Editing Confusion matrix (section)

{{short description|Table layout for visualizing performance; also called an error matrix}}
In the field of [[machine learning]] and specifically the problem of [[statistical classification]], a '''confusion matrix''', also known as '''error matrix''',<ref>{{cite journal |last1=Stehman |first1= Stephen V. |year= 1997|title=Selecting and interpreting measures of thematic classification accuracy |journal=Remote Sensing of Environment |volume=62 |issue=1 |pages=77–89 |doi=10.1016/S0034-4257(97)00083-7 |bibcode= 1997RSEnv..62...77S }}</ref> is a specific [[table (information)|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 (mathematics)|matrix]] represents the instances in an actual class while each column represents the instances in a predicted class, or vice versa &ndash; both variants are found in the literature.<ref name="Powers2011">{{cite journal |first=David M. W. |last=Powers |date=2011 |title=Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation |journal=Journal of Machine Learning Technologies |volume=2 |issue=1 |pages=37–63 |url=https://www.researchgate.net/publication/228529307 |s2cid=55767944 }}</ref> The diagonal of the matrix therefore represents all instances that are correctly predicted.<ref>{{cite journal|last=Opitz|first=Juri|title=A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice|journal=Transactions of the Association for Computational Linguistics|date=2024|volume=12|pages=820–836|doi=10.1162/tacl_a_00675|url=https://doi.org/10.1162/tacl_a_00675|arxiv=2404.16958}}</ref> The name stems from the fact that it makes it easy to see whether the system is confusing two classes (i.e. commonly mislabeling one as another). 

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).

__TOC__