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Regression

It's a statistical method used in machine learning to model the relationship between a dependent variable (target) and one or more independent variables (features). The goal of regression is to predict the value of the target variable based on the values of the features.

Widely used for prediction, estimation, hypothesis testing, and understanding causal relationships between variables.

Dependent and Independent Variables​

  • Dependent Variable (Target, Y): The variable we want to predict or explain. Output of systems, may not take values freely. Also called response variable, outcome variable, or predicted variable.
  • Independent Variables (Features, X): The variables used to predict the dependent variable. Inputs to systems, may take values freely. Also called explanatory variables, predictor variables, or input variables.

Regression Hypothesis​

The regression hypothesis is that there is a linear relationship between the dependent variable and the independent variables. One of the common types of linear regression is least squares regression, which aims to minimize the sum of the squared differences between the predicted and actual values of the dependent variable.