The correct answer is: B. FeatureHasher
A FeatureHasher is a transformer that converts a collection of features into a set of feature hashes. It does this by hashing each feature value and then converting the hash into a vector of binary values. This can be done very efficiently, and it can be used to create sparse matrices that can be fed into any machine learning model.
A DictVectorizer is a transformer that converts a collection of features into a sparse matrix. It does this by first converting each feature value into a vector of binary values. It then converts these vectors into a sparse matrix by only storing the non-zero values. This can be done very efficiently, and it can be used to create sparse matrices that can be fed into any machine learning model.
However, a DictVectorizer is not as efficient as a FeatureHasher, and it does not produce as sparse matrices. This is because a DictVectorizer stores the
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