Practice Why Is It Important? (2.4.2) - Data Wrangling and Feature Engineering

Practice: Why Is It Important?

Lesson 2.4.2Data Wrangling and Feature Engineering

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10 questions

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6 sample questions

Question 1.
Define feature engineering in your own words.
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Think about why it's necessary in machine learning.
Question 2.
What does 'overfitting' mean?
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Consider how a model may not perform well on new data.
3Question 3.
What is the primary purpose of feature engineering?
- To create visuals for analysis
- To improve model performance
- To eliminate data noise
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Consider why processing data for models is essential.
4Question 4.
True or False: Overfitting occurs when a model generalizes well to new data.
- True
- False
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Reflect on overfitting's effects on model predictions.
Question 5.
Given a dataset with missing values and outliers, describe a feature engineering process that prepares the dataset for training a model aimed at predicting health outcomes.
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Think about the steps in data wrangling that can be integrated with feature engineering.
Question 6.
Design a machine learning model predicting sales based on your chosen features. How would you ensure that your features help avoid overfitting?
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Consider ways to validate and improve model performance iteratively.

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