## The Superman Algorithm: Logistic Regression

**What is Logistic Regression?**

Logistic Regression is one of the most popular Machine Learning algorithms for binary classification. It is a simple but powerful Algorithm which can be used as baseline, easy implementation, and can do well enough in many tasks.

An example of a Logistic Regression problem is an algorithm used for cancer detection that takes screened picture as an input and give output as patient has cancer (1) or not (0).

**Why Logistic Regression?**

- It does not require much computational resources, highly interpretable, doesn’t require input features to be scaled, and its outputs well-calibrated predicted probabilities.
- We can achieve 95% accuracy with MNIST dataset using Logistic Regression only, it’s not a great result, but its more than good enough to make sure you pipeline works.
- Application in Deep Learning, we can think of each neuron in the network as a Logistic Regression. Moreover, the final layer of a neural network can also be a simple logistic regression.

**How it works? **

It’s a classification algorithm used when response variable is categorical. The idea of Logistic Regression is to find a relationship between features and probability of particular outcome.

These probabilities must then be transformed into binary values in order to make a prediction. This is the task of the logistic function, also called Sigmoid function. The Sigmoid-Function is S-shaped curve that can take any real-valued number and map it into a value between the range of 0 and 1. This value between 0 and 1 will then be transformed into either 0 or 1 using a threshold classifier.

The picture below illustrates the steps that logistic regression gives the desired output:

Below you can see how the logistic function (Sigmoid function) which is converted from Linear regression:

By: Sumit Ranjan