Github user facaiy commented on the issue:
https://github.com/apache/spark/pull/18288
In my opinion, `numFeatures` is vital for sparse data.
Say our feature is 100-dim indeed, while in a small train data their
maximum size is 990. It is dangerous (or wrong) to train a 990-dim model as it
might fail in the coming test data.
Hence, in most cases, `numFeatures` should be given by user.
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