What is the difference between classification and regression?
Classification and regression are two fundamental problems in machine learning.
Classification is the task of predicting a discrete class label for a given input, whereas regression is the task of predicting a continuous numerical value.
For example, if we have a dataset of images of fruits and we want to classify them as apples, bananas or oranges, this is a classification problem. On the other hand, if we want to predict the price of a house based on its features like the number of rooms, size, location, etc., this is a regression problem.
In classification, the output is a categorical variable, while in regression, it is a numerical variable. The goal in classification is to assign a label to a new instance that is not seen before, while in regression, the goal is to predict the value of a continuous variable.
To solve a classification problem, we typically use algorithms such as decision trees, support vector machines (SVM), logistic regression, and neural networks, while for regression problems, we can use linear regression, decision trees, random forests, and neural networks.
In summary, the main difference between classification and regression is the type of output variable we want to predict - categorical or numerical.