Feature Extraction in Machine Learning

What is the main purpose of feature extraction in machine learning?

• To collect more data

• To increase model complexity

• To reduce dimensionality and improve performance

• To store text data

Which of the following is NOT an application mentioned for feature extraction?

• Speech recognition

• Weather forecasting

• Text processing

• Image classification

In the Bag-of-Words model, what does each vector dimension represent?

• A sentence

• A paragraph

• A word in the dictionary

• A document

What value does a word receive in the BoW vector if it appears twice in a sentence?

• 1

• 2

• 0

• TF-IDF score

Which method takes into account how frequently a word appears in the entire corpus?

• BoW

• POS tagging

• TF-IDF

• Lemmatization

What is the formula for calculating TF (Term Frequency)?

• log(Number of docs / Doc freq)

• Word count / Max word count in doc

• 1 / Document frequency

• Word frequency squared

What does IDF stand for?

• Information Document Frequency

• Inverse Document Frequency

• Internal Document Function

• Indexed Data Format

What happens to rare words in the TF-IDF model?

• They get lower weight

• They get higher weight

• They are ignored

• They are repeated

Which method preserves word order and short context?

• Bag-of-Words

• TF-IDF

• N-gram

• Random Forest

What is the main disadvantage of large n in N-grams?

• Too few features

• Data explosion

• Wrong context

• Lower accuracy