In this tutorial, we will learn about binary logistic regression and its application to real life data using Python. We have also covered binary logistic regression in R in another tutorial. Without a doubt, binary logistic regression remains the most widely used predictive modeling method. Logistic Regression is a classification algorithm that is used to predict the probability of a categorical dependent variable. The method is used to model a binary variable that takes two possible values, typically coded as 0 and 1
Binary Logistic Regression – a tutorial
In this tutorial we’ll learn about binary logistic regression and its application to real life data. Without any doubt, binary logistic regression remains the most widely used predictive modeling method.
Binary Logistic Regression with R – a tutorial
In a previous tutorial, we discussed the concept and application of binary logistic regression. We’ll now learn more about binary logistic regression model building and its assessment using R.
Firstly, we’ll recap our earlier case study and then develop a binary logistic regression model in R. followed by and explanation of model sensitivity and specificity, and how to estimate these using R.