Practice In-processing Strategies (Algorithm-Level Interventions) - 1.3.2 | Module 7: Advanced ML Topics & Ethical Considerations (Weeks 14) | Machine Learning
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1.3.2 - In-processing Strategies (Algorithm-Level Interventions)

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the primary goal of in-processing strategies?

πŸ’‘ Hint: Think about when these adjustments take place.

Question 2

Easy

Name one benefit of regularization.

πŸ’‘ Hint: Consider how it helps with the growth of a model.

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 main focus of in-processing strategies?

  • Data collection
  • Model evaluation
  • Mitigating bias during training

πŸ’‘ Hint: Think about the timing of these strategies.

Question 2

True or False: Regularization can help ensure fairness in machine learning models.

  • True
  • False

πŸ’‘ Hint: Consider how regularization functions.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a machine learning model that prioritizes both predictive accuracy and fairness for a hiring algorithm. Identify how you would implement in-processing strategies to achieve this.

πŸ’‘ Hint: Think about multiple methods of fairness integration.

Question 2

Evaluate the effectiveness of adversarial debiasing in a real-world application, taking into account ethical concerns regarding bias and privacy issues.

πŸ’‘ Hint: Consider ethical implications and actual user experiences.

Challenge and get performance evaluation