Linear models, use a simple formula to find a best-fit line through a set of data points. Tree-based models, use a series of “if-then” rules to generate predictions from one or more decision trees.
They describe a continuous response variable as a function of one or more predictor variables. They can help you understand and predict the behavior of complex systems or analyze experimental, financial, and biological data. Linear regression is a statistical method used to create a linear model.
It is the equation of a line that describes the relationship between a predictor variable X and an outcome variable Y. You can think of a linear model as a function f that receives some input X and returns an output Y.