MLS-C01 Modeling Practice Question
A data scientist needs to choose an algorithm for a regression problem with 50 features and 1 million training examples. The model must be interpretable and the training data fits in memory. Which algorithm is most appropriate?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Linear regression
Linear regression is the most appropriate choice because it is interpretable, handles high-dimensional data with 50 features efficiently, scales well to 1 million training examples that fit in memory, and directly addresses regression. Option A (PCA) is a dimensionality reduction technique, not a regression algorithm. Option C (XGBoost) is powerful for regression but is less interpretable due to its ensemble nature. Option D (k-NN) is non-parametric and computationally expensive at inference time, lacking interpretability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is a dimensionality reduction technique, not a regression algorithm.
- ✓
Linear regression
Why this is correct
Linear regression is interpretable and efficient for large datasets.
- ✗
XGBoost
Why it's wrong here
XGBoost is a tree ensemble, less interpretable and may require more tuning.
- ✗
k-Nearest Neighbors
Why it's wrong here
k-NN is computationally expensive for large datasets and not interpretable.
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