise.haversine_distances" title="sklearn.metrics.pairwise.haversine_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.haversine_distances</span></code></a></p></td>
<td><p>Compute the Haversine distance between samples in X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.kernel_metrics.html#sklearn.metrics.pairwise.kernel_metrics" title="sklearn.metrics.pairwise.kernel_metrics"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.kernel_metrics</span></code></a></p></td>
<td><p>Valid metrics for pairwise_kernels.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.laplacian_kernel.html#sklearn.metrics.pairwise.laplacian_kernel" title="sklearn.metrics.pairwise.laplacian_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.laplacian_kernel</span></code></a></p></td>
<td><p>Compute the laplacian kernel between X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.linear_kernel.html#sklearn.metrics.pairwise.linear_kernel" title="sklearn.metrics.pairwise.linear_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.linear_kernel</span></code></a></p></td>
<td><p>Compute the linear kernel between X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.manhattan_distances.html#sklearn.metrics.pairwise.manhattan_distances" title="sklearn.metrics.pairwise.manhattan_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.manhattan_distances</span></code></a></p></td>
<td><p>Compute the L1 distances between the vectors in X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html#sklearn.metrics.pairwise.nan_euclidean_distances" title="sklearn.metrics.pairwise.nan_euclidean_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.nan_euclidean_distances</span></code></a></p></td>
<td><p>Calculate the euclidean distances in the presence of missing values.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.paired_cosine_distances.html#sklearn.metrics.pairwise.paired_cosine_distances" title="sklearn.metrics.pairwise.paired_cosine_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.paired_cosine_distances</span></code></a></p></td>
<td><p>Compute the paired cosine distances between X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.paired_distances.html#sklearn.metrics.pairwise.paired_distances" title="sklearn.metrics.pairwise.paired_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.paired_distances</span></code></a></p></td>
<td><p>Compute the paired distances between X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.paired_euclidean_distances.html#sklearn.metrics.pairwise.paired_euclidean_distances" title="sklearn.metrics.pairwise.paired_euclidean_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.paired_euclidean_distances</span></code></a></p></td>
<td><p>Compute the paired euclidean distances between X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.paired_manhattan_distances.html#sklearn.metrics.pairwise.paired_manhattan_distances" title="sklearn.metrics.pairwise.paired_manhattan_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.paired_manhattan_distances</span></code></a></p></td>
<td><p>Compute the paired L1 distances between X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.pairwise_kernels.html#sklearn.metrics.pairwise.pairwise_kernels" title="sklearn.metrics.pairwise.pairwise_kernels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.pairwise_kernels</span></code></a></p></td>
<td><p>Compute the kernel between arrays X and optional array Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html#sklearn.metrics.pairwise.polynomial_kernel" title="sklearn.metrics.pairwise.polynomial_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.polynomial_kernel</span></code></a></p></td>
<td><p>Compute the polynomial kernel between X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.rbf_kernel.html#sklearn.metrics.pairwise.rbf_kernel" title="sklearn.metrics.pairwise.rbf_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.rbf_kernel</span></code></a></p></td>
<td><p>Compute the rbf (gaussian) kernel between X and Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise.sigmoid_kernel.html#sklearn.metrics.pairwise.sigmoid_kernel" title="sklearn.metrics.pairwise.sigmoid_kernel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise.sigmoid_kernel</span></code></a></p></td>
<td><p>Compute the sigmoid kernel between X and Y.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise_distances.html#sklearn.metrics.pairwise_distances" title="sklearn.metrics.pairwise_distances"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise_distances</span></code></a></p></td>
<td><p>Compute the distance matrix from a vector array X and optional Y.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise_distances_argmin.html#sklearn.metrics.pairwise_distances_argmin" title="sklearn.metrics.pairwise_distances_argmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise_distances_argmin</span></code></a></p></td>
<td><p>Compute minimum distances between one point and a set of points.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise_distances_argmin_min.html#sklearn.metrics.pairwise_distances_argmin_min" title="sklearn.metrics.pairwise_distances_argmin_min"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise_distances_argmin_min</span></code></a></p></td>
<td><p>Compute minimum distances between one point and a set of points.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.pairwise_distances_chunked.html#sklearn.metrics.pairwise_distances_chunked" title="sklearn.metrics.pairwise_distances_chunked"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pairwise_distances_chunked</span></code></a></p></td>
<td><p>Generate a distance matrix chunk by chunk with optional reduction.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="plotting">
<span id="metrics-ref-plotting"></span><h2>Plotting<a class="headerlink" href="#plotting" title="Link to this heading">#</a></h2>
<p><strong>User guide.</strong> See the <a class="reference internal" href="../visualizations.html#visualizations"><span class="std std-ref">Visualizations</span></a> section for further details.</p>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.ConfusionMatrixDisplay.html#sklearn.metrics.ConfusionMatrixDisplay" title="sklearn.metrics.ConfusionMatrixDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConfusionMatrixDisplay</span></code></a></p></td>
<td><p>Confusion Matrix visualization.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.DetCurveDisplay.html#sklearn.metrics.DetCurveDisplay" title="sklearn.metrics.DetCurveDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DetCurveDisplay</span></code></a></p></td>
<td><p>DET curve visualization.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.PrecisionRecallDisplay.html#sklearn.metrics.PrecisionRecallDisplay" title="sklearn.metrics.PrecisionRecallDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PrecisionRecallDisplay</span></code></a></p></td>
<td><p>Precision Recall visualization.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.PredictionErrorDisplay.html#sklearn.metrics.PredictionErrorDisplay" title="sklearn.metrics.PredictionErrorDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">PredictionErrorDisplay</span></code></a></p></td>
<td><p>Visualization of the prediction error of a regression model.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay" title="sklearn.metrics.RocCurveDisplay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RocCurveDisplay</span></code></a></p></td>
<td><p>ROC Curve visualization.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
</section>


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