The correct answer is A.
Feature extraction is the process of identifying and selecting the most relevant features from a dataset for a particular task. In the case of email classification, the features might be the words or phrases that appear in the email. The bag-of-words model is a simple way to represent text as a set of features. Each feature is a word, and the value of the feature is the number of times the word appears in the text.
PCA is a dimensionality reduction technique that can be used to project high-dimensional data into a lower-dimensional space. This can be useful for tasks such as classification, where the number of features is large. However, PCA does not involve feature extraction.
Removing stop words is a preprocessing step that is often used in natural language processing. Stop words are common words that do not add much meaning to a text, such as “the”, “of”, and “and”. Removing stop words can help to improve the performance of many NLP tasks. However, it is not an example of feature extraction.
Forward selection is a stepwise regression method that is used to select the best subset of features for a model. It is not an example of feature extraction.
In conclusion, the correct answer is A.