![]() ![]() Multinomial: In multinomial Logistic regression, there can be 3 or more possible unordered types of the dependent variable, such as "cat", "dogs", or "sheep".Binomial: In binomial Logistic regression, there can be only two possible types of the dependent variables, such as 0 or 1, Pass or Fail, etc. ![]() On the basis of the categories, Logistic Regression can be classified into three types: The above equation is the final equation for Logistic Regression. But we need range between - to +, then take logarithm of the equation it will become:.In Logistic Regression y can be between 0 and 1 only, so for this let's divide the above equation by (1-y):.We know the equation of the straight line can be written as:.The mathematical steps to get Logistic Regression equations are given below: The Logistic regression equation can be obtained from the Linear Regression equation. The independent variable should not have multi-collinearity.The dependent variable must be categorical in nature.Such as values above the threshold value tends to 1, and a value below the threshold values tends to 0. In logistic regression, we use the concept of the threshold value, which defines the probability of either 0 or 1. ![]() The S-form curve is called the Sigmoid function or the logistic function. The value of the logistic regression must be between 0 and 1, which cannot go beyond this limit, so it forms a curve like the "S" form.It maps any real value into another value within a range of 0 and 1.The sigmoid function is a mathematical function used to map the predicted values to probabilities. ![]() Note: Logistic regression uses the concept of predictive modeling as regression therefore, it is called logistic regression, but is used to classify samples Therefore, it falls under the classification algorithm. The below image is showing the logistic function:
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