DMC Machine Learning & Algorithms 2

Question 1

A decision tree grows until every leaf is pure and achieves 100% training accuracy but performs poorly on new data. What is the most likely problem?

Accepted Answer

Overfitting due to high variance

Answer

A fully grown tree memorizes training noise, which gives low bias but high variance and poor generalization.

Question 2

Which splitting criterion does the CART algorithm use for classification trees?

Accepted Answer

Gini impurity

Answer

CART uses Gini impurity for classification and variance reduction (MSE) for regression.

Question 3

In k-means clustering, what happens to the within-cluster sum of squares (WCSS) as k increases?

Accepted Answer

It never increases and decreases toward zero when k equals the number of points

Answer

More centroids can only reduce or maintain WCSS, reaching zero when each point is its own cluster, which is why the elbow method looks for diminishing returns.

Question 4

Which statement best describes bagging as used in random forests?

Accepted Answer

Models are trained independently on bootstrap samples and their predictions are aggregated

Answer

Bagging trains models in parallel on bootstrap samples and averages or votes, mainly reducing variance.

Question 5

Which metric is most appropriate when only 1% of transactions are fraudulent and you want to evaluate the fraud class specifically?

Accepted Answer

Precision and recall (or the PR curve)

Answer

With heavy imbalance, accuracy is misleading, so precision, recall and PR-AUC reflect minority-class performance.

DMC - Data Mining Certification Practice Test

DMC - Data Mining Certification Practice Test

DMC Machine Learning & Algorithms 2