Simply because the data set is so small. Anything that's operating entirely
in memory is faster than something splitting the same data across multiple
machines, running multiple processes, and incurring all the overhead of
sending the data and results, combining them, etc.

That said, I suspect that you are not using any parallelism in Spark
either. You probably have 1 partition, which means at most 1 core is used
no matter how many are there. Repartition the data set.

On Fri, Mar 26, 2021 at 8:15 AM Williams, David (Risk Value Stream)
<david.willi...@lloydsbanking.com.invalid> wrote:

> Classification: Limited
>
>
>
> Many thanks for your response Sean.
>
>
>
> Question - why spark is overkill for this and why is sklearn is faster
> please?  It’s the same algorithm right?
>
>
>
> Thanks again,
>
> Dave Williams
>
>
>
> *From:* Sean Owen <sro...@gmail.com>
> *Sent:* 25 March 2021 16:40
> *To:* Williams, David (Risk Value Stream) <
> david.willi...@lloydsbanking.com>
> *Cc:* user@spark.apache.org
> *Subject:* Re: FW: Email to Spark Org please
>
>
>
>
> *-- This email has reached the Bank via an external source -- *
>
> Spark is overkill for this problem; use sklearn.
>
> But I'd suspect that you are using just 1 partition for such a small data
> set, and get no parallelism from Spark.
>
> repartition your input to many more partitions, but, it's unlikely to get
> much faster than in-core sklearn for this task.
>
>
>
> On Thu, Mar 25, 2021 at 11:39 AM Williams, David (Risk Value Stream) <
> david.willi...@lloydsbanking.com.invalid> wrote:
>
> Classification: Public
>
>
>
> Hi Team,
>
>
>
> We are trying to utilize ML Gradient Boosting Tree Classification
> algorithm and found the performance of the algorithm is very poor during
> training.
>
>
>
> We would like to see we can improve the performance timings since, it is
> taking 2 days for training for a smaller dataset.
>
>
>
> Our dataset size is 40000. Number of features used for training is 564.
>
>
>
> The same dataset when we use in Sklearn python training is completed in 3
> hours but when used ML Gradient Boosting it is taking 2 days.
>
>
>
> We tried increasing number of executors, executor cores, driver memory etc
> but couldn’t see any improvements.
>
>
>
> The following are the parameters used for training.
>
>
>
> gbt = GBTClassifier(featuresCol='features', labelCol='bad_flag',
> predictionCol='prediction', maxDepth=11,  maxIter=10000, stepSize=0.01,
> subsamplingRate=0.5, minInstancesPerNode=110)
>
>
>
> If you could help us with any suggestions to tune this,  that will be
> really helpful
>
>
>
> Many thanks,
>
> Dave Williams
>
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