# Difference between Regression and Classification Algorithms

Classification and regression are the very basic and important topics in machine learning. The article covers the major differences between Regression and Classification algorithms in machine learning.

When you have just started delving into Machine Learning, differentiating between Regression and Classification algorithms can be a bit confusing. Implementing the correct methodology while solving ML problems is the key to making accurate predictions.

Both are Supervised Learning Algorithms that use labeled data (aka training datasets) to train models to predict accurate outcomes.

If we represent the predicted output by *‘**y**‘* and the input data as *‘**x**‘*, then supervised algorithms are employed to estimate the mapping function *‘**f**‘*, such that ** y = f(x)**.

However, there’s a fundamental difference in their usage – the Classification algorithms basically predict a **categorical** outcome, and Regression algorithms are used to predict a **numerical** outcome.

In this blog, we will cover the following sections:

- Defining Regression Problems
- How Does a Regression Algorithm Work?
- Types of Regression Algorithms
- Defining Classification Problems
- How Does a Classification Algorithm Work?
- Types of Classification Algorithms
- Regression Vs. Classification Comparison Table
- Endnotes

**Defining Regression Problems**

Regression is a technique that predicts the **continuous quantity** outcome variable based on the independent variable(s).

Few more examples of regression problems –

- Price of a liter of petrol
- Value of a stock
- The popularity of a newly released album
- Sales revenue generated by a business

**How Does a Regression Algorithm Work?**

Regression algorithms attempt to approximate the mapping function *‘**f**‘ *based on the existing input data such that when new data *‘**x**‘ *is fed to the model, the numerical or continuous output *‘**y**‘* can be predicted as accurately as possible.

When dealing with regression problems, commonly linear, our goal is to find the best fit line for our data such that the equation ** y = f(x)** becomes linear, i.e.,

Let’s understand this through a fun little example – a company wants to predict the salary a person would draw based on the years of experience.

As the years of experience (*x*) increase, so does the salary (*y*). We can plot the known data for better visual understanding:

Our goal is to find a straight line, called the **regression line**, that best fits our plot. We can do this by finding the slope and intercept of this line. These values are actually the **regression coefficients**. With these values, our regression model will help predict the future salaries of employees based on their years of experience.

In the above example, we’re considering only one input variable (*x*), that is, the years of experience. However, there can be multiple factors affecting employee salaries. This would then become a **multi-linear regression** problem with many input variables (*xᵢ*).

Regression algorithms can be of **non-linear** types as well. Such algorithms model a non-linear relationship between the dependent (output) and independent (input) variables. They are used when the data shows a curvy trend.

**Types of Regression Algorithms**

Common regression algorithms include:

- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- Decision Tree Regression
- Random Forest Regression

**Defining Classification Problems**

Classification is a technique that predicts the **discrete** **class label** output to which the data element belongs.

Few more examples of classification problems –

- Spam texts/e-mails
- Segregation of waste
- Cancer detection
- Churn Prediction

**How Does a Classification Algorithm Work?**

Classification algorithms attempt to approximate the mapping function *‘f’ *basis the existing input data such that when new data *‘x’ *is fed to the model, we can predict the categorical or discrete output *‘y’* as accurately as possible.

Let’s understand this through a fun little example – Your friend has a high fever, and the doctor wants to run some tests to determine what disease he might have.

A classification model can be used for such medical diagnoses. One can build a **Disease Classifier Model **that considers the patient’s temperature and health records to predict whether this person has flu, pneumonia, or some other disease.

When training a classifier on a known dataset, you define a set of hyper-planes, called **decision boundary**, that separates the data points into specific classes, where the classification algorithm switches from one category to another.

For example, on one side of the decision boundary, data points are more likely to be called class A (or Disease A). While on the other side of the boundary, data points can be called class B (or Disease B). We use **Binary Classifiers** in case there are only two classes and **Multi-class Classifiers** for more than two class divisions.

**Types of Classification Algorithms**

Common classification algorithms include:

- Logistic Regression
- K-Nearest Neighbours
- Support Vector Machines
- Kernel SVM
- Naïve Bayes
- Decision Tree Classification
- Random Forest Classification

Note that, though the name is Logistic “Regression” it is actually a classification algorithm.

**Regression Vs. Classification Comparison Table**

Regression Algorithm |
Classification Algorithm |

In Regression, the output is a continuous or numerical value. | In Classification, the output is a discrete or categorical value. |

Regression model maps the input variable(x) with the continuous output variable(y). | Classification model maps the input variable(x) with the discrete output variable(y). |

In Regression, we find the best fit line that can predict the output accurately. | In Classification, we find the decision boundary that can divide the dataset into different classes. |

Regression algorithms solve regression problems such as house price prediction, cryptocurrency price prediction, etc. | Classification algorithms solve classification problems such as face detection, speech recognition, etc. |

Regression algorithms can be further divided into Linear and Non-linear Regression. | Classification algorithms can be divided into Binary classifiers and Multi-class classifiers. |

**Endnotes**

Regression and Classification algorithms are instrumental in solving Machine Learning problems. Hence, a clear understanding of choosing the correct model that deploys the best possible solution is necessary. Artificial Intelligence & Machine Learning is an increasingly growing domain that has hugely impacted big businesses worldwide. Interested in being a part of this frenzy? Explore related articles here.

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## FAQs

**How do you decide between classification and regression?**

In regression, the output variable must be continuous or real in nature. For classification, the output variable must be discrete. The task of a regression algorithm is to map input values u200bu200b(x) to continuous output variables (y).

**How does prediction depend on classification?**

Prediction is the process of identifying missing or unavailable numerical data for new observations. In classification, accuracy depends on finding class designations correctly. In forecasting, accuracy depends on how accurately a particular predictor can guess the value of the predictor attribute on new data.

**What is classification?**

Classification is the process of discovering or identifying designs or roles and helps to classify them into multiple categorical classes i.e. Discrete values. Classification labels the data under different labels according to certain parameters specified in the input and projects the labels onto the data.

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