In this case, the design itself – and not just certain variables – contributes to overfitting. Independence is still an important consideration in RF, and you are referring to strong non-independence. So, specific advice on chronological ML: RE-EM; repeated-measures ; and more clustered data.

Secondly, there are pre-analysis common-sense approaches: if you know that some ‘variables should not actually be included’, they need to be eliminated a priori. This is not the same as ‘feature selection’ but eliminating junk variables which would not be helpful in the forward model, based on the information you provide.

Aside from that, there are internal approaches to account for some of the overfitting, such as this beautiful simulation of mtry greediness as well as other fine-tuning methods.

In this case, the design itself – and not just certain variables – contributes to overfitting. Independence is still an important consideration in RF, and you are referring to strong non-independence. So, specific advice on chronological ML: RE-EM; repeated-measures ; and more clustered data.

Secondly, there are pre-analysis common-sense approaches: if you know that some ‘variables should not actually be included’, they need to be eliminated a priori. This is not the same as ‘feature selection’ but eliminating junk variables which would not be helpful in the forward model, based on the information you provide.

Aside from that, there are internal approaches to account for some of the overfitting, such as this beautiful simulation of mtry greediness as well as other fine-tuning methods.