sklearn.metrics

Score functions, performance metrics, pairwise metrics and distance computations.

User guide. See the Metrics and scoring: quantifying the quality of predictions and Pairwise metrics, Affinities and Kernels sections for further details.

Model selection interface

User guide. See the The scoring parameter: defining model evaluation rules section for further details.

check_scoring	Determine scorer from user options.
get_scorer	Get a scorer from string.
get_scorer_names	Get the names of all available scorers.
make_scorer	Make a scorer from a performance metric or loss function.

Classification metrics

User guide. See the Classification metrics section for further details.

accuracy_score	Accuracy classification score.
auc	Compute Area Under the Curve (AUC) using the trapezoidal rule.
average_precision_score	Compute average precision (AP) from prediction scores.
balanced_accuracy_score	Compute the balanced accuracy.
brier_score_loss	Compute the Brier score loss.
class_likelihood_ratios	Compute binary classification positive and negative likelihood ratios.
classification_report	Build a text report showing the main classification metrics.
cohen_kappa_score	Compute Cohen's kappa: a statistic that measures inter-annotator agreement.
confusion_matrix	Compute confusion matrix to evaluate the accuracy of a classification.
d2_log_loss_score	\(D^2\) score function, fraction of log loss explained.
dcg_score	Compute Discounted Cumulative Gain.
det_curve	Compute error rates for different probability thresholds.
f1_score	Compute the f1 score, also known as balanced f-score or f-measure.
fbeta_score	Compute the f-beta score.
hamming_loss	Compute the average Hamming loss.
hinge_loss	Average hinge loss (non-regularized).
jaccard_score	Jaccard similarity coefficient score.
log_loss	Log loss, aka logistic loss or cross-entropy loss.
matthews_corrcoef	Compute the Matthews correlation coefficient (MCC).
multilabel_confusion_matrix	Compute a confusion matrix for each class or sample.
ndcg_score	Compute Normalized Discounted Cumulative Gain.
precision_recall_curve	Compute precision-recall pairs for different probability thresholds.
precision_recall_fscore_support	Compute precision, recall, f-measure and support for each class.
precision_score	Compute the precision.
recall_score	Compute the recall.
roc_auc_score	Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
roc_curve	Compute Receiver operating characteristic (ROC).
top_k_accuracy_score	Top-k Accuracy classification score.
zero_one_loss	Zero-one classification loss.

Regression metrics

User guide. See the Regression metrics section for further details.

d2_absolute_error_score	\(D^2\) regression score function, fraction of absolute error explained.
d2_pinball_score	\(D^2\) regression score function, fraction of pinball loss explained.
d2_tweedie_score	\(D^2\) regression score function, fraction of Tweedie deviance explained.
explained_variance_score	Explained variance regression score function.
max_error	The max_error metric calculates the maximum residual error.
mean_absolute_error	Mean absolute error regression loss.
mean_absolute_percentage_error	Mean absolute percentage error (MAPE) regression loss.
mean_gamma_deviance	Mean Gamma deviance regression loss.
mean_pinball_loss	Pinball loss for quantile regression.
mean_poisson_deviance	Mean Poisson deviance regression loss.
mean_squared_error	Mean squared error regression loss.
mean_squared_log_error	Mean squared logarithmic error regression loss.
mean_tweedie_deviance	Mean Tweedie deviance regression loss.
median_absolute_error	Median absolute error regression loss.
r2_score	\(R^2\) (coefficient of determination) regression score function.
root_mean_squared_error	Root mean squared error regression loss.
root_mean_squared_log_error	Root mean squared logarithmic error regression loss.

Multilabel ranking metrics

User guide. See the Multilabel ranking metrics section for further details.

coverage_error	Coverage error measure.
label_ranking_average_precision_score	Compute ranking-based average precision.
label_ranking_loss	Compute Ranking loss measure.

Clustering metrics

Evaluation metrics for cluster analysis results.

• Supervised evaluation uses a ground truth class values for each sample.

• Unsupervised evaluation does use ground truths and measures the “quality” of the model itself.

User guide. See the Clustering performance evaluation section for further details.

adjusted_mutual_info_score	Adjusted Mutual Information between two clusterings.
adjusted_rand_score	Rand index adjusted for chance.
calinski_harabasz_score	Compute the Calinski and Harabasz score.
cluster.contingency_matrix	Build a contingency matrix describing the relationship between labels.
cluster.pair_confusion_matrix	Pair confusion matrix arising from two clusterings.
completeness_score	Compute completeness metric of a cluster labeling given a ground truth.
davies_bouldin_score	Compute the Davies-Bouldin score.
fowlkes_mallows_score	Measure the similarity of two clusterings of a set of points.
homogeneity_completeness_v_measure	Compute the homogeneity and completeness and V-Measure scores at once.
homogeneity_score	Homogeneity metric of a cluster labeling given a ground truth.
mutual_info_score	Mutual Information between two clusterings.
normalized_mutual_info_score	Normalized Mutual Information between two clusterings.
rand_score	Rand index.
silhouette_samples	Compute the Silhouette Coefficient for each sample.
silhouette_score	Compute the mean Silhouette Coefficient of all samples.
v_measure_score	V-measure cluster labeling given a ground truth.

Biclustering metrics

User guide. See the Biclustering evaluation section for further details.

consensus_score

The similarity of two sets of biclusters.

Distance metrics

DistanceMetric

Uniform interface for fast distance metric functions.

Pairwise metrics

Metrics for pairwise distances and affinity of sets of samples.

User guide. See the Pairwise metrics, Affinities and Kernels section for further details.

pairwise.additive_chi2_kernel	Compute the additive chi-squared kernel between observations in X and Y.
pairwise.chi2_kernel	Compute the exponential chi-squared kernel between X and Y.
pairwise.cosine_distances	Compute cosine distance between samples in X and Y.
pairwise.cosine_similarity	Compute cosine similarity between samples in X and Y.
pairwise.distance_metrics	Valid metrics for pairwise_distances.
pairwise.euclidean_distances	Compute the distance matrix between each pair from a vector array X and Y.
pairwise.haversine_distances	Compute the Haversine distance between samples in X and Y.
pairwise.kernel_metrics	Valid metrics for pairwise_kernels.
pairwise.laplacian_kernel	Compute the laplacian kernel between X and Y.
pairwise.linear_kernel	Compute the linear kernel between X and Y.
pairwise.manhattan_distances	Compute the L1 distances between the vectors in X and Y.
pairwise.nan_euclidean_distances	Calculate the euclidean distances in the presence of missing values.
pairwise.paired_cosine_distances	Compute the paired cosine distances between X and Y.
pairwise.paired_distances	Compute the paired distances between X and Y.
pairwise.paired_euclidean_distances	Compute the paired euclidean distances between X and Y.
pairwise.paired_manhattan_distances	Compute the paired L1 distances between X and Y.
pairwise.pairwise_kernels	Compute the kernel between arrays X and optional array Y.
pairwise.polynomial_kernel	Compute the polynomial kernel between X and Y.
pairwise.rbf_kernel	Compute the rbf (gaussian) kernel between X and Y.
pairwise.sigmoid_kernel	Compute the sigmoid kernel between X and Y.
pairwise_distances	Compute the distance matrix from a vector array X and optional Y.
pairwise_distances_argmin	Compute minimum distances between one point and a set of points.
pairwise_distances_argmin_min	Compute minimum distances between one point and a set of points.
pairwise_distances_chunked	Generate a distance matrix chunk by chunk with optional reduction.

Plotting

User guide. See the Visualizations section for further details.

ConfusionMatrixDisplay	Confusion Matrix visualization.
DetCurveDisplay	DET curve visualization.
PrecisionRecallDisplay	Precision Recall visualization.
PredictionErrorDisplay	Visualization of the prediction error of a regression model.
RocCurveDisplay	ROC Curve visualization.