Hello,
When running the daal4py decision tree model on a dataset, we notice that it takes longer to run that the sklearn decision tree model. This observation is unusual, as most of the models we've tested have a faster runtime with the daal4py implementation than the sklearn implementation. Do we know what the cause of the slow performance is for the daal4py decision tree model? Could it be that since the decision tree in daal4py does not have all the same hyperparameters available in the sklearn decision tree model, the daal4py decision tree has a set of default hyperparameters that lead to slower performance (e.g. min_samples_split, max_features, etc. which are adjustable for the sklearn model but not the daal4py model)?
