**confusion matrix or correlation matrix or **covariance** matrix?**

A **correlation matrix** is a table showing **correlation **coefficients between variables. Each cell in the table shows the **correlation** between two variables. A **correlation matrix**is used to summarize data, as an input into a more advanced analysis, and as a diagnostic for advanced analyses.

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**).

Each row of the matrix represents the instances in a predicted class while each column represents the instances in an actual class (or vice versa).

The name stems from the fact that it makes it easy to see if 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).

In probability theory and statistics, a **covariance matrix** (also known as **auto-covariance matrix**, **dispersion matrix**, **variance matrix**, or **variance–covariance matrix**) is a square matrix giving the covariance between each pair of elements of a given random vector. In the matrix diagonal there are variances, i.e., the covariance of each element with itself.