Machine Learning Machine Learning 5

Question 1

What is the difference between bagging and boosting in ensemble learning?

Accepted Answer

Bagging trains models in parallel on random subsets; boosting trains sequentially, correcting prior errors

Answer

Bagging builds independent models in parallel on bootstrapped subsets to reduce variance, while boosting trains models sequentially to reduce bias by correcting previous mistakes.

Question 2

What is the purpose of a validation set as distinct from a test set?

Accepted Answer

To tune hyperparameters and select the best model without using the final held-out test set

Answer

The validation set is used during development to compare models and tune hyperparameters, while the test set is held out and used only once for final unbiased evaluation.

Question 3

What does 'precision' measure in a classification model?

Accepted Answer

Of all predicted positives, what fraction were actually positive

Answer

Precision is the ratio of true positives to all predicted positives (TP / (TP + FP)), measuring how trustworthy positive predictions are.

Question 4

Which algorithm is used in the Expectation-Maximization (EM) framework for soft clustering?

Accepted Answer

Gaussian Mixture Models (GMM)

Answer

Gaussian Mixture Models use the EM algorithm to assign soft probabilities of cluster membership based on fitting multiple Gaussian distributions to the data.

Question 5

What is the main advantage of using ROC-AUC over accuracy as an evaluation metric?

Accepted Answer

ROC-AUC accounts for class imbalance by evaluating performance across all classification thresholds

Answer

ROC-AUC measures the area under the ROC curve across all decision thresholds, making it robust to class imbalance and providing a holistic view of classifier performance.

Machine Learning Practice Test

Machine Learning Practice Test

Machine Learning Machine Learning 5