Machine Learning & Data Science Flashcards
7 cards from real CAIC practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Machine Learning & Data Science flashcards as text
Which technique is used to reduce the number of input features by combining them into fewer dimensions while preserving variance?
Answer: Principal Component Analysis
PCA projects data onto orthogonal axes (principal components) that capture the most variance, effectively reducing dimensionality.
A dataset has 95% negative samples and 5% positive samples. Which metric is LEAST informative for evaluating a classifier on this dataset?
Answer: Accuracy
Accuracy is misleading on imbalanced datasets because a model predicting all negatives achieves 95% accuracy without learning anything meaningful.
What does the term 'bias-variance tradeoff' describe in machine learning?
Answer: The tension between underfitting (high bias) and overfitting (high variance)
High bias models underfit by making oversimplified assumptions, while high variance models overfit by being too sensitive to training data noise.
In gradient boosting, each successive tree is trained to predict what?
Answer: The residual errors of the previous ensemble
Gradient boosting fits each new tree to the residuals (errors) left by the current ensemble, iteratively reducing prediction error.
Which cross-validation strategy is most appropriate when the dataset has a temporal ordering and future data must not leak into training?
Answer: Time series split (walk-forward validation)
Time series split ensures training always uses only past data and validation uses future data, preventing temporal leakage.
What is the primary purpose of the 'elbow method' in k-means clustering?
Answer: To select the optimal number of clusters k
The elbow method plots inertia versus k and looks for the point where adding more clusters yields diminishing returns.
A model trained on data from 2020–2022 is deployed in 2025 and its performance degrades. What phenomenon describes this?
Answer: Concept drift
Concept drift occurs when the statistical relationship between input features and the target variable changes over time after deployment.