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From Ethan Xu <>
Subject Documents of SystemML Algorithms Reference
Date Wed, 19 Apr 2017 21:07:15 GMT

I'm reading the documents on Multinomial Logistic Regression (
with Scala API. It says

val model =
val prediction = model.transform(X_test_df)

The "Arguments" section below it says:

X: Location (on HDFS) to read the input matrix of feature vectors; each row
constitutes one feature vector.

Y: Location to read the input one-column matrix of category labels that
correspond to feature vectors in X. Note the following:...
The explanation of the arguments seem to correspond to the Hadoop and Spark

Could someone please advise what are the specifications of `X_train_df` and
`X_test_df`? Are they the same as specified in the Python API? i.e.:

# X_train, y_train and X_test can be NumPy matrices or Pandas
DataFrame or SciPy Sparse Matrixy_test =,
y_train).predict(X_test)# df_train is DataFrame that contains two
columns: "features" (of type Vector) and "label". df_test is a
DataFrame that contains the column "features"

The explanation of arguments for Python/Scala seem to be missing for other
algorithms, too.

Thanks a lot,


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