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dmitryikh / leaves

pure Go implementation of prediction part for GBRT (Gradient Boosting Regression Trees) models from popular frameworks
MIT License
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xgEnsemble prediction results are different from xgboost in python #50

Closed EleComb closed 5 years ago

EleComb commented 5 years ago

I traning and testing data with xgboost in python, then use leaves in production env. The more infos are there:

In Python xgb testing, The data structure that I set up with pd.DataFrame is [0:value1 1:v2, 2:v3, ... , n:v(n+1)] the value1 is any value int type. and v2, ... , v(n+1) is float64 type. The 0 is prediction value. This result is testing result.

And this structure: [feature1:v2, f2:v3, ... , f(n):v(n+1)] This result is NOT testing result.

In Golang and I use leaves XGEnsembleFromFile->model.PredictCSR() also the result is NOT testing result.

I have tried to find how to solve it for over 5h like add {0:0} to first features group, but for my ridiculous low English level and Math level I can't find it. What's wrong with my testing data = =

dmitryikh commented 5 years ago

Could you please provide golang code?

EleComb commented 5 years ago

I checked it again and found it seems my model was last version! = = Thank you dmitryikh! (....