Logistic Regression

Machine learning is a combination of art and science. No machine learning algorithm can solve all the problems. There are several factors that can influence your decision to choose a machine learning algorithm.

In any machine learning model, there are two sources of error: bias and variance. To better illustrate these two concepts, assume that a machine learning model has been created and the actual output of the data is known, trained with different parts of the same data, and as a result the machine learning model produces different parts of the data.

Logistic regression involves higher mathematics, linear algebra, probability theory, and optimization problems. This article tries to explain the Logistic regression to the readers in the simplest and most easy-to-understand narrative way, with less discussion of the principle of the formula and more on the case of visualization.