3/2/2024 0 Comments Data dredging eda![]() ![]() In today's data-driven world, understanding and harnessing the power of data is crucial for driving business success. > # additive model - scaled predictors > vif( lm(y ~ cx1 + cx2, data)) cx1 cx2ġ.743817 1.743817 Multiple Linear Regression Assumptionsġ.743817 1.743817 > # multiplicative model - raw predictors > vif( lm(y ~ x1 * x2, data)) x1 x2 x1:x2ħ.259729 5.913254 16.949468 Multiple Linear Regression Assumptions > # multiplicative model - raw predictors > vif( lm(y ~ x1 * x2, data)) x1 x2 x1:x2ħ.259729 5.913254 16.I've just uploaded a new GitHub repository focused on data preprocessing, EDA, and feature engineering for big market sales data! □□ ![]() Some prefer \(>3\) Multiple Linear Regression Assumptions \] Strong when \(R^2 \ge 0.8\) Multiple Linear Regression Variance inflation Multiple Linear Regression Variance inflation ![]() \] Centering data Multiple Linear Regression CenteringĪssumptions Multiple Linear Regression Assumptions ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |