In Regression, the output variable must be of continuous nature or real value. |
In Classification, the output variable must be a discrete value. |
The task of the regression algorithm is to map the input value (x) with the continuous output variable(y). |
The task of the classification algorithm is to map the input value(x) with the discrete output variable(y). |
Regression Algorithms are used with continuous data. |
Classification Algorithms are used with discrete data. |
In Regression, we try to find the best fit line, which can predict the output more accurately. |
In Classification, we try to find the decision boundary, which can divide the dataset into different classes. |
Regression algorithms can be used to solve the regression problems such as Weather Prediction, House price prediction, etc. |
Classification Algorithms can be used to solve classification problems such as Identification of spam emails, Speech Recognition, Identification of cancer cells, etc. |
The regression Algorithm can be further divided into Linear and Non-linear Regression. |
The Classification algorithms can be divided into Binary Classifier and Multi-class Classifier. |