Timeline for Logistic Regression: multicollinearity and Kappa statistics
Current License: CC BY-SA 4.0
3 events
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| Oct 15, 2018 at 16:27 | comment | added | Michael Grogan | Are you using the Kappa value to detect multicollinearity in its own right? Also, when you are detecting accuracy have you split the data into training and test partitions? If not, you might be overfitting. I would recommend using VIF to see if multicollinearity is present in the first instance, and you'll be better able to work from there. | |
| Oct 15, 2018 at 13:35 | comment | added | AlGrasso | Hi Michael, thanks for your prompt and insightful reply. In my dataset I have 3 variables: 2 predictive X made of quantitative continuous variables and 1 categorical binary dependent variable Y (following the dictates of the logistic regression). After calling postResample() I got an accuracy of the model of 93.18% (which sounds too good to be true) with Kappa value at 0. I am confident of the low incidence of the predictors (X) over Y (as also confirmed from the type="class" argument in the predict function) which made me wonder: how can I have such high accuracy from such variables? Thanks | |
| Oct 15, 2018 at 13:16 | history | answered | Michael Grogan | CC BY-SA 4.0 |