Practice Maximizing Log-likelihood (2.1.2.1) - Optimization Methods - Advance Machine Learning
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Maximizing log-likelihood

Practice - Maximizing log-likelihood

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is a likelihood function?

💡 Hint: Think about the relationship between probability and observed data.

Question 2 Easy

Why do we use log-likelihood instead of likelihood?

💡 Hint: Consider how multiplication can be cumbersome with multiple small probabilities.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary goal of maximizing log-likelihood?

To minimize error
To maximize data fit
To calculate likelihood ratios

💡 Hint: Remember the purpose of likelihood in data modeling.

Question 2

True or False: Log-likelihood simplifies products into sums.

True
False

💡 Hint: Think how likelihoods are calculated across multiple instances.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset and the likelihood function, explain how you would proceed to maximize log-likelihood. Discuss any potential pitfalls you might encounter.

💡 Hint: Think about calculus and optimization methods you know.

Challenge 2 Hard

Consider a case where you maximized log-likelihood, but your model is overfitting. What remedies would you suggest?

💡 Hint: Reflect on ways to prevent too much detail in your models.

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Reference links

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