MS-DS Master of Data science Supervised Learning Algorithms 5

Question 1

Which of the following is a key difference between bagging and boosting ensemble methods?

Accepted Answer

Bagging trains models in parallel on bootstrapped samples; boosting trains sequentially correcting prior errors

Answer

Bagging builds independent models in parallel on bootstrap samples and averages results, while boosting builds models sequentially where each corrects the errors of its predecessor.

Question 2

In a neural network, what does batch normalization do during training?

Accepted Answer

It normalizes activations of each layer across the mini-batch, stabilizing training

Answer

Batch normalization standardizes activations within each mini-batch, reducing internal covariate shift and allowing higher learning rates and faster convergence.

Question 3

What is the 'curse of dimensionality' and how does it affect k-NN classifiers?

Accepted Answer

In high dimensions, data becomes sparse and distance metrics lose discriminative power

Answer

As dimensionality grows, data points become increasingly equidistant, making nearest-neighbor distances meaningless and degrading k-NN performance significantly.

Question 4

Which of the following describes the purpose of cross-validation in supervised learning?

Accepted Answer

To estimate a model's generalization performance and tune hyperparameters on limited data

Answer

Cross-validation partitions data into multiple folds, training and evaluating the model on different subsets to produce a reliable estimate of generalization error.

Question 5

A decision tree trained to zero training error likely suffers from which problem?

Accepted Answer

Overfitting due to memorizing training noise

Answer

A tree that perfectly fits training data has memorized noise and specific examples, resulting in high variance and poor generalization to unseen data.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Supervised Learning Algorithms 5