Regression and classification are many times confusing to many beginners in the field of Machine learning. Eventually, this will make it impossible for them to adopt the correct methodologies for solving problems with prediction.

Regression and classification are both types of supervised machine learning algorithms, where a model is trained along with correctly labeled data according to the current model.

Before we dig deep into understanding the variations between algorithms for regression and classification. Let’s understand each algorithm first.

## Regression Machine Learning Algorithm:

Regression algorithms estimate a continuous value based on the input variables. The primary objective of problems with regression is to approximate a mapping function based on the variables of input and output. If the target variable is a quantity such as income, ratings, height or weight, or a binary category likelihood then the regression model should be used. The 3 types of the regression model are as follows:

## 1. Simple linear regression:

In this type, you may use a straight line to measure the relationship between one independent variable and another dependent variable using simple linear regression, provided that both variables are quantitative.

## 2. Multiple linear regression:

This is an extension of simple linear regression. Based on the values of two or more independent variables, multiple regressions will predict the values of a dependent variable.

## 3. Polynomial regression:

Modeling or discovering a nonlinear relationship between dependent and independent variables is the main goal of polynomial regression.

Classification is a predictive model that approximates a mapping function, which can be labels or categories, from input variables to classify discrete output variables. In order to predict the mark or group of the given input variables, the mapping function of classification algorithms is responsible. There can be both discrete and real-value variables in a classification algorithm, but it requires that the examples be classified into one of two or more classes.

The different types of classification algorithms include:

## 1. Logistic Regression :

This is a classic predictive modeling technique and a popular classification algorithm for modeling binary categorical variables. The advantage of logistic regression is that it computes a prediction probability score of an event.

## 2. Decision tree classification :

A classification model is generated in this algorithm by creating a decision tree in which each tree node is a test case for an attribute and each branch coming from the node is a potential value for that attribute.

## 3. Random forest classification :

The random forest classification algorithm aggregates outputs from all the different decision trees to settle on the final output estimate, which is more accurate than any of the individual trees.

## 4. K-nearest neighbor :

The K-nearest neighbor algorithm assumes that in close proximity to one another, similar items exist. For the prediction of values of new data points, it uses feature similarity. Based on their similarity, the algorithm helps group related data points together. The algorithm’s main objective is to decide how likely it is for a data point to be part of a particular category.

The most important distinction between regression and classification is that while regression helps to predict a continuous quantity, distinct class labels are predicted by classification. Some overlaps are often found between the two types of machine learning algorithms.

A regression algorithm can predict a discrete value which is in the form of an integer quantity whereas a classification algorithm can predict a continuous value if it is in the form of a class label probability. Let me explain to you with an example.

## Example:

Consider a dataset that includes a specific university’s student results. In this instance, a regression algorithm can be used to predict any student’s height based on their weight, gender, diet, or subject major. In this case, we use regression, since height is a continuous quantity. For a person’s height, there is an infinite number of potential values.

Classification, on the other hand, may be used to analyze whether or not an email is a spam. The keywords in an email are checked by the algorithm and the address of the sender is to find out the likelihood that the email is spam. Similarly, while a regression model can be used to forecast the next day’s temperature, a classification algorithm can be used to decide if it is cold or hot.

Understanding the difference between regression and classification algorithms will allow you to more effectively apply machine learning concepts.

Hope you are having a clear difference between the regression and classification after reading this blog. Enjoy reading!!