Top Machine Learning Algorithms for Regression

Linear regression finds the optimal linear relationship between independent variables and dependent variables, thus makes prediction accordingly. The simplest form is y b0 b1x. When there is only one input feature, linear regression model fits the line in a 2 dimensional space, in order to minimize the residuals between predicted values and actual values. The common cost function to measure the magnitude of residuals is residual sum of squared (RSS). As more features are introduced, simple linear regression evolves into multiple linear regression y b0 b1x1 b2x2 … bnxn. Feel free to check out my article if you want the specific guide to simple linear regression model.