Both classification and regression algorithms are extensively used in machine learning. However, there are many differences between them, which ultimately determine their use or purpose. The main difference is that while classification algorithms are used to classify or predict discrete values like male-female or true-false, regression algorithms are used to forecast non-discrete, continuous values such as salary, age, price, etc. Decision trees, random forest, Kernel SVM, and logistic regression are some of the most common classification algorithms, while simple and multiple linear regression, support vector regression, polynomial regression, and decision tree regression are some of the most popular regression algorithms used in machine learning.