# Introduction to Logistic Regression

Learn about classification problems and logistic regression.

## We'll cover the following

In this chapter of the course, we’ll discuss:

The classification problem and logistic regression to find the answer to our problem.

How to interpret the results from logistic regression through the confusion matrix.

## Overview

In the classification problems, rather than predicting a continuous or quantitative output value (that is, today’s stock price, house price, and others), we are interested in nonnumerical value, a categorical or qualitative output (that is, the stock index increase or decrease). In this section, our focus will be on learning logistic regression as a method for classification. The logistic regression model is one of the most widely used machine learning algorithms for binary classification problems.

### Examples of binary classification

The convention for binary classification is to have two classes, 0 and 1, like the followings:

Win or loss

Pass or fail

Dead or alive

Spam or ham email

Insurance or loan defaults (Yes/No or 1/0)

Healthy or sick (Yes/No or 1/0)

## Classification problem

Linear regression is not appropriate for a qualitative (classification problem) response. Let’s try to understand with a simple example below:

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