Classification and regression are two major prediction problems in machine learning that are usually dealt with in data mining and machine learning. Both are supervised learning algorithms and are used to predict in machine learning and work with labeled datasets. However, their differing approach to machine learning problems is their point of divergence.
Classification in Machine Learning
Classification is the process of finding or discovering a model or function that 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 the input, and then the labels are predicted for the data. The mapping function of classification algorithms is responsible for predicting the label or category of the given input variables. A classification algorithm can have both discrete and real-valued variables, but it requires that the examples be classified into one of two or more classes.
Some examples of classification algorithms are:
- Decision Tree
- Random Forest Classifier
- K – Nearest Neighbors
- Support Vector Machine
Classification algorithms are used for things like email and spam classification, predicting the willingness of bank customers to pay their loans, and identifying cancer tumor cells.
Regression in Machine Learning
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. Regression algorithms are used to predict continuous values such as price, salary, age, etc. .
Some examples of regression algorithms are:
- Linear Regression
- Polynomial Regression
- Decision Tree Regression
- Random Forest Regression
Regression algorithms solve problems like house price prediction, rainfall prediction, etc. .
Difference between Classification and Regression
The most significant difference between regression and classification is that while regression helps predict a continuous quantity, classification predicts discrete class labels. There are also some overlaps between the two, and some algorithms may need both classification and regression approaches.
In summary, classification and regression are two major prediction problems in machine learning. Classification is used to predict discrete class labels, while regression is used to predict continuous values.
