Logistic regression of machinelearning

I. Overview

Assuming there are some data points, we fit the points in a straight line (the line is called the best fit Line), and the fitting process is called regression;

The main idea of using logistic regression to classify the classification boundary line is to set up the regression formula according to the existing data. The word " regression " Here stems from the best fit, meaning that the mathematical analysis behind it will be described in the next section to find the best fitting set of parameters. The practice of training classifiers is to find the best fit parameters, using the optimization algorithm.

Second, the classification based on logistic regression and sigmoid function

The unit step function is also called the Hevy step function (Heaviside step functions)
sigmoid function:

　　　　

logistic regression classifier, We can take a regression coefficient in each feature and then add all the result values, substituting this sum into sigmoid0~10.51 0.5 is classified into 0

logistic

So what's the problem now? After determining the function form of the classifier, how to determine the optimal regression coefficient "is different from the weighting in weighted linear regression"?

Optimal regression coefficient determination based on optimization method

Gradient Rise method

Logistic regression of machinelearning