Hi,

To get reproducible model, you have to set the random_state.

Best regards,
Arnaud


On 16 Sep 2014, at 12:08, Debanjan Bhattacharyya <b.deban...@gmail.com> wrote:

> Hi I recently participated in the Atlas (Higgs Boson Machine Learning 
> Challenge)
> 
> One of the models I tried was GradientBoostingClassifier. I found it 
> extremely non deterministic.
> So if I use
> est = GradientBoostingClassifier(n_estimators=100, 
> max_depth=10,min_samples_leaf=20,max_features=6,verbose=1)
> 
> and train several times on the same training set (full). I end up having 
> models (significantly different in size - I mean pickle output) which predict 
> differently on the same instance. The difference is on the scale of 20 to 30% 
> (so I have seen values varying between 0.7x and 0.4x) on the same instance. 
> Even the (ordering) top 20 features (out of 30) differ from model to model 
> quite significantly.
> 
> Can someone tell me a bit more in details about this uncertainty.
> 
> The train data set can be downloaded from here 
> https://www.kaggle.com/c/higgs-boson/data
> 
> 
> 
> Thanks
> 
> Regards  
> 
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