Is it better to encode features like month and hour as factor or numeric in a machine learning model?

On the one hand, I feel numeric encoding might be reasonable, because month and hour are progressing processes (the fifth month is followed by the sixth month), but on the other hand I think categorial encoding might be more reasonable because of their cyclic nature ( the 12th month is followed by the first one).

Is there a general solution or convention for this?

Is it better to encode features like month and hour as factor or numeric in a machine learning model?

On the one hand, I feel numeric encoding might be reasonable, because time is a forward progressing process (the fifth month is followed by the sixth month), but on the other hand I think categorial encoding might be more reasonable because of the cyclic nature of years and days ( the 12th month is followed by the first one).

Is there a general solution or convention for this?

Is it better to encode features like month and hour as factor or numeric in a machine learning model?

On the one hand, I feel numeric encoding might be reasonable, because month and hour are progressing processes (the fifth month is followed by the sixth month), but on the other hand I think categorial encoding might be more reasonable because of their cyclic nature ( the 12th month is followed by the first one).

Is there a general solution or conevention for this?

Is it better to encode features like month and hour as factor or numeric in a machine learning model?

On the one hand, I feel numeric encoding might be reasonable, because month and hour are progressing processes (the fifth month is followed by the sixth month), but on the other hand I think categorial encoding might be more reasonable because of their cyclic nature ( the 12th month is followed by the first one).

Is there a general solution or convention for this?