Unfolding Logistic Regression. Don’t get confused with the name as it says regression but Logistic Regression is a supervised learning algorithm which is used for carrying out classification tasks.

After reading this article you’ll be equipped with the following,

- Hypothesis Function
- Sigmoid Function
- Cost Function

Don’t get confused with the name as it says regression but Logistic Regression is a supervised learning algorithm which is used for carrying out classification tasks.

To understand classification better let’s take an example,

Let’s say A college wants to classify whether the students will get the admission or not based on there exam scores, using the historical data they have. This task can be done using logistic regression.

Let’s start with the hypothesis function used in Logistic Regression.

“Hypothesis function approximates a target function for mapping inputs to outputs.”

Hypothesis Function and Sigmoid function which is used in Logistic Regression is given below,

1. Hypothesis Function and 2. Sigmoid Function

Graphical representation of the **sigmoid function** is,

mathematics supervised-learning logistic-regression machine-learning

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Logistic Regression is an omnipresent and extensively used algorithm for classification. It is a classification model, very easy to use and its performance is superlative in linearly separable class.