Hello, I'm reading the documents on Multinomial Logistic Regression ( https://apache.github.io/incubator-systemml/algorithms-classification.html#usage) with Scala API. It says
val model = lr.fit(X_train_df) 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 API. 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 = logistic.fit(X_train, 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, Ethan
