Linear models make the following assumptions over the independent variables X, used to predict Y: There is a linear relationship between X and the outcome Y The independent variables X are normally distributed There is no or little co-linearity among the independent variables Homoscedasticity (homogeneity of variance) Examples of linear […]
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The post Feature Engineering Tutorial Series 4: Linear Model Assumptions appeared first on KGP Talkie.