Practice Preprocessing Pipeline - 14.3.2 | 14. Machine Learning Pipelines and Automation | Data Science Advance
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14.3.2 - Preprocessing Pipeline

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the purpose of handling missing values in a dataset?

💡 Hint: Think about the impact of missing data on model predictions.

Question 2

Easy

What does One-Hot Encoding do?

💡 Hint: Consider how categorical features could be represented numerically.

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 purpose of a preprocessing pipeline?

  • To automate model training
  • To prepare data for machine learning
  • To visualize data

💡 Hint: Consider what your data goes through before reaching the model.

Question 2

True or False: Label Encoding and One-Hot Encoding are interchangeable and can be used in the same situations flawlessly.

  • True
  • False

💡 Hint: Think about when each encoding method is appropriate.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

Question 2

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.

Challenge and get performance evaluation