I read about the backward elimination and I understood its working as:
- Select the significance level.
- Fit our model with all independent variables.
- Consider variables with the highest p-value. If the p-value is greater than the significance level, remove that variable.
- Again build the model with leftover independent variables.
- Repeat the process until the removal of any variable will affect the accuracy of the model.
But on the other hand, I am not able to understand the working of RFE. If RFE also eliminates the variables on every iteration then what is the difference between both of these methods.