The correct answer is: A. LabelEncoder class
The LabelEncoder class is a class in the scikit-learn library that can be used to convert labels into sequential numbers. This can be useful for machine learning algorithms that require numeric input.
The LabelBinarizer class is a class in the scikit-learn library that can be used to convert labels into binary values. This can be useful for machine learning algorithms that require binary input.
The DictVectorizer class is a class in the scikit-learn library that can be used to convert a dictionary of features into a vector of features. This can be useful for machine learning algorithms that require vector input.
The FeatureHasher class is a class in the scikit-learn library that can be used to convert features into a hash vector. This can be useful for machine learning algorithms that require hash input.
Here is an example of how to use the LabelEncoder class:
“`from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
labels = [‘red’, ‘green’, ‘blue’]
encoded_labels = encoder.fit_transform(labels)
print(encoded_labels)
“`
This will print the following output:
[0 1 2]
As you can see, the labels have been converted into sequential numbers.