Regression algorithms are members of the Supervised Machine Learning algorithm family, which is a subset of machine learning algorithms. One of the key characteristics of supervised learning algorithms is their ability to forecast the value of fresh data by modeling dependencies and interactions between the intended output and input variables. Regression algorithms forecast output values based on input attributes from the system's data. The standard process is for the algorithm to create a model based on the attributes of training data and then use the model to forecast the value of fresh data.
Here's an excellent definition of Regression from Oracle: a data mining function that predicts a number. As an example, consider how regression models are used to forecast real estate value based on location, size, and other criteria. Regression models are now used in a variety of fields, including financial forecasting, trend analysis, marketing, time series prediction, and even medication response modeling. Regression techniques that are popular include linear regression, regression trees, lasso regression, and multivariate regression.
Types of Regression Models
Simple regression model − This is the most basic regression model in which predictions are formed from a single, univariate feature of the data.
Multiple regression model − As name implies, in this regression model the predictions are formed from multiple features of the data.
The following are some of the applications of ML regression algorithms:
1. Forecasting or Predictive Analysis Forecasting or predictive analysis is an important use of regression. For example, we can forecast GDP, oil prices, or any other quantitative data that fluctuates over time.
2. Optimization With the aid of regression, we may optimize business processes. A store manager, for example, can develop a statistical model to determine when consumers are most likely to arrive.
3. Error repair In business, making the right decisions is just as crucial as improving the business process. Regression may assist us in making proper decisions as well as in rectifying previously implemented decisions.
4. Economics It is the most often utilized economic instrument. Regression may be used to forecast supply, demand, consumption, inventory investment, and so on.
5. Finance, A financial organization is constantly interested in decreasing its risk portfolio and learning about the elements that influence its clients. All of this may be predicted using a regression model.