AML Supervised & Unsupervised Learning Algorithms 4

Question 1

Which regularization technique used in neural networks randomly sets a fraction of neuron activations to zero during training?

Accepted Answer

Dropout

Answer

Dropout randomly zeroes a proportion of neuron outputs each forward pass, preventing co-adaptation of neurons and acting as an ensemble of many subnetworks.

Question 2

In Principal Component Analysis (PCA), what do the eigenvalues of the covariance matrix represent?

Accepted Answer

The proportion of variance explained by each principal component

Answer

Each eigenvalue quantifies the amount of variance in the data that is captured by its corresponding eigenvector (principal component direction).

Question 3

What distinguishes hard margin SVM from soft margin SVM?

Accepted Answer

Hard margin SVM allows no misclassifications; soft margin SVM allows controlled misclassifications via slack variables

Answer

Hard margin SVM requires all training points to be correctly classified and outside the margin, while soft margin SVM introduces slack variables to permit some violations for non-separable data.

Question 4

Which statement about t-SNE (t-Distributed Stochastic Neighbor Embedding) is TRUE?

Accepted Answer

t-SNE uses Student's t-distribution in low-dimensional space to alleviate the crowding problem

Answer

t-SNE uses a heavy-tailed t-distribution in the low-dimensional map, which allows moderately dissimilar points to be placed further apart and alleviates the crowding problem.

Question 5

When would you prefer Naive Bayes over Logistic Regression for text classification?

Accepted Answer

When training data is scarce and the conditional independence assumption is approximately met

Answer

Naive Bayes performs well with small datasets because it estimates feature probabilities independently, requiring far fewer parameters than logistic regression to learn.

(AML) Advanced Machine Learning Certification Practice Test

(AML) Advanced Machine Learning Certification Practice Test

AML Supervised & Unsupervised Learning Algorithms 4