The correct answer is A. RobustScaler.
RobustScaler is a class in scikit-learn that can be used to scale features in a dataset. It is designed to be more robust to outliers than other scaling methods, such as StandardScaler. RobustScaler can also be used to select a quantile range, which can be useful for reducing the impact of outliers.
DictVectorizer is a class in scikit-learn that can be used to convert a dictionary of features into a vector of features. This can be useful for features that are not easily represented as numbers, such as text features.
LabelBinarizer is a class in scikit-learn that can be used to convert labels into binary features. This can be useful for classification tasks, where the labels are typically binary (e.g., 0 for “not spam” and 1 for “spam”).
FeatureHasher is a class in scikit-learn that can be used to hash features. This can be useful for dimensionality reduction, as it can reduce the number of features without losing too much information.
In summary, RobustScaler is the correct answer because it is a more powerful scaling feature than the other options. It is designed to be more robust to outliers and the possibility to select a quantile range.