A linear regression model

In the previous tutorial posts, we looked at the Linear Regression and discussed some basics of statistics such as the Standard Deviation and the Standard Error. Today, we will look at the Logistic Regression. It is similar in name to the linear regression, but different in usage. Let’s have a look

The Logistic Regression explained

One of the main difference to the Linear Regression for the Logistic Regression is that you the logistic regression is binary – it calculates values between 0 and 1 and thus states if something is rather true or false. This means that the result of a prediction could be “fail” or “succeed” for a test. In a churn model, this would mean that a customer either stays with the company or leaves the company.

Another key difference to the Linear Regression is that the regression curve can’t be calculated. Therefore, in the Logistic Regression, the regression curve is “estimated” and optimised. There is a mathematical function to do this estimation – called the “Maximum Likelihood Method”. Normally, these Parameters are calculated by different Machine Learning Tools so that you don’t have to do it.

Another aspect is the concept of “Odds”. Basically, the odd of a certain event happening or not happening is calculated. This could be a certain team winning a soccer game: let’s assume that Team X wins 7 out of 10 games (thus loosing 3, we don’t take a draw). The odds in this case would be 7:10 on winning or 3:10 on loosing.

This time we won’t calculate the Logistic Regression, since it is way too long. In the next tutorial, I will focus on classifiers such as Random Forest and Naive Bayes.

This tutorial is part of the Machine Learning Tutorial. You can learn more about Machine Learning by going through this tutorial. On Cloudvane, there are many more tutorials about (Big) Data, Data Science and alike, read about them in the Big Data Tutorials here. If you look for great datasets to play with, I would recommend you Kaggle.

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