The Matthews correlation coefficient (MCC) is more informative than Cohen's Kappa and Brier score in binary classification assessment
Even if measuring the outcome of binary classifications is a pivotal task in machine learning
and statistics, no consensus has been reached yet about which statistical rate to employ to …
and statistics, no consensus has been reached yet about which statistical rate to employ to …
The benefits of the Matthews correlation coefficient (MCC) over the diagnostic odds ratio (DOR) in binary classification assessment
To assess the quality of a binary classification, researchers often take advantage of a four-
entry contingency table called confusion matrix, containing true positives, true negatives …
entry contingency table called confusion matrix, containing true positives, true negatives …
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Background To evaluate binary classifications and their confusion matrices, scientific
researchers can employ several statistical rates, accordingly to the goal of the experiment …
researchers can employ several statistical rates, accordingly to the goal of the experiment …
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix …
Evaluating binary classifications is a pivotal task in statistics and machine learning, because
it can influence decisions in multiple areas, including for example prognosis or therapies of …
it can influence decisions in multiple areas, including for example prognosis or therapies of …
Empirical comparison of area under ROC curve (AUC) and Mathew correlation coefficient (MCC) for evaluating machine learning algorithms on imbalanced datasets …
A common challenge encountered when trying to perform classifications and comparing
classifiers is selecting a suitable performance metric. This is particularly important when the …
classifiers is selecting a suitable performance metric. This is particularly important when the …
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification
Binary classification is a common task for which machine learning and computational
statistics are used, and the area under the receiver operating characteristic curve (ROC …
statistics are used, and the area under the receiver operating characteristic curve (ROC …
The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets
T Saito, M Rehmsmeier - PloS one, 2015 - journals.plos.org
Binary classifiers are routinely evaluated with performance measures such as sensitivity and
specificity, and performance is frequently illustrated with Receiver Operating Characteristics …
specificity, and performance is frequently illustrated with Receiver Operating Characteristics …
[HTML][HTML] The impact of class imbalance in classification performance metrics based on the binary confusion matrix
A major issue in the classification of class imbalanced datasets involves the determination of
the most suitable performance metrics to be used. In previous work using several examples …
the most suitable performance metrics to be used. In previous work using several examples …
A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes–Mallows index
Even if assessing binary classifications is a common task in scientific research, no
consensus on a single statistic summarizing the confusion matrix has been reached so far …
consensus on a single statistic summarizing the confusion matrix has been reached so far …
Consistent binary classification with generalized performance metrics
OO Koyejo, N Natarajan… - Advances in neural …, 2014 - proceedings.neurips.cc
Performance metrics for binary classification are designed to capture tradeoffs between four
fundamental population quantities: true positives, false positives, true negatives and false …
fundamental population quantities: true positives, false positives, true negatives and false …