There are numerous algorithms proposed and available, which can be used for the regression analysis. For example, LR tries to find relationships and dependencies between variables. It models the relationship between a continuous dependent variable y (that is, a label or target) and one or more independent variables, x, using a linear function. Examples of regression algorithms include the following:
- Linear regression (LR)
- Generalized linear regression (GLR)
- Survival regression (SR)
- Isotonic regression (IR)
- Decision tree regressor (DTR)
- Random forest regression (RFR)
- Gradient boosted trees regression (GBTR)
We start by explaining regression with the simplest LR algorithm, which models the relationship between a dependent variable, y, which involves a linear combination of interdependent variables, x:
In the preceding equation letters, β0...
