The correct answer is: C. labelbinarizer class

A labelbinarizer class is a class that converts categorical labels into binary vectors. It does this by first turning each categorical label into a positive integer, and then transforming it into a vector where only one feature is 1 while all the others are 0.

A labelencoder class is a class that converts categorical labels into integers. It does this by first assigning a unique integer to each label, and then using those integers to represent the labels in the model.

A dictvectorizer class is a class that converts a dictionary of features into a vector. It does this by first converting each feature into a numeric value, and then using those values to represent the features in the model.

A featurehasher class is a class that converts features into a hash. It does this by first converting each feature into a numeric value, and then using a hash function to convert that value into a hash.

Here is an example of how a labelbinarizer class can be used:

“`
import sklearn.preprocessing as preprocessing

Create a labelbinarizer object

label_binarizer = preprocessing.LabelBinarizer()

Convert the labels to integers

labels = [“red”, “green”, “blue”]
integer_labels = label_binarizer.fit_transform(labels)

Print the integer labels

print(integer_labels)
“`

Output:

[[1 0 0]
[0 1 0]
[0 0 1]]

As you can see, the labelbinarizer class has converted the labels to integers, with each label being represented by a unique integer.