Practice Diagnosing Model Behavior with Learning and Validation Curves - 4.5.2.4 | Module 4: Advanced Supervised Learning & Evaluation (Weeks 8) | Machine Learning
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4.5.2.4 - Diagnosing Model Behavior with Learning and Validation Curves

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define a Learning Curve and its purpose in model evaluation.

πŸ’‘ Hint: Think about how performance relates to data size.

Question 2

Easy

What is underfitting?

πŸ’‘ Hint: What happens when a model cannot learn enough from the data?

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 Learning Curves indicate in a machine learning context?

  • Model accuracy for a specific hyperparameter
  • The relationship between training data size and model performance
  • The effectiveness of a chosen algorithm

πŸ’‘ Hint: Focus on the effect of data size on learning.

Question 2

True or False: A high training score and low validation score suggests the model is underfitting.

  • True
  • False

πŸ’‘ Hint: Refer to the definitions of underfitting and overfitting.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You notice that as you increase the data size, both training and validation scores of your model plateau at low values. What should you assess about this model, and what corrective measures could you take?

πŸ’‘ Hint: Reflect on the reasons why models may not fit data well.

Question 2

While examining Validation Curves for your Random Forest model, you see that validation accuracy peaks at max_depth=10 and then falls sharply. How would you interpret this, and what recommendations would you make?

πŸ’‘ Hint: Consider what happens when you add complexity beyond optimal limits.

Challenge and get performance evaluation