- In the one-hot encoding solution, can you use different classifiers supported in PySpark instead of logistic regression, such as decision tree, random forest, and linear SVM?
- In the feature hashing solution, can you try other hash sizes, such as 5,000, and 20,000? What do you observe?
- In the feature interaction solution, can you try other interactions, such as C1 and C20?
- Can you first use feature interaction and then feature hashing in order to lower the expanded dimension? Are you able to obtain higher AUC?
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