How does Feature Selection - Forward Elimination work in detail?
Hello together,
I have a question regarding regarding the picking process of Forward Elimination. The documentation of RapidMiner tells us that: "The Forward Selection operator starts with an empty selection of attributes and, in each round, it adds each unused attribute of the given ExampleSet. For each added attribute, the performance is estimated using the inner operators, e.g. a cross-validation. Only the attribute giving the highest increase of performance is added to the selection. Then a new round is started with the modified selection. [...]"
What does this mean in detail? Assumed I have dataset with 10 attributes from a1,a2, ... to a10. Does Forward Selection sequentially go from a1 to a2 and then to a3 until no increase in performace is reached or what is meant with " Only the attribute giving the highest increase of performance is added to the selection."?
Thank you in advance for your responses!
Best regards,
Fatih
I have a question regarding regarding the picking process of Forward Elimination. The documentation of RapidMiner tells us that: "The Forward Selection operator starts with an empty selection of attributes and, in each round, it adds each unused attribute of the given ExampleSet. For each added attribute, the performance is estimated using the inner operators, e.g. a cross-validation. Only the attribute giving the highest increase of performance is added to the selection. Then a new round is started with the modified selection. [...]"
What does this mean in detail? Assumed I have dataset with 10 attributes from a1,a2, ... to a10. Does Forward Selection sequentially go from a1 to a2 and then to a3 until no increase in performace is reached or what is meant with " Only the attribute giving the highest increase of performance is added to the selection."?
Thank you in advance for your responses!
Best regards,
Fatih