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 entire vector for each feature value, even if only a few of the values are non-zero. A FeatureHasher, on the other hand, only stores the non-zero values, which makes it more efficient and produces sparser matrices.
Therefore, the correct answer is: B. FeatureHasher