I want to use xgb.train() to train my xgb model and my DMatrix is created by scipy.sparse, which includes many sparse data like following:
# csr_array is the list of scipy.sparse.csr.csr_matrix.
XGBoost works well with CPU(“tree_method” is “exact”) but when I use GPU (“tree_method” is “exact”) XGB can’t return correct model (train auc is not correct).
I would appreciate it if anyone could advise how to make GPU work well.
PS, if I use dense data, DMatrix is created by dense data,
both CPU and GPU work well. So do I need other action using GPU training for sparse data?