Practical aspects — Logistic Regression in layman terms

Ever wondered how your credit card company identifies those suspicious transactions on your card and alerts you? And how gmail automatically identifies and classifies mails as spam or not spam?

Problems like these are called classification problems which demand separating your data (credit card transactions or emails) into two (More than two classes are also possible with an extension of this concept of logistic regression) different categories or clusters. If you are a doctor who wants to diagnose a possibly cancerous tumor by looking at the tissue image or a loan officer who wants to know whether the next customer is likely to default, logistic regression can come to your rescue.

Unlike linear regression where we estimate the trend of a continuous data using a linear approximation, logistic regression gives you Yes/No answers. (I would highly encourage you to read the previous article in this series about linear regression. Here’s the link — Linear regression in layman terms).

Let’s look at a few examples to see what logistic regression really tries to do.

Cancer detection

Suppose you are a scientist working at a cancer research hospital. You want to look at the shapes and sizes of different tumors and predict whether it is malignant or not. You’ve been given some samples marked with their true values which when plotted look like this.