AML Supervised & Unsupervised Learning Algorithms 2

Question 1

In a gradient boosting model, what does the 'learning rate' (shrinkage) parameter control?

Accepted Answer

The fraction by which each tree's contribution is scaled down

Answer

The learning rate (shrinkage) multiplies each new tree's contribution, preventing overfitting by making the ensemble learn more slowly and conservatively.

Question 2

Which unsupervised algorithm is best suited for detecting arbitrarily shaped clusters in noisy data?

Accepted Answer

DBSCAN

Answer

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) discovers clusters of arbitrary shape and explicitly labels outliers as noise points.

Question 3

What is the primary purpose of using kernel functions in Support Vector Machines?

Accepted Answer

To implicitly map data into a higher-dimensional feature space where it becomes linearly separable

Answer

Kernel functions compute dot products in a higher-dimensional space without explicitly transforming the data, enabling SVMs to find non-linear decision boundaries.

Question 4

In Expectation-Maximization (EM) for Gaussian Mixture Models, what happens during the E-step?

Accepted Answer

Soft cluster membership probabilities are computed for each data point

Answer

The E-step computes the posterior probability (responsibility) that each Gaussian component generated each data point, given the current parameter estimates.

Question 5

Which statement correctly describes the bias-variance tradeoff in the context of k-Nearest Neighbors (kNN)?

Accepted Answer

Larger k increases bias and decreases variance

Answer

A larger k smooths the decision boundary by averaging more neighbors, increasing bias (underfitting) but reducing variance (sensitivity to noise).

(AML) Advanced Machine Learning Certification Practice Test

(AML) Advanced Machine Learning Certification Practice Test

AML Supervised & Unsupervised Learning Algorithms 2