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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is bias in machine learning?
π‘ Hint: Think about how overly simplistic models perform.
Question 2
Easy
What does variance measure?
π‘ Hint: Consider what happens when you have complex models.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is high bias indicative of?
π‘ Hint: Remember the definitions of the terms.
Question 2
True or False: Irreducible error can be reduced by improving the model.
π‘ Hint: Consider the nature of this type of error.
Solve and get performance evaluation
Push your limits with challenges.
Question 1
Considering a dataset with mostly quadratic relationships and a model using linear regression, analyze the potential errors the model may incur.
π‘ Hint: Review the definitions of bias and how they relate to underfitting.
Question 2
Describe a scenario in which increasing the training set size might help mitigate overfitting in a complex model.
π‘ Hint: Think about the balance between model complexity and available data.
Challenge and get performance evaluation