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Research Topics for Regression Algorithms

Research Topics for Regression Algorithms

   Regression is a prominent technique of supervised machine learning. It is the process of analyzing the relationship between the predictor output variable and the dependent target variables, and it gives as a result of continuous numerical value output. It is a broadly used statistical approach in supervised machine learning. Regression plays an important role in machine learning due to its capability of handling real or continuous values. Regression models use the independent variables and their corresponding continuous dependent to learn the specific association between inputs and corresponding outputs. Regression algorithms are categorized under simple and multiple regression models. Simple regression model includes Simple Linear Regression algorithm.
   Multiple regression model includes Logistic Regression, Stepwise regression, Elastic Net Regression, Bayesian Linear Regression Polynomial Regression, Support Vector Regression, K-nearest neighbor, Decision Tree Regression, Random Forest Regression, Ridge Regression, and Lasso Regression algorithms. The most popular regression applications are House price prediction, road accidents due to rash driving, prediction of rain using temperature, trend analysis, marketing, time series modeling, and forecasting. Recent research areas of regression are forest above-ground biomass estimation, prognostication in traumatic brain injury, solar radiation estimation, forward stock price forecast, and more.