Virtual Labs

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Aim

Theory

Pretest

Procedure

Simulation

Posttest

References

Feedback

Evaluation Metrics, Overfitting/Underfitting

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What is the primary cause of overfitting in machine learning models?

a: Insufficient training data Explanation

Explanation

b: Using too few parameters in the model Explanation

Explanation

c: Learning noise instead of general patterns Explanation

Explanation

d: Using a very simple model Explanation

Explanation

Which of the following is a common symptom of underfitting?

a: High training accuracy but low test accuracy Explanation

Explanation

b: Low accuracy on both training and test data Explanation

Explanation

c: Model performing exceptionally well on unseen data Explanation

Explanation

d: A high number of parameters leading to complex decision boundaries Explanation

Explanation

Why is a train/test split necessary in machine learning?

a: To make the model memorize the test data Explanation

Explanation

b: To evaluate the model's performance on unseen data Explanation

Explanation

c: To prevent the model from learning any patterns Explanation

Explanation

d: To increase the training accuracy Explanation

Explanation

What is the primary difference between supervised and unsupervised learning?

a: Supervised learning requires labeled data, while unsupervised learning does not Explanation

Explanation

b: Unsupervised learning uses neural networks, while supervised learning does not Explanation

Explanation

c: Supervised learning is only used for regression tasks Explanation

Explanation

d: Unsupervised learning always provides better accuracy Explanation

Explanation

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