Classification: Limited

Hi Team,

We are trying to use Gradient Boosting Classification algorithm and in Python 
we tried using Sklearn library and in Pyspark we are using ML library.

We have around 45k dataset which is used for training and that dataset is 
taking around 3 to 4 hours in python but in Pyspark it is taking more than 18 
hours for the same hyper parameters used between Python and Pyspark.

We tried Pyspark by repartitioning the dataframe and can see a little 
improvement in performance but still we are not able to get timings near to 
Python.

We have live run which need to evaluation predictions for 40million plus data 
and data resides in Hadoop. So it is difficult to get that huge amount to data 
to different system and convert to Pandas dataframe and run against Python.

So we are trying to train the same model against Pyspark so, that I can do the 
evaluation against trained model in Pyspark but, here the concern that we have 
is the time taken for training is very high and we want to check what will be 
the general approach followed in these kind of scenarios.


Thanks,
Anupama.
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