CAIC Machine Learning & Data Science 2

Question 1

Which technique is used to reduce the number of input features by combining them into fewer dimensions while preserving variance?

Accepted Answer

Principal Component Analysis

Answer

PCA projects data onto orthogonal axes (principal components) that capture the most variance, effectively reducing dimensionality.

Question 2

A dataset has 95% negative samples and 5% positive samples. Which metric is LEAST informative for evaluating a classifier on this dataset?

Accepted Answer

Accuracy

Answer

Accuracy is misleading on imbalanced datasets because a model predicting all negatives achieves 95% accuracy without learning anything meaningful.

Question 3

What does the term 'bias-variance tradeoff' describe in machine learning?

Accepted Answer

The tension between underfitting (high bias) and overfitting (high variance)

Answer

High bias models underfit by making oversimplified assumptions, while high variance models overfit by being too sensitive to training data noise.

Question 4

In gradient boosting, each successive tree is trained to predict what?

Accepted Answer

The residual errors of the previous ensemble

Answer

Gradient boosting fits each new tree to the residuals (errors) left by the current ensemble, iteratively reducing prediction error.

Question 5

Which cross-validation strategy is most appropriate when the dataset has a temporal ordering and future data must not leak into training?

Accepted Answer

Time series split (walk-forward validation)

Answer

Time series split ensures training always uses only past data and validation uses future data, preventing temporal leakage.

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