Update README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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David Rotermund
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1 changed files with 111 additions and 327 deletions
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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, *[, ...])
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Accuracy classification score.
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metrics.auc(x, y)
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Compute Area Under the Curve (AUC) using the trapezoidal rule.
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metrics.average_precision_score(y_true, ...)
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Compute average precision (AP) from prediction scores.
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metrics.balanced_accuracy_score(y_true, ...)
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Compute the balanced accuracy.
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metrics.brier_score_loss(y_true, y_prob, *)
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Compute the Brier score loss.
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metrics.class_likelihood_ratios(y_true, ...)
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Compute binary classification positive and negative likelihood ratios.
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metrics.classification_report(y_true, y_pred, *)
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Build a text report showing the main classification metrics.
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metrics.cohen_kappa_score(y1, y2, *[, ...])
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Compute Cohen's kappa: a statistic that measures inter-annotator agreement.
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metrics.confusion_matrix(y_true, y_pred, *)
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Compute confusion matrix to evaluate the accuracy of a classification.
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metrics.dcg_score(y_true, y_score, *[, k, ...])
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Compute Discounted Cumulative Gain.
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metrics.det_curve(y_true, y_score[, ...])
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Compute error rates for different probability thresholds.
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metrics.f1_score(y_true, y_pred, *[, ...])
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Compute the F1 score, also known as balanced F-score or F-measure.
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metrics.fbeta_score(y_true, y_pred, *, beta)
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Compute the F-beta score.
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metrics.hamming_loss(y_true, y_pred, *[, ...])
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Compute the average Hamming loss.
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metrics.hinge_loss(y_true, pred_decision, *)
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Average hinge loss (non-regularized).
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metrics.jaccard_score(y_true, y_pred, *[, ...])
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Jaccard similarity coefficient score.
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metrics.log_loss(y_true, y_pred, *[, eps, ...])
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Log loss, aka logistic loss or cross-entropy loss.
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metrics.matthews_corrcoef(y_true, y_pred, *)
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Compute the Matthews correlation coefficient (MCC).
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metrics.multilabel_confusion_matrix(y_true, ...)
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Compute a confusion matrix for each class or sample.
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metrics.ndcg_score(y_true, y_score, *[, k, ...])
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Compute Normalized Discounted Cumulative Gain.
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metrics.precision_recall_curve(y_true, ...)
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Compute precision-recall pairs for different probability thresholds.
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metrics.precision_recall_fscore_support(...)
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Compute precision, recall, F-measure and support for each class.
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metrics.precision_score(y_true, y_pred, *[, ...])
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Compute the precision.
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metrics.recall_score(y_true, y_pred, *[, ...])
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Compute the recall.
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metrics.roc_auc_score(y_true, y_score, *[, ...])
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Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
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metrics.roc_curve(y_true, y_score, *[, ...])
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Compute Receiver operating characteristic (ROC).
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metrics.top_k_accuracy_score(y_true, y_score, *)
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Top-k Accuracy classification score.
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metrics.zero_one_loss(y_true, y_pred, *[, ...])
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Zero-one classification loss.
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|metrics.accuracy_score(y_true, y_pred, *[, ...])|Accuracy classification score.|
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|metrics.auc(x, y)|Compute Area Under the Curve (AUC) using the trapezoidal rule.|
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|metrics.average_precision_score(y_true, ...)|Compute average precision (AP) from prediction scores.|
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|metrics.balanced_accuracy_score(y_true, ...)|Compute the balanced accuracy.|
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|metrics.brier_score_loss(y_true, y_prob, *)|Compute the Brier score loss.|
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|metrics.class_likelihood_ratios(y_true, ...)|Compute binary classification positive and negative likelihood ratios.|
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|metrics.classification_report(y_true, y_pred, *)|Build a text report showing the main classification metrics.|
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|metrics.cohen_kappa_score(y1, y2, *[, ...])|Compute Cohen's kappa: a statistic that measures inter-annotator agreement.|
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|metrics.confusion_matrix(y_true, y_pred, *)|Compute confusion matrix to evaluate the accuracy of a classification.|
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|metrics.dcg_score(y_true, y_score, *[, k, ...])|Compute Discounted Cumulative Gain.|
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|metrics.det_curve(y_true, y_score[, ...])|Compute error rates for different probability thresholds.|
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|metrics.f1_score(y_true, y_pred, *[, ...])|Compute the F1 score, also known as balanced F-score or F-measure.|
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|metrics.fbeta_score(y_true, y_pred, *, beta)|Compute the F-beta score.|
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|metrics.hamming_loss(y_true, y_pred, *[, ...])|Compute the average Hamming loss.|
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|metrics.hinge_loss(y_true, pred_decision, *)|Average hinge loss (non-regularized).|
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|metrics.jaccard_score(y_true, y_pred, *[, ...])|Jaccard similarity coefficient score.|
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|metrics.log_loss(y_true, y_pred, *[, eps, ...])|Log loss, aka logistic loss or cross-entropy loss.|
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|metrics.matthews_corrcoef(y_true, y_pred, *)|Compute the Matthews correlation coefficient (MCC).|
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|metrics.multilabel_confusion_matrix(y_true, ...)|Compute a confusion matrix for each class or sample.|
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|metrics.ndcg_score(y_true, y_score, *[, k, ...])|Compute Normalized Discounted Cumulative Gain.|
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|metrics.precision_recall_curve(y_true, ...)|Compute precision-recall pairs for different probability thresholds.|
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|metrics.precision_recall_fscore_support(...)|Compute precision, recall, F-measure and support for each class.|
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|metrics.precision_score(y_true, y_pred, *[, ...])|Compute the precision.|
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|metrics.recall_score(y_true, y_pred, *[, ...])|Compute the recall.|
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|metrics.roc_auc_score(y_true, y_score, *[, ...])|Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.|
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|metrics.roc_curve(y_true, y_score, *[, ...])|Compute Receiver operating characteristic (ROC).|
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|metrics.top_k_accuracy_score(y_true, y_score, *)|Top-k Accuracy classification score.|
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|metrics.zero_one_loss(y_true, y_pred, *[, ...])|Zero-one classification loss.|
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### Regression metrics
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metrics.explained_variance_score(y_true, ...)
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Explained variance regression score function.
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metrics.max_error(y_true, y_pred)
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The max_error metric calculates the maximum residual error.
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metrics.mean_absolute_error(y_true, y_pred, *)
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Mean absolute error regression loss.
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metrics.mean_squared_error(y_true, y_pred, *)
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Mean squared error regression loss.
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metrics.mean_squared_log_error(y_true, y_pred, *)
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Mean squared logarithmic error regression loss.
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metrics.median_absolute_error(y_true, y_pred, *)
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Median absolute error regression loss.
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metrics.mean_absolute_percentage_error(...)
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Mean absolute percentage error (MAPE) regression loss.
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metrics.r2_score(y_true, y_pred, *[, ...])
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R^2 (coefficient of determination) regression score function.
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metrics.mean_poisson_deviance(y_true, y_pred, *)
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Mean Poisson deviance regression loss.
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metrics.mean_gamma_deviance(y_true, y_pred, *)
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Mean Gamma deviance regression loss.
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metrics.mean_tweedie_deviance(y_true, y_pred, *)
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Mean Tweedie deviance regression loss.
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metrics.d2_tweedie_score(y_true, y_pred, *)
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D^2 regression score function, fraction of Tweedie deviance explained.
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metrics.mean_pinball_loss(y_true, y_pred, *)
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Pinball loss for quantile regression.
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metrics.d2_pinball_score(y_true, y_pred, *)
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D^2 regression score function, fraction of pinball loss explained.
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metrics.d2_absolute_error_score(y_true, ...)
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D^2 regression score function, fraction of absolute error explained.
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|metrics.explained_variance_score(y_true, ...)|Explained variance regression score function.|
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|metrics.max_error(y_true, y_pred)|The max_error metric calculates the maximum residual error.|
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|metrics.mean_absolute_error(y_true, y_pred, *)|Mean absolute error regression loss.|
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|metrics.mean_squared_error(y_true, y_pred, *)|Mean squared error regression loss.|
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|metrics.mean_squared_log_error(y_true, y_pred, *)|Mean squared logarithmic error regression loss.|
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|metrics.median_absolute_error(y_true, y_pred, *)|Median absolute error regression loss.|
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|metrics.mean_absolute_percentage_error(...)|Mean absolute percentage error (MAPE) regression loss.|
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|metrics.r2_score(y_true, y_pred, *[, ...])|R^2 (coefficient of determination) regression score function.|
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|metrics.mean_poisson_deviance(y_true, y_pred, *)|Mean Poisson deviance regression loss.|
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|metrics.mean_gamma_deviance(y_true, y_pred, *)|Mean Gamma deviance regression loss.|
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|metrics.mean_tweedie_deviance(y_true, y_pred, *)|Mean Tweedie deviance regression loss.|
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|metrics.d2_tweedie_score(y_true, y_pred, *)|D^2 regression score function, fraction of Tweedie deviance explained.|
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|metrics.mean_pinball_loss(y_true, y_pred, *)|Pinball loss for quantile regression.|
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|metrics.d2_pinball_score(y_true, y_pred, *)|D^2 regression score function, fraction of pinball loss explained.|
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|metrics.d2_absolute_error_score(y_true, ...)|D^2 regression score function, fraction of absolute error explained.|
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### Multilabel ranking metrics
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metrics.coverage_error(y_true, y_score, *[, ...])
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Coverage error measure.
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metrics.label_ranking_average_precision_score(...)
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Compute ranking-based average precision.
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metrics.label_ranking_loss(y_true, y_score, *)
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Compute Ranking loss measure.
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|metrics.coverage_error(y_true, y_score, *[, ...])|Coverage error measure.|
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|metrics.label_ranking_average_precision_score(...)|Compute ranking-based average precision.|
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|metrics.label_ranking_loss(y_true, y_score, *)|Compute Ranking loss measure.|
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### Clustering metrics
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metrics.adjusted_mutual_info_score(...[, ...])
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Adjusted Mutual Information between two clusterings.
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metrics.adjusted_rand_score(labels_true, ...)
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Rand index adjusted for chance.
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metrics.calinski_harabasz_score(X, labels)
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Compute the Calinski and Harabasz score.
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metrics.davies_bouldin_score(X, labels)
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Compute the Davies-Bouldin score.
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metrics.completeness_score(labels_true, ...)
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Compute completeness metric of a cluster labeling given a ground truth.
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metrics.cluster.contingency_matrix(...[, ...])
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Build a contingency matrix describing the relationship between labels.
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metrics.cluster.pair_confusion_matrix(...)
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Pair confusion matrix arising from two clusterings [R9ca8fd06d29a-1].
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metrics.fowlkes_mallows_score(labels_true, ...)
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Measure the similarity of two clusterings of a set of points.
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metrics.homogeneity_completeness_v_measure(...)
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Compute the homogeneity and completeness and V-Measure scores at once.
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metrics.homogeneity_score(labels_true, ...)
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Homogeneity metric of a cluster labeling given a ground truth.
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metrics.mutual_info_score(labels_true, ...)
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Mutual Information between two clusterings.
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metrics.normalized_mutual_info_score(...[, ...])
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Normalized Mutual Information between two clusterings.
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metrics.rand_score(labels_true, labels_pred)
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Rand index.
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metrics.silhouette_score(X, labels, *[, ...])
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Compute the mean Silhouette Coefficient of all samples.
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metrics.silhouette_samples(X, labels, *[, ...])
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Compute the Silhouette Coefficient for each sample.
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metrics.v_measure_score(labels_true, ...[, beta])
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V-measure cluster labeling given a ground truth.
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|metrics.adjusted_mutual_info_score(...[, ...])|Adjusted Mutual Information between two clusterings.|
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|metrics.adjusted_rand_score(labels_true, ...)|Rand index adjusted for chance.|
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|metrics.calinski_harabasz_score(X, labels)|Compute the Calinski and Harabasz score.|
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|metrics.davies_bouldin_score(X, labels)|Compute the Davies-Bouldin score.|
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|metrics.completeness_score(labels_true, ...)|Compute completeness metric of a cluster labeling given a ground truth.|
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|metrics.cluster.contingency_matrix(...[, ...])|Build a contingency matrix describing the relationship between labels.|
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|metrics.cluster.pair_confusion_matrix(...)|Pair confusion matrix arising from two clusterings [R9ca8fd06d29a-1].|
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|metrics.fowlkes_mallows_score(labels_true, ...)|Measure the similarity of two clusterings of a set of points.|
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|metrics.homogeneity_completeness_v_measure(...)|Compute the homogeneity and completeness and V-Measure scores at once.|
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|metrics.homogeneity_score(labels_true, ...)|Homogeneity metric of a cluster labeling given a ground truth.|
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|metrics.mutual_info_score(labels_true, ...)|Mutual Information between two clusterings.|
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|metrics.normalized_mutual_info_score(...[, ...])|Normalized Mutual Information between two clusterings.|
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|metrics.rand_score(labels_true, labels_pred)|Rand index.|
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|metrics.silhouette_score(X, labels, *[, ...])|Compute the mean Silhouette Coefficient of all samples.|
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|metrics.silhouette_samples(X, labels, *[, ...])|Compute the Silhouette Coefficient for each sample.|
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|metrics.v_measure_score(labels_true, ...[, beta])|V-measure cluster labeling given a ground truth.|
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### Biclustering metrics
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metrics.consensus_score(a, b, *[, similarity])
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The similarity of two sets of biclusters.
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|metrics.consensus_score(a, b, *[, similarity])|The similarity of two sets of biclusters.|
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### Distance metrics
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metrics.DistanceMetric
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Uniform interface for fast distance metric functions.
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|metrics.DistanceMetric|Uniform interface for fast distance metric functions.|
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### Pairwise metrics
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metrics.pairwise.additive_chi2_kernel(X[, Y])
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Compute the additive chi-squared kernel between observations in X and Y.
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metrics.pairwise.chi2_kernel(X[, Y, gamma])
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Compute the exponential chi-squared kernel between X and Y.
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metrics.pairwise.cosine_similarity(X[, Y, ...])
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Compute cosine similarity between samples in X and Y.
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metrics.pairwise.cosine_distances(X[, Y])
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Compute cosine distance between samples in X and Y.
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metrics.pairwise.distance_metrics()
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Valid metrics for pairwise_distances.
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metrics.pairwise.euclidean_distances(X[, Y, ...])
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Compute the distance matrix between each pair from a vector array X and Y.
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metrics.pairwise.haversine_distances(X[, Y])
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Compute the Haversine distance between samples in X and Y.
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metrics.pairwise.kernel_metrics()
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Valid metrics for pairwise_kernels.
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metrics.pairwise.laplacian_kernel(X[, Y, gamma])
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Compute the laplacian kernel between X and Y.
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metrics.pairwise.linear_kernel(X[, Y, ...])
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Compute the linear kernel between X and Y.
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metrics.pairwise.manhattan_distances(X[, Y, ...])
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Compute the L1 distances between the vectors in X and Y.
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metrics.pairwise.nan_euclidean_distances(X)
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Calculate the euclidean distances in the presence of missing values.
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metrics.pairwise.pairwise_kernels(X[, Y, ...])
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Compute the kernel between arrays X and optional array Y.
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metrics.pairwise.polynomial_kernel(X[, Y, ...])
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Compute the polynomial kernel between X and Y.
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metrics.pairwise.rbf_kernel(X[, Y, gamma])
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Compute the rbf (gaussian) kernel between X and Y.
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metrics.pairwise.sigmoid_kernel(X[, Y, ...])
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Compute the sigmoid kernel between X and Y.
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metrics.pairwise.paired_euclidean_distances(X, Y)
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Compute the paired euclidean distances between X and Y.
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metrics.pairwise.paired_manhattan_distances(X, Y)
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Compute the paired L1 distances between X and Y.
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metrics.pairwise.paired_cosine_distances(X, Y)
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Compute the paired cosine distances between X and Y.
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metrics.pairwise.paired_distances(X, Y, *[, ...])
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Compute the paired distances between X and Y.
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metrics.pairwise_distances(X[, Y, metric, ...])
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Compute the distance matrix from a vector array X and optional Y.
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metrics.pairwise_distances_argmin(X, Y, *[, ...])
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Compute minimum distances between one point and a set of points.
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metrics.pairwise_distances_argmin_min(X, Y, *)
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Compute minimum distances between one point and a set of points.
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metrics.pairwise_distances_chunked(X[, Y, ...])
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Generate a distance matrix chunk by chunk with optional reduction.
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|metrics.pairwise.additive_chi2_kernel(X[, Y])|Compute the additive chi-squared kernel between observations in X and Y.|
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|metrics.pairwise.chi2_kernel(X[, Y, gamma])|Compute the exponential chi-squared kernel between X and Y.|
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|metrics.pairwise.cosine_similarity(X[, Y, ...])|Compute cosine similarity between samples in X and Y.|
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|metrics.pairwise.cosine_distances(X[, Y])|Compute cosine distance between samples in X and Y.|
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|metrics.pairwise.distance_metrics()|Valid metrics for pairwise_distances.|
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|metrics.pairwise.euclidean_distances(X[, Y, ...])|Compute the distance matrix between each pair from a vector array X and Y.|
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|metrics.pairwise.haversine_distances(X[, Y])|Compute the Haversine distance between samples in X and Y.|
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|metrics.pairwise.kernel_metrics()|Valid metrics for pairwise_kernels.|
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|metrics.pairwise.laplacian_kernel(X[, Y, gamma])Compute the laplacian kernel between X and Y.|
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|metrics.pairwise.linear_kernel(X[, Y, ...])|Compute the linear kernel between X and Y.|
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|metrics.pairwise.manhattan_distances(X[, Y, ...])|Compute the L1 distances between the vectors in X and Y.|
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|metrics.pairwise.nan_euclidean_distances(X)|Calculate the euclidean distances in the presence of missing values.|
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|metrics.pairwise.pairwise_kernels(X[, Y, ...])|Compute the kernel between arrays X and optional array Y.|
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|metrics.pairwise.polynomial_kernel(X[, Y, ...])|Compute the polynomial kernel between X and Y.|
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|metrics.pairwise.rbf_kernel(X[, Y, gamma])|Compute the rbf (gaussian) kernel between X and Y.|
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|metrics.pairwise.sigmoid_kernel(X[, Y, ...])|Compute the sigmoid kernel between X and Y.|
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|metrics.pairwise.paired_euclidean_distances(X, Y)|Compute the paired euclidean distances between X and Y.|
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|metrics.pairwise.paired_manhattan_distances(X, Y)|Compute the paired L1 distances between X and Y.|
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|metrics.pairwise.paired_cosine_distances(X, Y)|Compute the paired cosine distances between X and Y.|
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|metrics.pairwise.paired_distances(X, Y, *[, ...])|Compute the paired distances between X and Y.|
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|metrics.pairwise_distances(X[, Y, metric, ...])|Compute the distance matrix from a vector array X and optional Y.|
|
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|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.|
|
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|metrics.pairwise_distances_chunked(X[, Y, ...])|Generate a distance matrix chunk by chunk with optional reduction.|
|
||||
|
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### Plotting
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
metrics.ConfusionMatrixDisplay(...[, ...])
|
||||
Confusion Matrix visualization.
|
||||
|
||||
metrics.DetCurveDisplay(*, fpr, fnr[, ...])
|
||||
DET curve 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.
|
||||
|
||||
|metrics.ConfusionMatrixDisplay(...[, ...])|Confusion Matrix visualization.|
|
||||
|metrics.DetCurveDisplay(*, fpr, fnr[, ...])|DET curve 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)
|
||||
|
||||
|||
|
||||
|---|---|
|
||||
mixture.BayesianGaussianMixture(*[, ...])
|
||||
Variational Bayesian estimation of a Gaussian mixture.
|
||||
|
||||
mixture.GaussianMixture([n_components, ...])
|
||||
Gaussian Mixture.
|
||||
|
||||
|mixture.BayesianGaussianMixture(*[, ...])|Variational Bayesian estimation of a 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)
|
||||
|
||||
|
|
@ -892,50 +705,21 @@ Gaussian Mixture.
|
|||
|
||||
|||
|
||||
|---|---|
|
||||
model_selection.GroupKFold([n_splits])
|
||||
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.LeaveOneGroupOut()
|
||||
Leave One Group Out cross-validator
|
||||
|
||||
model_selection.LeavePGroupsOut(n_groups)
|
||||
Leave P Group(s) Out 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
|
||||
|model_selection.GroupKFold([n_splits])|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.LeaveOneGroupOut()|Leave One Group Out cross-validator|
|
||||
|model_selection.LeavePGroupsOut(n_groups)|Leave P Group(s) Out 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
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue