Practice Model Selection: Choosing The Number Of Components (5.6) - Latent Variable & Mixture Models
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Model Selection: Choosing the Number of Components

Practice - Model Selection: Choosing the Number of Components

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does AIC stand for?

💡 Hint: What is the full name of AIC?

Question 2 Easy

True or False: Lower values of BIC indicate a better model.

💡 Hint: Remember what BIC assesses.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is one reason we select the number of components K in a mixture model?

To avoid overfitting
To increase complexity
To ensure uniform distribution

💡 Hint: Think about what happens when a model is too complex.

Question 2

Is AIC related to model complexity?

True
False

💡 Hint: Consider how AIC factors in the number of parameters.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

A researcher tests various K values from 2 to 10 for a dataset of 150 samples. After calculating AIC and BIC, they observe that both criteria favor K=5. Discuss why this validation is important.

💡 Hint: Consider what each criterion is indicating.

Challenge 2 Hard

Given two models with the following AIC values: Model 1 (K=3, AIC=300) and Model 2 (K=4, AIC=295), interpret these in terms of model selection.

💡 Hint: Think about the implications of AIC regarding trade-offs.

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Reference links

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