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@ -561,330 +561,143 @@ Make a scorer from a performance metric or loss function.
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|---|---| |---|---|
metrics.accuracy_score(y_true, y_pred, *[, ...]) |metrics.accuracy_score(y_true, y_pred, *[, ...])|Accuracy classification score.|
Accuracy classification score. |metrics.auc(x, y)|Compute Area Under the Curve (AUC) using the trapezoidal rule.|
|metrics.average_precision_score(y_true, ...)|Compute average precision (AP) from prediction scores.|
metrics.auc(x, y) |metrics.balanced_accuracy_score(y_true, ...)|Compute the balanced accuracy.|
Compute Area Under the Curve (AUC) using the trapezoidal rule. |metrics.brier_score_loss(y_true, y_prob, *)|Compute the Brier score loss.|
|metrics.class_likelihood_ratios(y_true, ...)|Compute binary classification positive and negative likelihood ratios.|
metrics.average_precision_score(y_true, ...) |metrics.classification_report(y_true, y_pred, *)|Build a text report showing the main classification metrics.|
Compute average precision (AP) from prediction scores. |metrics.cohen_kappa_score(y1, y2, *[, ...])|Compute Cohen's kappa: a statistic that measures inter-annotator agreement.|
|metrics.confusion_matrix(y_true, y_pred, *)|Compute confusion matrix to evaluate the accuracy of a classification.|
metrics.balanced_accuracy_score(y_true, ...) |metrics.dcg_score(y_true, y_score, *[, k, ...])|Compute Discounted Cumulative Gain.|
Compute the balanced accuracy. |metrics.det_curve(y_true, y_score[, ...])|Compute error rates for different probability thresholds.|
|metrics.f1_score(y_true, y_pred, *[, ...])|Compute the F1 score, also known as balanced F-score or F-measure.|
metrics.brier_score_loss(y_true, y_prob, *) |metrics.fbeta_score(y_true, y_pred, *, beta)|Compute the F-beta score.|
Compute the Brier score loss. |metrics.hamming_loss(y_true, y_pred, *[, ...])|Compute the average Hamming loss.|
|metrics.hinge_loss(y_true, pred_decision, *)|Average hinge loss (non-regularized).|
metrics.class_likelihood_ratios(y_true, ...) |metrics.jaccard_score(y_true, y_pred, *[, ...])|Jaccard similarity coefficient score.|
Compute binary classification positive and negative likelihood ratios. |metrics.log_loss(y_true, y_pred, *[, eps, ...])|Log loss, aka logistic loss or cross-entropy loss.|
|metrics.matthews_corrcoef(y_true, y_pred, *)|Compute the Matthews correlation coefficient (MCC).|
metrics.classification_report(y_true, y_pred, *) |metrics.multilabel_confusion_matrix(y_true, ...)|Compute a confusion matrix for each class or sample.|
Build a text report showing the main classification metrics. |metrics.ndcg_score(y_true, y_score, *[, k, ...])|Compute Normalized Discounted Cumulative Gain.|
|metrics.precision_recall_curve(y_true, ...)|Compute precision-recall pairs for different probability thresholds.|
metrics.cohen_kappa_score(y1, y2, *[, ...]) |metrics.precision_recall_fscore_support(...)|Compute precision, recall, F-measure and support for each class.|
Compute Cohen's kappa: a statistic that measures inter-annotator agreement. |metrics.precision_score(y_true, y_pred, *[, ...])|Compute the precision.|
|metrics.recall_score(y_true, y_pred, *[, ...])|Compute the recall.|
metrics.confusion_matrix(y_true, y_pred, *) |metrics.roc_auc_score(y_true, y_score, *[, ...])|Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.|
Compute confusion matrix to evaluate the accuracy of a classification. |metrics.roc_curve(y_true, y_score, *[, ...])|Compute Receiver operating characteristic (ROC).|
|metrics.top_k_accuracy_score(y_true, y_score, *)|Top-k Accuracy classification score.|
metrics.dcg_score(y_true, y_score, *[, k, ...]) |metrics.zero_one_loss(y_true, y_pred, *[, ...])|Zero-one classification loss.|
Compute Discounted Cumulative Gain.
metrics.det_curve(y_true, y_score[, ...])
Compute error rates for different probability thresholds.
metrics.f1_score(y_true, y_pred, *[, ...])
Compute the F1 score, also known as balanced F-score or F-measure.
metrics.fbeta_score(y_true, y_pred, *, beta)
Compute the F-beta score.
metrics.hamming_loss(y_true, y_pred, *[, ...])
Compute the average Hamming loss.
metrics.hinge_loss(y_true, pred_decision, *)
Average hinge loss (non-regularized).
metrics.jaccard_score(y_true, y_pred, *[, ...])
Jaccard similarity coefficient score.
metrics.log_loss(y_true, y_pred, *[, eps, ...])
Log loss, aka logistic loss or cross-entropy loss.
metrics.matthews_corrcoef(y_true, y_pred, *)
Compute the Matthews correlation coefficient (MCC).
metrics.multilabel_confusion_matrix(y_true, ...)
Compute a confusion matrix for each class or sample.
metrics.ndcg_score(y_true, y_score, *[, k, ...])
Compute Normalized Discounted Cumulative Gain.
metrics.precision_recall_curve(y_true, ...)
Compute precision-recall pairs for different probability thresholds.
metrics.precision_recall_fscore_support(...)
Compute precision, recall, F-measure and support for each class.
metrics.precision_score(y_true, y_pred, *[, ...])
Compute the precision.
metrics.recall_score(y_true, y_pred, *[, ...])
Compute the recall.
metrics.roc_auc_score(y_true, y_score, *[, ...])
Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
metrics.roc_curve(y_true, y_score, *[, ...])
Compute Receiver operating characteristic (ROC).
metrics.top_k_accuracy_score(y_true, y_score, *)
Top-k Accuracy classification score.
metrics.zero_one_loss(y_true, y_pred, *[, ...])
Zero-one classification loss.
### Regression metrics ### Regression metrics
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|---|---| |---|---|
metrics.explained_variance_score(y_true, ...) |metrics.explained_variance_score(y_true, ...)|Explained variance regression score function.|
Explained variance regression score function. |metrics.max_error(y_true, y_pred)|The max_error metric calculates the maximum residual error.|
|metrics.mean_absolute_error(y_true, y_pred, *)|Mean absolute error regression loss.|
metrics.max_error(y_true, y_pred) |metrics.mean_squared_error(y_true, y_pred, *)|Mean squared error regression loss.|
The max_error metric calculates the maximum residual error. |metrics.mean_squared_log_error(y_true, y_pred, *)|Mean squared logarithmic error regression loss.|
|metrics.median_absolute_error(y_true, y_pred, *)|Median absolute error regression loss.|
metrics.mean_absolute_error(y_true, y_pred, *) |metrics.mean_absolute_percentage_error(...)|Mean absolute percentage error (MAPE) regression loss.|
Mean absolute error regression loss. |metrics.r2_score(y_true, y_pred, *[, ...])|R^2 (coefficient of determination) regression score function.|
|metrics.mean_poisson_deviance(y_true, y_pred, *)|Mean Poisson deviance regression loss.|
metrics.mean_squared_error(y_true, y_pred, *) |metrics.mean_gamma_deviance(y_true, y_pred, *)|Mean Gamma deviance regression loss.|
Mean squared error regression loss. |metrics.mean_tweedie_deviance(y_true, y_pred, *)|Mean Tweedie deviance regression loss.|
|metrics.d2_tweedie_score(y_true, y_pred, *)|D^2 regression score function, fraction of Tweedie deviance explained.|
metrics.mean_squared_log_error(y_true, y_pred, *) |metrics.mean_pinball_loss(y_true, y_pred, *)|Pinball loss for quantile regression.|
Mean squared logarithmic error regression loss. |metrics.d2_pinball_score(y_true, y_pred, *)|D^2 regression score function, fraction of pinball loss explained.|
|metrics.d2_absolute_error_score(y_true, ...)|D^2 regression score function, fraction of absolute error explained.|
metrics.median_absolute_error(y_true, y_pred, *)
Median absolute error regression loss.
metrics.mean_absolute_percentage_error(...)
Mean absolute percentage error (MAPE) regression loss.
metrics.r2_score(y_true, y_pred, *[, ...])
R^2 (coefficient of determination) regression score function.
metrics.mean_poisson_deviance(y_true, y_pred, *)
Mean Poisson deviance regression loss.
metrics.mean_gamma_deviance(y_true, y_pred, *)
Mean Gamma deviance regression loss.
metrics.mean_tweedie_deviance(y_true, y_pred, *)
Mean Tweedie deviance regression loss.
metrics.d2_tweedie_score(y_true, y_pred, *)
D^2 regression score function, fraction of Tweedie deviance explained.
metrics.mean_pinball_loss(y_true, y_pred, *)
Pinball loss for quantile regression.
metrics.d2_pinball_score(y_true, y_pred, *)
D^2 regression score function, fraction of pinball loss explained.
metrics.d2_absolute_error_score(y_true, ...)
D^2 regression score function, fraction of absolute error explained.
### Multilabel ranking metrics ### Multilabel ranking metrics
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|---|---| |---|---|
metrics.coverage_error(y_true, y_score, *[, ...]) |metrics.coverage_error(y_true, y_score, *[, ...])|Coverage error measure.|
Coverage error measure. |metrics.label_ranking_average_precision_score(...)|Compute ranking-based average precision.|
|metrics.label_ranking_loss(y_true, y_score, *)|Compute Ranking loss measure.|
metrics.label_ranking_average_precision_score(...)
Compute ranking-based average precision.
metrics.label_ranking_loss(y_true, y_score, *)
Compute Ranking loss measure.
### Clustering metrics ### Clustering metrics
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|---|---| |---|---|
metrics.adjusted_mutual_info_score(...[, ...]) |metrics.adjusted_mutual_info_score(...[, ...])|Adjusted Mutual Information between two clusterings.|
Adjusted Mutual Information between two clusterings. |metrics.adjusted_rand_score(labels_true, ...)|Rand index adjusted for chance.|
|metrics.calinski_harabasz_score(X, labels)|Compute the Calinski and Harabasz score.|
metrics.adjusted_rand_score(labels_true, ...) |metrics.davies_bouldin_score(X, labels)|Compute the Davies-Bouldin score.|
Rand index adjusted for chance. |metrics.completeness_score(labels_true, ...)|Compute completeness metric of a cluster labeling given a ground truth.|
|metrics.cluster.contingency_matrix(...[, ...])|Build a contingency matrix describing the relationship between labels.|
metrics.calinski_harabasz_score(X, labels) |metrics.cluster.pair_confusion_matrix(...)|Pair confusion matrix arising from two clusterings [R9ca8fd06d29a-1].|
Compute the Calinski and Harabasz score. |metrics.fowlkes_mallows_score(labels_true, ...)|Measure the similarity of two clusterings of a set of points.|
|metrics.homogeneity_completeness_v_measure(...)|Compute the homogeneity and completeness and V-Measure scores at once.|
metrics.davies_bouldin_score(X, labels) |metrics.homogeneity_score(labels_true, ...)|Homogeneity metric of a cluster labeling given a ground truth.|
Compute the Davies-Bouldin score. |metrics.mutual_info_score(labels_true, ...)|Mutual Information between two clusterings.|
|metrics.normalized_mutual_info_score(...[, ...])|Normalized Mutual Information between two clusterings.|
metrics.completeness_score(labels_true, ...) |metrics.rand_score(labels_true, labels_pred)|Rand index.|
Compute completeness metric of a cluster labeling given a ground truth. |metrics.silhouette_score(X, labels, *[, ...])|Compute the mean Silhouette Coefficient of all samples.|
|metrics.silhouette_samples(X, labels, *[, ...])|Compute the Silhouette Coefficient for each sample.|
metrics.cluster.contingency_matrix(...[, ...]) |metrics.v_measure_score(labels_true, ...[, beta])|V-measure cluster labeling given a ground truth.|
Build a contingency matrix describing the relationship between labels.
metrics.cluster.pair_confusion_matrix(...)
Pair confusion matrix arising from two clusterings [R9ca8fd06d29a-1].
metrics.fowlkes_mallows_score(labels_true, ...)
Measure the similarity of two clusterings of a set of points.
metrics.homogeneity_completeness_v_measure(...)
Compute the homogeneity and completeness and V-Measure scores at once.
metrics.homogeneity_score(labels_true, ...)
Homogeneity metric of a cluster labeling given a ground truth.
metrics.mutual_info_score(labels_true, ...)
Mutual Information between two clusterings.
metrics.normalized_mutual_info_score(...[, ...])
Normalized Mutual Information between two clusterings.
metrics.rand_score(labels_true, labels_pred)
Rand index.
metrics.silhouette_score(X, labels, *[, ...])
Compute the mean Silhouette Coefficient of all samples.
metrics.silhouette_samples(X, labels, *[, ...])
Compute the Silhouette Coefficient for each sample.
metrics.v_measure_score(labels_true, ...[, beta])
V-measure cluster labeling given a ground truth.
### Biclustering metrics ### Biclustering metrics
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|---|---| |---|---|
metrics.consensus_score(a, b, *[, similarity]) |metrics.consensus_score(a, b, *[, similarity])|The similarity of two sets of biclusters.|
The similarity of two sets of biclusters.
### Distance metrics ### Distance metrics
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|---|---| |---|---|
metrics.DistanceMetric |metrics.DistanceMetric|Uniform interface for fast distance metric functions.|
Uniform interface for fast distance metric functions.
### Pairwise metrics ### Pairwise metrics
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|---|---| |---|---|
metrics.pairwise.additive_chi2_kernel(X[, Y]) |metrics.pairwise.additive_chi2_kernel(X[, Y])|Compute the additive chi-squared kernel between observations in X and Y.|
Compute the additive chi-squared kernel between observations in X and Y. |metrics.pairwise.chi2_kernel(X[, Y, gamma])|Compute the exponential chi-squared kernel between X and Y.|
|metrics.pairwise.cosine_similarity(X[, Y, ...])|Compute cosine similarity between samples in X and Y.|
metrics.pairwise.chi2_kernel(X[, Y, gamma]) |metrics.pairwise.cosine_distances(X[, Y])|Compute cosine distance between samples in X and Y.|
Compute the exponential chi-squared kernel between X and Y. |metrics.pairwise.distance_metrics()|Valid metrics for pairwise_distances.|
|metrics.pairwise.euclidean_distances(X[, Y, ...])|Compute the distance matrix between each pair from a vector array X and Y.|
metrics.pairwise.cosine_similarity(X[, Y, ...]) |metrics.pairwise.haversine_distances(X[, Y])|Compute the Haversine distance between samples in X and Y.|
Compute cosine similarity between samples in X and Y. |metrics.pairwise.kernel_metrics()|Valid metrics for pairwise_kernels.|
|metrics.pairwise.laplacian_kernel(X[, Y, gamma])Compute the laplacian kernel between X and Y.|
metrics.pairwise.cosine_distances(X[, Y]) |metrics.pairwise.linear_kernel(X[, Y, ...])|Compute the linear kernel between X and Y.|
Compute cosine distance between samples in X and Y. |metrics.pairwise.manhattan_distances(X[, Y, ...])|Compute the L1 distances between the vectors in X and Y.|
|metrics.pairwise.nan_euclidean_distances(X)|Calculate the euclidean distances in the presence of missing values.|
metrics.pairwise.distance_metrics() |metrics.pairwise.pairwise_kernels(X[, Y, ...])|Compute the kernel between arrays X and optional array Y.|
Valid metrics for pairwise_distances. |metrics.pairwise.polynomial_kernel(X[, Y, ...])|Compute the polynomial kernel between X and Y.|
|metrics.pairwise.rbf_kernel(X[, Y, gamma])|Compute the rbf (gaussian) kernel between X and Y.|
metrics.pairwise.euclidean_distances(X[, Y, ...]) |metrics.pairwise.sigmoid_kernel(X[, Y, ...])|Compute the sigmoid kernel between X and Y.|
Compute the distance matrix between each pair from a vector array X and Y. |metrics.pairwise.paired_euclidean_distances(X, Y)|Compute the paired euclidean distances between X and Y.|
|metrics.pairwise.paired_manhattan_distances(X, Y)|Compute the paired L1 distances between X and Y.|
metrics.pairwise.haversine_distances(X[, Y]) |metrics.pairwise.paired_cosine_distances(X, Y)|Compute the paired cosine distances between X and Y.|
Compute the Haversine distance between samples in X and Y. |metrics.pairwise.paired_distances(X, Y, *[, ...])|Compute the paired distances between X and Y.|
|metrics.pairwise_distances(X[, Y, metric, ...])|Compute the distance matrix from a vector array X and optional Y.|
metrics.pairwise.kernel_metrics() |metrics.pairwise_distances_argmin(X, Y, *[, ...])|Compute minimum distances between one point and a set of points.|
Valid metrics for pairwise_kernels. |metrics.pairwise_distances_argmin_min(X, Y, *)|Compute minimum distances between one point and a set of points.|
|metrics.pairwise_distances_chunked(X[, Y, ...])|Generate a distance matrix chunk by chunk with optional reduction.|
metrics.pairwise.laplacian_kernel(X[, Y, gamma])
Compute the laplacian kernel between X and Y.
metrics.pairwise.linear_kernel(X[, Y, ...])
Compute the linear kernel between X and Y.
metrics.pairwise.manhattan_distances(X[, Y, ...])
Compute the L1 distances between the vectors in X and Y.
metrics.pairwise.nan_euclidean_distances(X)
Calculate the euclidean distances in the presence of missing values.
metrics.pairwise.pairwise_kernels(X[, Y, ...])
Compute the kernel between arrays X and optional array Y.
metrics.pairwise.polynomial_kernel(X[, Y, ...])
Compute the polynomial kernel between X and Y.
metrics.pairwise.rbf_kernel(X[, Y, gamma])
Compute the rbf (gaussian) kernel between X and Y.
metrics.pairwise.sigmoid_kernel(X[, Y, ...])
Compute the sigmoid kernel between X and Y.
metrics.pairwise.paired_euclidean_distances(X, Y)
Compute the paired euclidean distances between X and Y.
metrics.pairwise.paired_manhattan_distances(X, Y)
Compute the paired L1 distances between X and Y.
metrics.pairwise.paired_cosine_distances(X, Y)
Compute the paired cosine distances between X and Y.
metrics.pairwise.paired_distances(X, Y, *[, ...])
Compute the paired distances between X and Y.
metrics.pairwise_distances(X[, Y, metric, ...])
Compute the distance matrix from a vector array X and optional Y.
metrics.pairwise_distances_argmin(X, Y, *[, ...])
Compute minimum distances between one point and a set of points.
metrics.pairwise_distances_argmin_min(X, Y, *)
Compute minimum distances between one point and a set of points.
metrics.pairwise_distances_chunked(X[, Y, ...])
Generate a distance matrix chunk by chunk with optional reduction.
### Plotting ### Plotting
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|---|---| |---|---|
metrics.ConfusionMatrixDisplay(...[, ...]) |metrics.ConfusionMatrixDisplay(...[, ...])|Confusion Matrix visualization.|
Confusion Matrix visualization. |metrics.DetCurveDisplay(*, fpr, fnr[, ...])|DET curve visualization.|
|metrics.PrecisionRecallDisplay(precision, ...)|Precision Recall visualization.|
metrics.DetCurveDisplay(*, fpr, fnr[, ...]) |metrics.PredictionErrorDisplay(*, y_true, y_pred)|Visualization of the prediction error of a regression model.|
DET curve visualization. |metrics.RocCurveDisplay(*, fpr, tpr[, ...])|ROC Curve visualization.|
|calibration.CalibrationDisplay(prob_true, ...)|Calibration curve (also known as reliability diagram) visualization.|
metrics.PrecisionRecallDisplay(precision, ...)
Precision Recall visualization.
metrics.PredictionErrorDisplay(*, y_true, y_pred)
Visualization of the prediction error of a regression model.
metrics.RocCurveDisplay(*, fpr, tpr[, ...])
ROC Curve visualization.
calibration.CalibrationDisplay(prob_true, ...)
Calibration curve (also known as reliability diagram) visualization.
## [sklearn.mixture: Gaussian Mixture Models](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.mixture) ## [sklearn.mixture: Gaussian Mixture Models](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.mixture)
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|---|---| |---|---|
mixture.BayesianGaussianMixture(*[, ...]) |mixture.BayesianGaussianMixture(*[, ...])|Variational Bayesian estimation of a Gaussian mixture.|
Variational Bayesian estimation of a Gaussian mixture. |mixture.GaussianMixture([n_components, ...])|Gaussian Mixture.|
mixture.GaussianMixture([n_components, ...])
Gaussian Mixture.
## [sklearn.model_selection: Model Selection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection) ## [sklearn.model_selection: Model Selection](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection)
@ -892,50 +705,21 @@ Gaussian Mixture.
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|---|---| |---|---|
model_selection.GroupKFold([n_splits]) |model_selection.GroupKFold([n_splits])|K-fold iterator variant with non-overlapping groups.|
K-fold iterator variant with non-overlapping groups. |model_selection.GroupShuffleSplit([...])|Shuffle-Group(s)-Out cross-validation iterator|
|model_selection.KFold([n_splits, shuffle, ...])|K-Folds cross-validator|
model_selection.GroupShuffleSplit([...]) |model_selection.LeaveOneGroupOut()|Leave One Group Out cross-validator|
Shuffle-Group(s)-Out cross-validation iterator |model_selection.LeavePGroupsOut(n_groups)|Leave P Group(s) Out cross-validator|
|model_selection.LeaveOneOut()|Leave-One-Out cross-validator|
model_selection.KFold([n_splits, shuffle, ...]) |model_selection.LeavePOut(p)|Leave-P-Out cross-validator|
K-Folds cross-validator |model_selection.PredefinedSplit(test_fold)|Predefined split cross-validator|
|model_selection.RepeatedKFold(*[, n_splits, ...])|Repeated K-Fold cross validator.|
model_selection.LeaveOneGroupOut() |model_selection.RepeatedStratifiedKFold(*[, ...])|Repeated Stratified K-Fold cross validator.|
Leave One Group Out cross-validator |model_selection.ShuffleSplit([n_splits, ...])|Random permutation cross-validator|
|model_selection.StratifiedKFold([n_splits, ...])|Stratified K-Folds cross-validator.|
model_selection.LeavePGroupsOut(n_groups) |model_selection.StratifiedShuffleSplit([...])|Stratified ShuffleSplit cross-validator|
Leave P Group(s) Out cross-validator |model_selection.StratifiedGroupKFold([...])|Stratified K-Folds iterator variant with non-overlapping groups.|
|model_selection.TimeSeriesSplit([n_splits, ...])|Time Series cross-validator|
model_selection.LeaveOneOut()
Leave-One-Out cross-validator
model_selection.LeavePOut(p)
Leave-P-Out cross-validator
model_selection.PredefinedSplit(test_fold)
Predefined split cross-validator
model_selection.RepeatedKFold(*[, n_splits, ...])
Repeated K-Fold cross validator.
model_selection.RepeatedStratifiedKFold(*[, ...])
Repeated Stratified K-Fold cross validator.
model_selection.ShuffleSplit([n_splits, ...])
Random permutation cross-validator
model_selection.StratifiedKFold([n_splits, ...])
Stratified K-Folds cross-validator.
model_selection.StratifiedShuffleSplit([...])
Stratified ShuffleSplit cross-validator
model_selection.StratifiedGroupKFold([...])
Stratified K-Folds iterator variant with non-overlapping groups.
model_selection.TimeSeriesSplit([n_splits, ...])
Time Series cross-validator
### Splitter Functions ### Splitter Functions