14.3.2 - Preprocessing Pipeline
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Practice Questions
Test your understanding with targeted questions
What is the purpose of handling missing values in a dataset?
💡 Hint: Think about the impact of missing data on model predictions.
What does One-Hot Encoding do?
💡 Hint: Consider how categorical features could be represented numerically.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the purpose of a preprocessing pipeline?
💡 Hint: Consider what your data goes through before reaching the model.
True or False: Label Encoding and One-Hot Encoding are interchangeable and can be used in the same situations flawlessly.
💡 Hint: Think about when each encoding method is appropriate.
2 more questions available
Challenge Problems
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Given a dataset with missing values in both categorical and numerical columns, design a preprocessing pipeline to handle this before model training.
💡 Hint: Consider how you would apply each imputer to different columns within the dataset.
Critique a preprocessing workflow that ignores scaling and encoding in a model that heavily relies on feature interaction. What are potential outcomes?
💡 Hint: Think about the effects of unprocessed data on a model's ability to learn from features.
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