Practice Initial Data Split for Final, Unbiased Evaluation (Crucial Step) - 4.2.2 | Module 2: Supervised Learning - Regression & Regularization (Weeks 4) | Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the purpose of performing a train-test split in machine learning?

πŸ’‘ Hint: Think about the differences between seen and unseen data.

Question 2

Easy

What is the typical ratio of training to testing data?

πŸ’‘ Hint: This is a common practice in many machine learning tasks.

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 do we aim to achieve by performing an initial data split?

  • Verify model training accuracy
  • Ensure unbiased evaluation of predictions
  • Maximize training data utilization

πŸ’‘ Hint: Consider the role of unseen data in validating your model.

Question 2

True or False: A model can generalize well if it is only trained on the entire dataset without a test set.

  • True
  • False

πŸ’‘ Hint: Think about the generalization of the trained model.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Create a report explaining how you would ensure the reliability of your model’s performance using a test set, including specific metrics you would collect.

πŸ’‘ Hint: Think about the metrics that best represent the model’s performance.

Question 2

Describe a modification you would propose for the conventional train-test split method to improve model evaluation reliability.

πŸ’‘ Hint: Consider how to make sure no group is left out in evaluations.

Challenge and get performance evaluation