xgboost precision

[KOVI89alipes]

Hi,

I found an issue with xgboost example https://github.com/thesps/conifer/blob/master/examples/xgboost_to_hls.py

y_hls and y_xgb aren't close

y_hls = expit(model.decision_function(X_test))
y_xgb = bst.predict(dtest)
diff  =  y_xgb  - y_hls 
print(diff[abs(diff)>0.05])

[-0.13502171  0.06955624 -0.1099674  -0.2427507  -0.14311438 -0.0606428
  0.08703702 -0.054607   -0.41907781 -0.12813512  0.28282228 -0.21637464
  0.31876776  0.26711339 -0.14989728 -0.05887845 -0.06809392  0.12303647
 -0.08492118 -0.07751923 -0.05739652 -0.11599926 -0.14425865 -0.08459726
 -0.12540119 -0.06227853 -0.27874367 -0.29141373  0.12563779 -0.22311496
 -0.13287621 -0.17924546 -0.10041202]

As soon as output is normalized to 1, absolute error up to 0.31 seems to be too high for practical usage. Is it a known issue?

[thesps]

Hi, these examples are not at all optimized in terms of the precision used, which can have an effect on numerical accuracy. Did you try any tuning?

[KOVI89alipes]

Nope, just vanilla example out of the box