confusion-matrix-classification Calculate Precision & Recall & F-Measure & Accuracy with Confusion Matrix (TP, TN, FP, FN)
Confusion Matrix ::
Positive Prediction Negative Prediction Positive Class True Positive (TP) False Negative (FN) Negative Class False Positive (FP) True Negative (TN)
Precision quantifies Formula :
Precision = TruePositives / (TruePositives + FalsePositives)Precision = TP / ( TP + FP )Recall quantifies Formula :
Recall = TruePositives / (TruePositives + FalseNegatives)Recall = TP / ( TP + FN )F-Measure quantifies Formula :
F-Measure = (2 * Precision * Recall) / (Precision + Recall)Accuracy quantifies Formula :
accuracy = (TruePositives + TrueNegative) / (TruePositives + FalsePositive + False Negative + TrueNegative)accuracy = (TP + TN) / (TP + FP + FN + TN)Code (python class) :
class confusionMatrixClassification:# Author : Amir Shokri# Email : amirsh.nll@gmail.com# Date : Dec 2021# git : https://github.com/amirshnll/confusion-matrix-classification/def __init__(self):self.tp = 0.0self.tf = 0.0self.fp = 0.0self.fn = 0.0def setTP(self, tp):self.tp = float(tp)def getTP(self):return float(self.tp)def setTF(self, tf):self.tf = float(tf)def getTF(self):return float(self.tf)def setFP(self, fp):self.fp = float(fp)def getFP(self):return float(self.fp)def setFN(self, fn):self.fn = float(fn)def getFN(self):return float(self.fn)def precision(self):try:return ( self.getTP() + self.getTF() ) / ( self.getTP() + self.getTF() + self.getFP() + self.getFN() )except:return -1 # errordef accuracy(self):try:return self.getTP() / ( self.getTP() + self.getFP() )except:return -1 # errordef recall(self):try:return self.getTP() / ( self.getTP() + self.getFN() )except:return -1 # errordef fmeasure(self):precision_value = self.precision()recall_value = self.recall()if precision_value == -1 or recall_value == -1:return -1 # errortry:return ( 2 * precision_value ) / ( precision_value + recall_value )except:return -1 # errorCode (usage) :
cmc = confusionMatrixClassification()cmc.setTP(90)cmc.setFP(30)cmc.setFN(10)print("Accuracy : " + str(cmc.accuracy())) # 0.75print("Precision : " + str(cmc.precision())) # 0.6923076923076923print("Recall : " + str(cmc.recall())) # 0.9print("F-Measure : " + str(cmc.fmeasure())) # 0.8695652173913042