Binary Classification Models with pySpark in Apache Spark
Experiment with various binary classification models below and select the most appropriate based on Area Under the ROC Curve together with Principal Component Analysis (PCA) in Apache Spark.
The following package to be installed:
pyspark 2.4.5 py_0 
Ideally One-Vs-Rest classifier to be added to an arsenal of binary classifications.
Using the already used base logistic Regression model as input as below,
OVR = OneVsRest(classifier=LR)
OVR_paramGrid = (ParamGridBuilder().build())
classification_model_and_paramgrid_list.append([OVR, OVR_paramGrid])
the line cvModel = cv.fit(df_train) throws the following exception.
IllegalArgumentException: 'Field "rawPrediction" does not exist.\nAvailable fields: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 1_INDEX, 1_VEC, 3_INDEX, 3_VEC, 4_INDEX, 4_VEC, 6_INDEX, 6_VEC, 7_INDEX, 7_VEC, 9_INDEX, 9_VEC, 10_INDEX, 10_VEC, 12_INDEX, 12_VEC, 14_INDEX, 14_VEC, 15_INDEX, 15_VEC, 17_INDEX, 17_VEC, 19_INDEX, 19_VEC, 20_INDEX, 20_VEC, label, features, std_features, pca_features, CrossValidator_01aa8f348031_rand, prediction'
I have tried three separate methods to achieve SVD, yet still in vain.
RowMatrix's computeSVD.
MLUtils.convertVectorColumnsToML after TypeError: Cannot convert type <class 'pyspark.ml.linalg.DenseVector'> into Vector
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