Thanks a lot! You are right!
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Hello,
I have a LogisticRegression model for predicting a binary label. Once I
train the model, I run it to get some predictions. I get the following
values for RawPrediction. How should I interpret these? Whdo they mean?
++|rawPrediction
I am using Spark ML's pipeline to classify text documents with the following
steps:
Tokenizer -> CountVectorizer -> LogisticRegression
I want to be able to print the words with the highest weights. Can this be
done?
So far I have been able to extract the LR coefficients, but can those be
tied up t
Nm. Rookie error. I wasn't caching the DF.
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Hello,I have a model, which uses CountVectorizer and LogisticRegression.
*Everything seems to work fine, except that when I am running the last step
to get results and predictions, the document ids (doc_id) are being changed
completely. Do you know why that is? Am I doing something wrong?*
import
Thank you so much!Any sense to how long this may take to get released? TIA
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Hello,
My understanding is that word2vec can be ran in two modes:
continuous bag-of-words (CBOW) (order of words does not matter)
continuous skip-gram (order of words matters)
I would like to run the *CBOW* implementation from Spark's MLlib, but it is
not clear to me from the documentation and th
