# What is a Confusion Matrix?
In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one; in unsupervised learning it is usually called a matching matrix.
Each row of the matrix represents the instances in an actual class while each column represents the instances in a predicted class, or vice versa – both variants are found in the literature.
# Confusion Matrix:
4 outcomes
True, if the prediction matches the classification False, if the prediction does not match the classification
- actual classification is positive - predicted classification is positive (1, 1) = True Positive
- actual classification is positive - predicted classification is negative (1, 0) = False Negative
- actual classification is negative - predicted classification is positive (0, 1) = False Positive
- actual classification is negative - predicted classification is negative (0, 0) = True Negative
Wikipedia Example
- We have 12 individuals
- 8 have been diagnosed with cancer (1) - 4 are cancer-free (0)
- A classifier distinguishes between individuals with / without cancer
# Example
In a Classificationmodel (picture: man/woman) with a test amount of 100 pictures (60/40) show the following results:
- 40 / men - were right
- 35 / women - were right
