Spark's implementation does perform PAVA on each partition only to then
collect each result to the driver and to perform PAVA again on the
collected results. The hope of that is, that enough data is pooled, so
that the the last step does not exceed the drivers memory limits. This
assumption doe
IsotonicRegression can handle feature column of vector type. It will
extract the a certain index (controlled by param "featureIndex") of this
feature vector and feed it into model training. It will perform Pool
adjacent violators algorithms on each partition, so it's distributed and
the data is not
Hi Swaroop,
from my understanding, Isotonic Regression is currently limited to data
with 1 feature plus weight and label. Also the entire data is required
to fit into memory of a single machine.
I did some work on the latter issue but discontinued the project,
because I felt no one really need
Hi Swaroop,
Would you mind to share your code that others can help you to figure out
what caused this error?
I can run the isotonic regression examples well.
Thanks
Yanbo
2016-07-08 13:38 GMT-07:00 dsp :
> Hi I am trying to perform Isotonic Regression on a data set with 9 features
> and a label
