This allows more detailed analysis than mere proportion of correct classifications (accuracy). In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of false positives, false negatives, true positives, and true negatives. Models #1, #2 and #4 have a very similar accuracy but different false negative or overestimation levels. It is a special kind of contingency table, with two dimensions ('actual' and 'predicted'), and identical sets of 'classes' in both dimensions (each combination of dimension and class is a variable in the contingency table).Ĭomparing mean accuracy and percent of false negative (overestimation) of five machine learning (multi-class) classification models. The name stems from the fact that it makes it easy to see whether the system is confusing two classes (i.e. 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. In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an 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). Sources: Fawcett (2006), Piryonesi and El-Diraby (2020), Sensitivity, recall, hit rate, or true positive rate (TPR) T P R = T P P = T P T P + F N = 1 − F N R with miss, type II error or overestimation

with false alarm, type I error or underestimation false negative (FN) eqv.

with correct rejection false positive (FP) eqv.

Table layout for visualizing performance also called an error matrix Terminology and derivationsįrom a confusion matrix condition positive (P) the number of real positive cases in the data condition negative (N) the number of real negative cases in the data

The matrix free