Stepwise regression is used when there is uncertainty about which of a set of predictor variables should be included in a regression model. It works by adding and/or removing individual variables from the model and observing the resulting effect on its accuracy.
Stepwise regression is no longer regarded as a valid tool for dimensionality reduction because it produces unstable results that heavily overfit the training data, but see least angle regression (LARS).
- alias
- subtype
- has functional building block
- FBB_Dimensionality reduction
- has input data type
- IDT_Vector of quantitative variables
- has internal model
- INM_Function
- has output data type
- has learning style
- LST_Supervised
- has parametricity
- PRM_Parametric
- has relevance
- REL_Obsolete
- uses
- ALG_Least Squares Regression ALG_Logistic regression
- sometimes supports
- ALG_Least Squares Regression ALG_Logistic regression
- mathematically similar to