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DATA WITH PARTIAL MULTICOLLINEARITY HELPS TO RESOLVE OVERFIT PROBLEM IN LINEAR MODELS
2022
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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.
doi:10.24412/2701-8369-2022-25-33-35
fatcat:bf5lv4tftrch7bl6seicehd3me