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I am working with more than one dataset. So, I have to test my Random Forest Model over 4 datasets. The parameter grid I am taking for dataset D1 is not producing good results for dataset D2 and so on. I have tried multiple range of parameters but none is working for all datasets. Please suggest if their is any empirical/ analytical way by which I can set a common parameter grid for all datasets.

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Why are you keeping these data sets separate? If you want to keep them separate, then you could use these data sets as your folds for k-fold cross validation. Then choose the parameter set which leads to the best average performance.

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  • $\begingroup$ All these datasets have different dimensions, i.e. no two datasets have common features. So I can't club them together. $\endgroup$
    – Swarnima
    Jun 10, 2021 at 4:51

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