How does the Recursive Feature elimination(RFE) works and how it is different from Backward elimination?

@neha30 wrote:

I read about the backward elimination and I understood its working as:

1. Select the significance level.
2. Fit our model with all independent variables.
3. Consider variables with the highest p-value. If the p-value is greater than the significance level, remove that variable.
4. Again build the model with leftover independent variables.
5. 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.

Thanks

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