Question
A Spark ML workflow includes Tokenizer, HashingTF, and LogisticRegression. Why is it often better to pass the full Pipeline as the CrossValidator estimator instead of only LogisticRegression?
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A Spark ML workflow includes Tokenizer, HashingTF, and LogisticRegression. Why is it often better to pass the full Pipeline as the CrossValidator estimator instead of only LogisticRegression?
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