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From "SRITHALAM, ANUPAMA (Risk Value Stream)" <Anupama.Sritha...@lloydsbanking.com.INVALID>
Subject Apache ML Agorithm Solution
Date Wed, 07 Apr 2021 16:12:58 GMT
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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Conduct Authority.

Lloyds Bank Corporate Markets Wertpapierhandelsbank GmbH is a wholly-owned subsidiary of Lloyds
Bank Corporate Markets plc. Lloyds Bank Corporate Markets Wertpapierhandelsbank GmbH has its
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