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DATA WITH PARTIAL MULTICOLLINEARITY HELPS TO RESOLVE OVERFIT PROBLEM IN LINEAR MODELS
release_bf5lv4tftrch7bl6seicehd3me
by
O. Solovei
Published
by Deutsche Internationale Zeitschrift für zeitgenössische Wissenschaft.
2022
Abstract
Linear regression models are built on raw data which is supposed to have linear relation between predictors and target and no multicollinearity between predictors [1]. However, multicollinearity can be complete or partial and the second type of multicollinearity may be successfully utilized in Ridge regression algorithms to solve overfit problem.
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