Practice Advanced Model Evaluation (on A Preliminary Model To Understand Metrics) (4.5.2.2)
Students

Academic Programs

AI-powered learning for grades 8-12, aligned with major curricula

Engineering Courses
Professional

Professional Courses

Industry-relevant training in Business, Technology, and Design

Certifications
Games

Interactive Games

Fun games to boost memory, math, typing, and English skills

Advanced Model Evaluation (on a Preliminary Model to understand metrics)

Practice - Advanced Model Evaluation (on a Preliminary Model to understand metrics)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does ROC stand for?

💡 Hint: Think of the type of curve that evaluates classification decisions.

Question 2 Easy

What does a high AUC value indicate?

💡 Hint: Consider what the value represents on a scale.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main purpose of the ROC curve?

To compare multiple models
To visualize classifier performance
To calculate accuracy

💡 Hint: Think about the information that the ROC curve represents.

Question 2

True or False: AUC values can only range from 0 to 1.

True
False

💡 Hint: Think of AUC as a measure of model reliability.

Get performance evaluation

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset with a high imbalance (e.g., 90% negative and 10% positive), would you prioritize recall or precision when tuning your model? Justify your reasoning.

💡 Hint: Consider the consequences of false negatives in your context.

Challenge 2 Hard

How would you approach modifying a model that shows low precision but very high recall in an imbalanced dataset? What steps would you take?

💡 Hint: Think about how modifying thresholds and model parameters can enhance results.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.