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Hub AI
P4-metric AI simulator
(@P4-metric_simulator)
Hub AI
P4-metric AI simulator
(@P4-metric_simulator)
P4-metric
P4 metric (also known as FS or Symmetric F ) enables performance evaluation of the binary classifier. It is calculated from precision, recall, specificity and NPV (negative predictive value). P4 is designed in similar way to F1 metric, however addressing the criticisms leveled against F1. It may be perceived as its extension.
Like the other known metrics, P4 is a function of: TP (true positives), TN (true negatives), FP (false positives), FN (false negatives).
The key concept of P4 is to leverage the four key conditional probabilities:
The main assumption behind this metric is, that a properly designed binary classifier should give the results for which all the probabilities mentioned above are close to 1. P4 is designed the way that requires all the probabilities being equal 1. It also goes to zero when any of these probabilities go to zero.
P4 is defined as a harmonic mean of four key conditional probabilities:
In terms of TP,TN,FP,FN it can be calculated as follows:
Evaluating the performance of binary classifier is a multidisciplinary concept. It spans from the evaluation of medical tests, psychiatric tests to machine learning classifiers from a variety of fields. Thus, many metrics in use exist under several names. Some of them being defined independently.
Dependency table for selected metrics ("true" means depends, "false" - does not depend):
P4-metric
P4 metric (also known as FS or Symmetric F ) enables performance evaluation of the binary classifier. It is calculated from precision, recall, specificity and NPV (negative predictive value). P4 is designed in similar way to F1 metric, however addressing the criticisms leveled against F1. It may be perceived as its extension.
Like the other known metrics, P4 is a function of: TP (true positives), TN (true negatives), FP (false positives), FN (false negatives).
The key concept of P4 is to leverage the four key conditional probabilities:
The main assumption behind this metric is, that a properly designed binary classifier should give the results for which all the probabilities mentioned above are close to 1. P4 is designed the way that requires all the probabilities being equal 1. It also goes to zero when any of these probabilities go to zero.
P4 is defined as a harmonic mean of four key conditional probabilities:
In terms of TP,TN,FP,FN it can be calculated as follows:
Evaluating the performance of binary classifier is a multidisciplinary concept. It spans from the evaluation of medical tests, psychiatric tests to machine learning classifiers from a variety of fields. Thus, many metrics in use exist under several names. Some of them being defined independently.
Dependency table for selected metrics ("true" means depends, "false" - does not depend):