Regression problems and classification problems are the two most common types of prediction problems in machine learning.
What are regression and classification problems?
A regression problem is one that, given input variables (features) and a continuous output variable (label), builds a function to predict the value of the output variable. In other words, the goal of a regression problem is to predict a continuous output value, such as predicting house prices, stock prices, sales, etc. Regression problems usually use regression analysis techniques such as linear regression, polynomial regression, decision tree regression, etc.
Classification problems are given input variables (features) and a discrete output variable (label), building a function to predict the category of the output variable. In other words, the goal of a classification problem is to predict a discrete output value, such as classifying an image as a cat or a dog, predicting whether an email is spam or ham, etc. Classification problems usually use classification algorithms such as logistic regression, support vector machines, decision tree classification, etc.
The difference between a regression problem and a classification problem is the type of output variable. The output variables of regression problems are continuous while those of classification problems are discrete. In machine learning, it is usually necessary to select appropriate algorithms and evaluation indicators according to the requirements of the task to solve regression or classification problems.
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