Classification Metrics

classification_metrics.py

# TP = True Positive
# TN = True Negative
# FP = False Positive
# FN = False Negative

def precision(TP, FP):
	return TP / (TP + FP)

def recall(TP, FN):
	return TP / (TP + FN)

def negative_predictive_value(TN, FN):
	return TN / (TN + FN)

def sensitivity(TP, FN):
	return TP / (TP + FN)

def specificity(TN, FP):
	return TN / (TN + FP)

def accuracy(TP, TN, FP, FN):
	return (TP + TN) / (TP + TN + FP + FN)

def f1_score(TP, FP, FN):
	P = precision(TP, FP)
	R = recall(TP, FN)
	return 2 * ((P * R) / (P + R))

def f1_score_alt(TP, FP, FN):
	return TP / (TP + ((FP + FN) / 2))