What are Regression Algorithms in Supervised Machine Learning?

Regression algorithms are a subset of Supervised Machine Learning technique which are used to predict output values based on the input labeled data.

Opposite to classification technique which is used to make predictions where the output variable is a category (yes/no, spam/not spam), regression models are used to predict a continuous value (real numbered output - temperature, stock price, weight, etc). So, regression is one of the most widely used statistics and machine learning tools for deriving intelligence from data.

Photo source: Coursera.org

Which are the main applications of regression models in machine learning?

• to predict real estate values based on location, size, price, and other factors;
• financial forecasting
• trend analysis
• marketing
• time series predictions

Which are the most popular types of regression algorithm?

1. Simple Linear Regression;
2. Lasso Regression;
3. Logistic Regression;
4. Support Vector Machines;
5. Decision Tree Regression
6. Random Forest Regression
7. Multivariate Regression algorithm;
8. Multiple Regression Algorithm;
9. Polynomial Regression.

Resources:

1. Top 6 Regression Algorithms Used In Data Mining And Their Applications In Industry
2. Regression in Machine Learning
3. What is regression in machine learning?
4. Machine Learning: Regression