**Introduction to Logistic Regression**

## Learn how logistic regression is implemented from scratch

Logistic regression is a classification algorithm, which is pretty popular in some communities especially in the field of biostatistics, bioinformatics and credit scoring. It’s used to assign observations a discrete set of classes(target).

**Why not logistic classification?**

Logistic regression is strictly **not** a classification algorithm on its own. Because its output is a probability(a continuous number between 0 and 1), it will be only a classification algorithm in combination with a decision boundary which is generally fixed at 0.5

For example, if we have a logistic regression that has to predict whether an email is a spam or not, the output of the function will be 0.2 or 0.7. By default, the logistic regression makes it easier for us by assigning 0(not a spam) for the one who got 0.2 and assigning 1(a spam) for the latter. The threshold is 0.5 but we can manage to change it.

**Comparison to linear regression**

Let’s suppose you have data on time spent studying, playing and exam score.

**Linear Regression: **because it’s a regression, meaning that the output is continuous, it could help us predict the student test score between a certain range.

**Logistic Regression:** this one could help us predict whether the student passed the exam or not. Its…