What is the origin of the term confusion matrix?

The confusion matrix was invented by Carl Pearson in 1904. He uses the term "strain gauge". It appeared in Karl Pearson, F. R. (1904). Mathematical Contribution to Evolution (PDF) DeLau and Company

During World War II, the detection theory was developed as a survey of the relationship between stimuli and reactions. The confusion matrix used there

This term is used in psychology for detection theory. Let's start machine learning from there

This concept seems to have been invented in fields closely related to machine learning which achieved machine learning during statistics, 100 years round trip.

The confusion matrix is ​​a table that summarizes various predictions and test results and compares them with actual values. The confusion matrix is ​​used for statistics, data mining, machine learning models, and other artificial intelligence (AI) applications. Error matrix

A well-known matrix decomposition method is singular value decomposition (SVD). At higher levels, SVD is an algorithm that decomposes Matrix A into the lowest level (ie, smaller / simpler) of the original matrix A. Mathematically, we break up A into two unitary matrices and diagonal matrices. Where A is the input data matrix (user evaluation), U is the left singular vector (user's "feature" matrix) and Sum is a singular value pair. Angle matrix (basically the weight / intensity of each concept) V ^ T is a right singular vector ("feature" matrix of the movie). U and V ^ T are column orthogonal and represent different things. U indicates how much the user likes each function, and V ^ T indicates how much each function is associated with each movie.

Let A be an m × n matrix. We define U as an m × m matrix, D as an m × n matrix, and V as an n × n matrix. Each of these matrices is defined as having a special structure. Both matrices U and V are defined as orthogonal matrices. The matrix D is defined as a diagonal matrix. Please note that D is not necessarily a square. The elements along the diagonal of D are called singular values ​​of matrix A. The U column is called the left singular vector. Right singular vector

Where A, B, C, and D are constant matrices. This system is linear and time invariant, or simply called LTI. The matrix A is the dynamic matrix, the matrix B is the control matrix, the matrix C is the sensor matrix, and the matrix D is called the direct term. There is usually no direct item in the system. This indicates that the control signal does not directly affect the output. Example 2.1 (Balance System) An example of the type of system that can be modeled using ordinary differential equations is the "balanced system" class. Balance adjustment system is a mechanical system in which the center of gravity is balanced above the pivot point. A general example of a balance system is shown in Figure 2.3. The Segway Human Transport System (Figure 2.3a) stabilizes the standing person using the electric platform. When the rider is leaning forward, the vehicle propels along the ground but maintains its upright position