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Sometimes accuracy itself is not a good measure of classifier goodness.
AUC gives a measure of classifier performance.
This margin is employed as a measure of classifier robustness to noise: classifiers with larger margins are supposed to be more resistant to measurement error or peculiarities of the training set.
Accuracy and Positive Predictive Value (PPV) is used as the measure of classifier performance.
Area Under the Precision – Recall Curve (AUPR) is a well-known measure of classifier power.
The F-measure (harmonic mean of precision and recall) was taken as a measure of classifier performance.
Similar(48)
We introduce three measures of classifier's competence designed specifically for one-class problems.
Summary: Precision-recall (PR) and receiver operating characteristic (ROC) curves are valuable measures of classifier performance.
As seen in Figure 2, the use of both the Wrapper and the Relief feature selection algorithms results in falsified measures of classifier performance.
The procedure is iterated for two purposes: first, to derive confidence measures of classifier performance across different partitionings of the data, thereby minimizing the risk of a spurious result from accidental overfitting, and second, to tailor the parameters of the classifier so as to optimize it for the specific task.
The AUC measure of a classifier is equivalent to the probability that the classifier will rank a randomly chosen (truly) positive diagnosis higher than a randomly chosen negative diagnosis [ 40].
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com