Regression: Difference between revisions
Jump to navigation
Jump to search
| (3 intermediate revisions by the same user not shown) | |||
| Line 6: | Line 6: | ||
* [[Statistical Concepts#Regression|Statistical Concepts]] | * [[Statistical Concepts#Regression|Statistical Concepts]] | ||
* [[Machine_Learning# | * [[Machine_Learning#Supervised_Learning|Supervised Learning]] | ||
* [[Classification]] | |||
=Overview= | =Overview= | ||
| Line 17: | Line 18: | ||
A common example of regression analysis is linear regression where the dependent variable is modeled as a linear function of the features. | A common example of regression analysis is linear regression where the dependent variable is modeled as a linear function of the features. | ||
=Organizatorium= | |||
* Linear regression with one variable - univariate linear regression. | |||
Latest revision as of 22:10, 18 December 2017
External
Internal
Overview
Regression (or regression analysis) is a statistical process for estimating the relationship among variables. Typically, there's an output or dependent variable, and one or more predictors or features. Regression models estimate how the "typical" value of the output variable changes when one of the future is changed, but others are fixed constant.
Regression refers to the estimation of continuous response (dependent) variables, as opposed to the discrete response variable used in classification.
Linear Regression
A common example of regression analysis is linear regression where the dependent variable is modeled as a linear function of the features.
Organizatorium
- Linear regression with one variable - univariate linear regression.