On the other hand, using say CatBoost I don’t see how you can ensure there’s only 1 sample per leaf – catboost uses oblivious decision trees which applies the same split for every node at the current level.

]]>Greeting and Regards

At first, thanks for learning and explain.

In your data set, you have some samples that each sample contains a number of attributes. for example, we have 100 samples that each sample contain 30 attributes.

My question is whether can we use this algorithm for a data set that has 100 samples with 30 attributes, Each feature has three parts? ,

i,e: we have a population of samples, that each sample contain 56 feature and each feature contains 3 parts. ]]>