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Update Lab 2 - RDD, DataFrame, ML pipeline, and parallelization.md #11

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4 changes: 4 additions & 0 deletions Lab 2 - RDD, DataFrame, ML pipeline, and parallelization.md
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Expand Up @@ -540,6 +540,10 @@ Starting from this lab, you need to use *as many DataFrame functions as possible
### Logistic regression for document classification

5. Construct another test dataset for the [machine learning pipeline for document classification example](#example-machine-learning-pipeline-for-document-classification) with three test document samples: `"pyspark hadoop"`; `"spark a b c"`; `"mapreduce spark"` and report the prediction probabilities and the predicted labels for these three sample.
[Answer:
(4, spark hadoop) --> prob=[0.8551194341387968,0.14488056586120324], prediction=0.000000
(5, spark a b c) --> prob=[0.017552839599023443,0.9824471604009766], prediction=1.000000
(6, mapreduce spark) --> prob=[0.8551194341387968,0.14488056586120324], prediction=0.000000]

## 5. Additional ideas to explore (*optional*)

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