Concepts

Machine Learning Concepts

Accuracy

Accuracy = All True (Positive/Negative) Classifications / (Total Classifications)
Accuracy = TP + TN / (TP + TN + FP + FN)
Accuracy = True Positives + True Negatives / (True Positives + True Negatives + False Positives + False Negatives)

Precision

Precision = True Positives / (Positive Classifications Made)
Precision = TP / (TP + FP)
Precision = True Positives / (True Positives + False Positives)

Recall

Recall = True Positives (Correct Positive Classifications) / (Actual Positives)
Recall = TP / (TP + FN)
Recall = True Positives / (True Positives + False Negatives)

False Positive Rate (FPR) or Fall-out (logically, False Alarms)

False Positive Rate = FP / (Total Classifications)
False Positive Rate = FPR = FP / (FP + FN + TP + TN)

False Negative Rate(FNR) aka Miss Rate

False Negative Rate = FN / (Total Classifications)
False Negative Rate = FNR = FN / (FP + FN + TP + TN)