Classification and Regression are two major prediction problems which are usually dealt with Data mining and machine learning.
Classification is the process of finding or discovering a model or function which helps in separating the data into multiple categorical classes i.e. discrete values. In classification, data is categorized under different labels according to some parameters given in input and then the labels are predicted for the data.
The derived mapping function could be demonstrated in the form of “IF-THEN” rules. The classification process deal with the problems where the data can be divided into binary or multiple discrete labels.
Let’s take an example, suppose we want to predict the possibility of the wining of match by Team A on the basis of some parameters recorded earlier. Then there would be two labels Yes and No.
Fig : Binary Classification and Multiclass Classification
Regression is the process of finding a model or function for distinguishing the data into continuous real values instead of using classes or discrete values. It can also identify the distribution movement depending on the historical data. Because a regression predictive model predicts a quantity, therefore, the skill of the model must be reported as an error in those predictions
Let’s take a similar example in regression also, where we are finding the possibility of rain in some particular regions with the help of some parameters recorded earlier. Then there is a probability associated with the rain.
Fig : Regression of Day vs Rainfall (in mm)
Comparison between Clasification and Regression:
|Basic||Mapping Function is used for mapping of values to predefined classes.||Mapping Function is used for mapping of values to continuous output.|
|Involves prediction of||Discrete values||Continuous values|
|Nature of the predicted data||Unordered||Ordered|
|Method of calculation||by measuring accuracy||by measurement of root mean square error|
|Example Algorithms||Decision tree, logistic regression, etc.||Regression tree (Random forest), Linear regression, etc.|
Attention reader! Don’t stop learning now. Get hold of all the important CS Theory concepts for SDE interviews with the CS Theory Course at a student-friendly price and become industry ready.
- Regression and Classification | Supervised Machine Learning
- ML | Why Logistic Regression in Classification ?
- ML | Logistic Regression v/s Decision Tree Classification
- ML | Linear Regression vs Logistic Regression
- Getting started with Classification
- Multiclass classification using scikit-learn
- Basic Concept of Classification (Data Mining)
- Python | Image Classification using keras
- ML | Cancer cell classification using Scikit-learn
- ML | Using SVM to perform classification on a non-linear dataset
- ML | Classification vs Clustering
- OpenCV and Keras | Traffic Sign Classification for Self-Driving Car
- Classification of Data Mining Systems
- An introduction to MultiLabel classification
- Difference between Classification and Clustering in DBMS
- Multi-Label Image Classification - Prediction of image labels
- One-vs-Rest strategy for Multi-Class Classification
- Handling Imbalanced Data for Classification
- Image Classification with Web App
- Sentiment Classification Using BERT
If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to email@example.com. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.
Improved By : Akanksha_Rai