AML Supervised & Unsupervised Learning Algorithms 3

Question 1

Which loss function is minimized by logistic regression during training?

Accepted Answer

Binary Cross-Entropy (Log Loss)

Answer

Logistic regression minimizes binary cross-entropy (negative log-likelihood), which penalizes confident wrong predictions more heavily than uncertain ones.

Question 2

What is the key difference between agglomerative and divisive hierarchical clustering?

Accepted Answer

Agglomerative starts with each point as its own cluster and merges; divisive starts with one cluster and splits

Answer

Agglomerative clustering is bottom-up: each point starts as its own cluster, and pairs are merged iteratively; divisive is top-down, splitting clusters recursively.

Question 3

In a decision tree, information gain is computed as the reduction in which quantity after a split?

Accepted Answer

Entropy

Answer

Information gain measures the reduction in entropy (Shannon entropy) of the target variable achieved by splitting on a particular feature.

Question 4

What is the primary advantage of using Isolation Forest over LOF for anomaly detection?

Accepted Answer

Isolation Forest scales efficiently to high-dimensional, large datasets

Answer

Isolation Forest has linear time complexity and scales well to high dimensions and large datasets by isolating anomalies with random partitioning, unlike the quadratic-complexity LOF.

Question 5

Which scenario best illustrates the concept of 'label leakage' in a supervised classification task?

Accepted Answer

Including a feature derived from the target variable in training features

Answer

Label leakage occurs when a feature directly or indirectly encodes information about the target variable, causing artificially high training performance that won't generalize.

(AML) Advanced Machine Learning Certification Practice Test

(AML) Advanced Machine Learning Certification Practice Test

AML Supervised & Unsupervised Learning Algorithms 3