Walking you through OLS, its assumptions, testing them and using advanced ML algorithms when they don't hold. For example, when there is Heteroskedasticity, Weighted Least Squared Regression Is Used. When there is non linearity between the predictor and the dependent variable, we try Piecewise Reg or Polynomial Reg.. Follow me for a fun ride :)
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Walking you through OLS, its assumptions, testing them and using advanced ML algorithms when they don't hold. For example, when there is Heteroskedasticity, Weighted Least Squared Regression Is Used. When there is non linearity between the predictor and the dependent variable, we try Piecewise Reg or Polynomial Reg.. Follow me for a fun ride :)
Agoons20/Understanding_ML
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Walking you through OLS, its assumptions, testing them and using advanced ML algorithms when they don't hold. For example, when there is Heteroskedasticity, Weighted Least Squared Regression Is Used. When there is non linearity between the predictor and the dependent variable, we try Piecewise Reg or Polynomial Reg.. Follow me for a fun ride :)
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