Skip to main content

Metrics for Machine Learning evaluation Data Science Measurement

Project description

Metrics for evaluating machine learning models or Data Science

Include : All metrics from SKLEARN. Category based metrics. ########################################################################

from metric.metric import *

Classification metrics

accuracy_score(y_true, y_pred[, …]) Accuracy classification score. auc(x, y) Compute Area Under the Curve (AUC) using the trapezoidal rule average_precision_score(y_true, y_score) Compute average precision (AP) from prediction scores balanced_accuracy_score(y_true, y_pred) Compute the balanced accuracy brier_score_loss(y_true, y_prob[, …]) Compute the Brier score. classification_report(y_true, y_pred) Build a text report showing the main classification metrics cohen_kappa_score(y1, y2[, labels, …]) Cohen’s kappa: a statistic that measures inter-annotator agreement. confusion_matrix(y_true, y_pred[, …]) Compute confusion matrix to evaluate the accuracy of a classification. dcg_score(y_true, y_score[, k, …]) Compute Discounted Cumulative Gain. f1_score(y_true, y_pred[, labels, …]) Compute the F1 score, also known as balanced F-score or F-measure fbeta_score(y_true, y_pred, beta[, …]) Compute the F-beta score hamming_loss(y_true, y_pred[, …]) Compute the average Hamming loss. hinge_loss(y_true, pred_decision[, …]) Average hinge loss (non-regularized) jaccard_score(y_true, y_pred[, …]) Jaccard similarity coefficient score log_loss(y_true, y_pred[, eps, …]) Log loss, aka logistic loss or cross-entropy loss. matthews_corrcoef(y_true, y_pred[, …]) Compute the Matthews correlation coefficient (MCC) multilabel_confusion_matrix(y_true, …) Compute a confusion matrix for each class or sample ndcg_score(y_true, y_score[, k, …]) Compute Normalized Discounted Cumulative Gain. precision_recall_curve(y_true, …) Compute precision-recall pairs for different probability thresholds precision_recall_fscore_support(…) Compute precision, recall, F-measure and support for each class precision_score(y_true, y_pred[, …]) Compute the precision recall_score(y_true, y_pred[, …]) Compute the recall roc_auc_score(y_true, y_score[, …]) Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores. roc_curve(y_true, y_score[, …]) Compute Receiver operating characteristic (ROC) zero_one_loss(y_true, y_pred[, …]) Zero-one classification loss.

Regression metrics

explained_variance_score(y_true, y_pred) Explained variance regression score function max_error(y_true, y_pred) max_error metric calculates the maximum residual error. mean_absolute_error(y_true, y_pred) Mean absolute error regression loss mean_squared_error(y_true, y_pred[, …]) Mean squared error regression loss mean_squared_log_error(y_true, y_pred) Mean squared logarithmic error regression loss median_absolute_error(y_true, y_pred) Median absolute error regression loss r2_score(y_true, y_pred[, …]) R^2 (coefficient of determination) regression score function. mean_poisson_deviance(y_true, y_pred) Mean Poisson deviance regression loss. mean_gamma_deviance(y_true, y_pred) Mean Gamma deviance regression loss. mean_tweedie_deviance(y_true, y_pred) Mean Tweedie deviance regression loss.

Multilabel ranking metrics

coverage_error(y_true, y_score[, …]) Coverage error measure label_ranking_average_precision_score(…) Compute ranking-based average precision label_ranking_loss(y_true, y_score) Compute Ranking loss measure

Clustering metrics

supervised, which uses a ground truth class values for each sample. unsupervised, which does not and measures the ‘quality’ of the model itself.

adjusted_mutual_info_score(…[, …]) Adjusted Mutual Information between two clusterings. adjusted_rand_score(labels_true, …) Rand index adjusted for chance. calinski_harabasz_score(X, labels) Compute the Calinski and Harabasz score. davies_bouldin_score(X, labels) Computes the Davies-Bouldin score. completeness_score(labels_true, …) Completeness metric of a cluster labeling given a ground truth. cluster.contingency_matrix(…[, …]) Build a contingency matrix describing the relationship between labels. fowlkes_mallows_score(labels_true, …) 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(labels_true, …) Homogeneity metric of a cluster labeling given a ground truth. mutual_info_score(labels_true, …) Mutual Information between two clusterings. normalized_mutual_info_score(…[, …]) Normalized Mutual Information between two clusterings. silhouette_score(X, labels[, …]) Compute the mean Silhouette Coefficient of all samples. silhouette_samples(X, labels[, metric]) Compute the Silhouette Coefficient for each sample. v_measure_score(labels_true, labels_pred) V-measure cluster labeling given a ground truth.

Biclustering metrics

consensus_score(a, b[, similarity]) The similarity of two sets of biclusters.

Pairwise metrics

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

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for metric, version 0.5.0
Filename, size File type Python version Upload date Hashes
Filename, size metric-0.5.0-py3-none-any.whl (45.0 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size metric-0.5.0.tar.gz (40.9 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page