DAAL model created from XGBoost model doesn't match

[DK0]

I trained an XGBoost model with default hyper-parameters on a trivial data set (see attached code) and then used

daal4py.get_gbt_model_from_xgboost

to create a daal model.

My XGBoost model worked fine but the daal model was useless!

	y_train	xgb - y_train	daal - y_train
count	101	101	101
mean	50	-0.00012	42.90
std	29.300171	0.03422	26.94
min	0	-0.07770	-0.02
25%	25	-0.01358	19.96
50%	50	0.00177	42.05
75%	75	0.01322	65.98
max	100	0.07726	89.98

Any idea what is wrong with the (short) attached code (or the environment)?

Steps to reproduce:

1. Install the Intel Distribution for Python  on windows10.
2. Install the Intel® oneAPI Data Analytics Library (oneDAL) toolkit.
3. conda install xgboost
4. Run the attached (short) python script

[DK0]

Solved!

It turns out that the DAAL model uses a permutation of the features. If I train XGBoost on ["X1", "X2"] I need to feed the DAAL model ["X2", "X1"]. I figured it out after coming across this thread which also suggests:

Additionally, calling .dump_model() on both your Python and C++ model objects will yield the same Decision Trees, but the Python one will have all the feature names and the C++ one will likely have f0, f1, f2, .... You can compare these two to get your actual column ordering, and then your predictions will match across languages(Not entirely, b/c of rounding).

[JaideepK_Intel]

Hi,

Thank you for posting in Intel Communities.

Glad to know that your issue is resolved. Thanks for sharing the solution with us. Can we close this case?

Thanks,

Jaideep

[JaideepK_Intel]

Hi,

We have not heard back from you. This thread will no longer be monitored by Intel. If you need further assistance, please post a new question.

Thanks,

Jaideep