Practice Preprocessing Pipeline (14.3.2) - Machine Learning Pipelines and Automation

Practice: Preprocessing Pipeline

Lesson 14.3.2Machine Learning Pipelines and Automation

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10 questions

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Quiz
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6 sample questions

Question 1.
What is the purpose of handling missing values in a dataset?
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Think about the impact of missing data on model predictions.
Question 2.
What does One-Hot Encoding do?
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Consider how categorical features could be represented numerically.
3Question 3.
What is the purpose of a preprocessing pipeline?
- To automate model training
- To prepare data for machine learning
- To visualize data
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Consider what your data goes through before reaching the model.
4Question 4.
True or False: Label Encoding and One-Hot Encoding are interchangeable and can be used in the same situations flawlessly.
- True
- False
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Think about when each encoding method is appropriate.
Question 5.
Given a dataset with missing values in both categorical and numerical columns, design a preprocessing pipeline to handle this before model training.
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Consider how you would apply each imputer to different columns within the dataset.
Question 6.
Critique a preprocessing workflow that ignores scaling and encoding in a model that heavily relies on feature interaction. What are potential outcomes?
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Think about the effects of unprocessed data on a model's ability to learn from features.

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