brian-methodical

sklearn handles text labels differently than ml_dask on OneHotEncoding

1

**Describe the issue**: [OneHotEncoding](https://github.com/dask/dask-ml/blob/ba2024d68557fade43f4dc2fa369445d01629923/dask_ml/preprocessing/_encoders.py#L16) defaults to sets `catagories_` to a numpy dtype='

HashingVectorizer behaves differently from FeatureHasher

1

**Describe the issue**: [HashingVectorizer](https://ml.dask.org/modules/generated/dask_ml.feature_extraction.text.HashingVectorizer.html#dask-ml-feature-extraction-text-hashingvectorizer) behaves differently from FeatureHasher, HashingVectorizer can work off a Sting like ``` JUNK_FOOD_DOCS = ( "the pizza pizza beer copyright", "the pizza burger beer copyright", "the...