CAIC Machine Learning & Data Science 3

Question 1

Which ensemble method trains multiple models in parallel on random subsets of the training data and aggregates their predictions?

Accepted Answer

Bagging

Answer

Bagging (Bootstrap Aggregating) trains independent learners on bootstrapped samples and combines them via voting or averaging to reduce variance.

Question 2

What does the ROC curve plot?

Accepted Answer

True Positive Rate vs. False Positive Rate at different thresholds

Answer

The ROC curve shows the tradeoff between sensitivity (TPR) and specificity (1-FPR) across all classification thresholds.

Question 3

Which regularization technique randomly sets a fraction of neuron activations to zero during each training step?

Accepted Answer

Dropout

Answer

Dropout randomly deactivates neurons during training, forcing the network to learn redundant representations and reducing co-adaptation.

Question 4

In a confusion matrix for binary classification, which formula correctly computes Precision?

Accepted Answer

TP / (TP + FP)

Answer

Precision measures what fraction of predicted positives are actually positive: TP divided by all predicted positives (TP + FP).

Question 5

What is the key difference between supervised and unsupervised learning?

Accepted Answer

Supervised learning requires labeled training data; unsupervised discovers patterns without labels

Answer

Supervised learning learns a mapping from inputs to known labels, while unsupervised learning finds hidden structure in unlabeled data.

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