# Chapter. Logistic Regression

### What is logistic regression?

Logistic regression is a statistical analysis method used to predict a data value based on prior observations of a data set. A logistic regression model predicts a dependent data variable by analyzing the relationship between one or more existing independent variables.



The dependent variable of logistics regression can be two-category or multi-category, but the two-category is more common and easier to explain. So the most common use in practice is the logistics of the two classifications. An example used by TensorFlow.NET is a hand-written digit recognition, which is a multi-category.



Softmax regression allows us to handle ![1557035393445](_static\logistic-regression\1557035393445.png) where K is the number of classes.


The full example is [here](https://github.com/SciSharp/TensorFlow.NET-Examples/blob/master/src/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs).