Which dtype performs better when training a randomforest in python?
Nearly all scikit-learn estimators will convert input data to float before running the algorithm, regardless of the original types in the array. This holds for the random forest implementation.
I was trying to train a randomforest classifier in python. However, in my original pandas.dataframe, there are float64, object, datetime64, int64 and bool dtypes(nearly all kinds of dtypes allowed in pandas). 

Is it necessary to convert a bool to float or int?
For a two-value object column, should I convert it to bool, int, or float? Which one would perform better? Or does it not matter?

Thanks!