![]() That’s just what we need for binary classification, as we can set the threshold at 0.5 and make predictions according to the output of the logistic function. The logistic function is an S-shaped function developed in statistics, and it takes any real-valued number and maps it to a value between 0 and 1. The algorithm got the name from its underlying mechanism - the logistic function (sometimes called the sigmoid function). Logistic regression is a great introductory algorithm for binary classification (two class values) borrowed from the field of statistics. That’s it for the introduction section - we have many things to cover, so let’s jump right to it. We’ll cover data preparation, modeling, and evaluation of the well-known Titanic dataset. Our little journey to machine learning with R continues! Today’s topic is logistic regression - as an introduction to machine learning classification tasks. Classification fundamentals in R - code included ![]()
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