Logistic Regression Algorithm – Aulia Khalqillah

Logistic regression is a method to create a model by using binary data (0 and 1). The goal is to predict something independent variable based on a dependent variable. In a real application, logistic regression is applied to predict the number of customers who buy a product or who did not base on their previous transaction, to predict the number of fraud transactions in credit cards, and so on. In logistic regression, Y-axis lies from 0 – 1. Logistic regression cannot be solved by using a linear equation like linear regression. That is because if the Y-axis of the logistic function is transformed into a linear function, the boundary of the Y-axis lies from -infinity to infinity. Then, when we calculate the misfit error between actual data and predicted data, it will not get a good misfit error.