Model Evaluation

41 methods.

Classification Metric 13

Accuracy Balanced Accuracy F-beta Score F1-Score Macro-averaged F1 Matthews Correlation Coefficient Micro-averaged F1 Precision Precision-Recall AUC Recall (Sensitivity)Specificity Weighted F1 Youdens J Statistic

External Clustering Validation 4

Adjusted Rand Index Fowlkes-Mallows Index Normalized Mutual Information V-measure

Clustering Validation 4

Calinski-Harabasz Index Davies-Bouldin Index Dunn Index Silhouette Score

Error metric 3

Mean Absolute Error Mean Squared Error Root Mean Squared Error

Regression evaluation 2

Adjusted R-squared R-squared

Information-theoretic criterion 2

Akaike Information Criterion Bayesian Information Criterion

Probabilistic Loss Metric 2

Brier Score Log-Loss (Cross-Entropy Loss)

Cluster Number Selection 2

Elbow Method Gap Statistic

Multi-label Metric 2

Hamming Loss Jaccard Index

Relative error metric 2

Mean Absolute Percentage Error Symmetric MAPE

Diagnostic Tool 1

Confusion Matrix

Statistical testing 1

Goodness-of-Fit

Cluster Cohesion Measure 1

Inertia (Within-Cluster Sum of Squares)

Classification Evaluation Tool 1

Lift and Gain Chart

Scaled error metric 1

Mean Absolute Scaled Error