Practice Feature Extraction (2.5.1) - Data Wrangling and Feature Engineering

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9 questions

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6 sample questions

Question 1.
What does feature extraction do?
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Think about transforming raw data.
Question 2.
Name a technique used in feature extraction for text data.
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What method evaluates word importance?
3Question 3.
What is feature extraction?
- Deriving new features
- Removing data
- Adding noise
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Think about the purpose of transforming data in ML.
4Question 4.
TF-IDF stands for what?
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Remember it evaluates word importance.
Question 5.
Draft a complete feature extraction plan for a dataset containing user activities on a website, specifying which features to extract and the techniques to use.
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Think about what information would be most relevant for analyzing user behavior.
Question 6.
Analyze the pros and cons of using feature extraction methods like TF-IDF and Bag of Words in a machine learning pipeline.
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Consider performance and outcome versus complexity.

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