| Title: | Classification Evaluation Metrics |
|---|---|
| Description: | A fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix. |
| Authors: | G. Dudnik [aut, cre, cph] |
| Maintainer: | G. Dudnik <[email protected]> |
| License: | GPL-2 |
| Version: | 1.0.1 |
| Built: | 2024-12-16 06:49:09 UTC |
| Source: | CRAN |
Calculates multi-class classification evaluation metrics: balanced.accuracy, balanced accuracy (balanced.accuracy), diagnostic odds ratio (DOR), error rate (error.rate), F.beta (F0.5, F1 (F-measure, F-score), F2 with where beta is 0.5, 1 and 2 respectively), false positive rate (FPR), false negative rate (FNR), false omission rate ((FOR)), false discovery rate (FDR), geometric mean (geometric.mean), Jaccard, positive likelihood ratio (p+, LR(+) or simply L), negative likelihood ratio (p-, LR(-) or simply lambda), Matthews corellation coefficient (MCC), markedness (MK), negative predictive value (NPV), optimization precision OP, precision, recall (sensitivity), specificity and finally Youden's index. The function calculates the aforementioned metrics from a confusion matrix (contingency matrix) where TP, TN, FP FN are abbreviations for true positives, true negatives, false positives and false negatives respectively.
ml_test(predicted, true, output.as.table = FALSE)ml_test(predicted, true, output.as.table = FALSE)
predicted |
class labels predicted by the classifier model (a set of classes convertible into type factor with levels representing labels) |
true |
true class labels (a set of classes convertible into type factor of the same length and with the same levels as predicted) |
output.as.table |
the function returns all metrics except for accuracy and error.rate in a tabular format if this argument is set to TRUE |
the function returns a list of following metrics:
\strong{accuracy} |
calculated as: (TP+TN) / (TP+FP+TN+FN) (doesn't show up when output.as.table = TRUE) |
\strong{balanced.accuracy} |
calculated as: (TP / (TP+FN)+TN / (TN+FP)) / 2 = (recall+specificity) / 2 |
\strong{DOR} |
calculated as: TP*TN / (FP*FN) = L / lambda |
\strong{error.rate} |
calculated as: (FP+FN) / (TP+TN+FP+FN) = 1-accuracy (doesn't show up when output.as.table = TRUE) |
\strong{F0.5} |
calculated as: 1.25*(recall*precision/(0.25*precision+recall)) |
\strong{F1} |
calculated as: 2*(precision*recall / (precision+recall)) |
\strong{F2} |
calculated as: 5*(precision*recall / (4*precision+recall)) |
\strong{FDR} |
calculated as: 1-precision |
\strong{FNR} |
calculated as: 1-recall |
\strong{FOR} |
calculated as: 1-NPV |
\strong{FPR} |
calculated as: 1-specificity |
\strong{geometric.mean} |
calculated as: (recall*specificity)^0.5 |
\strong{Jaccard} |
calculated as: TP / (TP+FP+FN) |
\strong{L} |
calculated as: recall / (1-specificity) |
\strong{lambda} |
calculated as: (1-recall) / (specificity) |
\strong{MCC} |
calculated as: (TP*TN-FP*FN) / (((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN))^0.5) |
\strong{MK} |
calculated as: precision + NPV - 1 |
\strong{NPV} |
calculated as: TN / (TN+FN) |
\strong{OP} |
calculated as: accuracy - |recall-specificity| / (recall+specificity) |
\strong{precision} |
calculated as: TP / (TP+FP) |
\strong{recall} |
calculated as: TP / (TP+FN) |
\strong{specificity} |
calculated as: TN / (TN+FP) |
\strong{Youden} |
calculated as: recall+specificity-1 |
G. Dudnik
Sasaki Y. (2007). The truth of the F-measure.:1–5. https://www.researchgate.net/publication/268185911_The_truth_of_the_F-measure.
Powers DMW. (2011). Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness & Correlation. Arch Geschwulstforsch. 2(1):37–63. https://www.researchgate.net/publication/313610493_Evaluation_From_precision_recall_and_fmeasure_to_roc_informedness_markedness_and_correlation.
Bekkar M, Djemaa HK, Alitouche TA. (2013). Evaluation Measures for Models Assessment over Imbalanced Data Sets. J Inf Eng Appl. 3(10):27–38. https://www.iiste.org/Journals/index.php/JIEA/article/view/7633.
Jeni LA, Cohn JF, De La Torre F. (2013). Facing Imbalanced Data Recommendations for the Use of Performance Metrics. Conference on Affective Computing and Intelligent Interaction. IEEE. p. 245–51. http://ieeexplore.ieee.org/document/6681438/.
López V, Fernández A, García S, Palade V, Herrera F. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Inf Sci. 250:113–41. http://dx.doi.org/10.1016/j.ins.2013.07.007.
Tharwat A. (2018). Classification assessment methods. Appl Comput Informatics . https://linkinghub.elsevier.com/retrieve/pii/S2210832718301546.
library(mltest) # class labels ("cat, "dog" and "rat") predicted by the classifier model predicted_labels <- as.factor(c("dog", "cat", "dog", "rat", "rat")) # true labels (test set) true_labels <- as.factor(c("dog", "cat", "dog", "rat", "dog")) classifier_metrics <- ml_test(predicted_labels, true_labels, output.as.table = FALSE) # overall classification accuracy accuracy <- classifier_metrics$accuracy # F1-measures for classes "cat", "dog" and "rat" F1 <- classifier_metrics$F1 # tabular view of the metrics (except for 'accuracy' and 'error.rate') classifier_metrics <- ml_test(predicted_labels, true_labels, output.as.table = TRUE)library(mltest) # class labels ("cat, "dog" and "rat") predicted by the classifier model predicted_labels <- as.factor(c("dog", "cat", "dog", "rat", "rat")) # true labels (test set) true_labels <- as.factor(c("dog", "cat", "dog", "rat", "dog")) classifier_metrics <- ml_test(predicted_labels, true_labels, output.as.table = FALSE) # overall classification accuracy accuracy <- classifier_metrics$accuracy # F1-measures for classes "cat", "dog" and "rat" F1 <- classifier_metrics$F1 # tabular view of the metrics (except for 'accuracy' and 'error.rate') classifier_metrics <- ml_test(predicted_labels, true_labels, output.as.table = TRUE)