Data Science MCQ Flashcards
7 cards from real Data Science practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Data Science MCQ flashcards as text
Which technique is most appropriate for reducing the dimensionality of a dataset while preserving as much variance as possible?
Answer: Principal Component Analysis
PCA projects data onto orthogonal components ordered by the variance they capture.
In a confusion matrix, what does precision measure?
Answer: True positives divided by all predicted positives
Precision is TP / (TP + FP), the fraction of positive predictions that are correct.
Which method helps prevent overfitting by penalizing large model coefficients?
Answer: L2 (Ridge) regularization
Ridge regularization adds a penalty proportional to the squared magnitude of coefficients.
What is the primary purpose of cross-validation?
Answer: To estimate model performance on unseen data more reliably
Cross-validation rotates train/test splits to give a more robust performance estimate.
Which distance metric is most commonly used by default in K-means clustering?
Answer: Euclidean distance
K-means minimizes within-cluster sum of squared Euclidean distances.
A model performs well on training data but poorly on test data. This is a sign of what?
Answer: Overfitting
Overfitting occurs when a model memorizes training noise and fails to generalize.
Which of the following is a supervised learning task?
Answer: Predicting house prices from labeled sales data
Supervised learning uses labeled outputs, such as known house prices, to train a model.