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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
Define feature engineering in your own words.
💡 Hint: Think about why it's necessary in machine learning.
Question 2
Easy
What does 'overfitting' mean?
💡 Hint: Consider how a model may not perform well on new data.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the primary purpose of feature engineering?
- To create visuals for analysis
- To improve model performance
- To eliminate data noise
💡 Hint: Consider why processing data for models is essential.
Question 2
True or False: Overfitting occurs when a model generalizes well to new data.
- True
- False
💡 Hint: Reflect on overfitting's effects on model predictions.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
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.
💡 Hint: Think about the steps in data wrangling that can be integrated with feature engineering.
Question 2
Design a machine learning model predicting sales based on your chosen features. How would you ensure that your features help avoid overfitting?
💡 Hint: Consider ways to validate and improve model performance iteratively.
Challenge and get performance evaluation