You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I have the impression that the objective function implemented in the code might be incorrect.
The returned value of objective_function is var - (left_val + right_val), which is the reduction in variance. According to the paper, the split to be chosen has the LARGEST reduction in variance.
Therefore, in train_recurse it should be (objective >= maximum_objective) instead of (objective < minimum_objective) I think, with maximum_objective initialized to 0.
Otherwise, you are rewarding nodes that divide the parent set into one empty set and a child set with the same elements as the parent set.
The text was updated successfully, but these errors were encountered:
I have the impression that the objective function implemented in the code might be incorrect.
The returned value of objective_function is var - (left_val + right_val), which is the reduction in variance. According to the paper, the split to be chosen has the LARGEST reduction in variance.
Therefore, in train_recurse it should be (objective >= maximum_objective) instead of (objective < minimum_objective) I think, with maximum_objective initialized to 0.
Otherwise, you are rewarding nodes that divide the parent set into one empty set and a child set with the same elements as the parent set.
The text was updated successfully, but these errors were encountered: