MLS-C01 Modeling Practice Question
Which THREE factors should be considered when choosing between a parametric and a non-parametric machine learning model?
⚠ Common exam trap
AWS often tests the misconception that non-parametric models are less prone to overfitting because they are 'simpler,' when in fact their flexibility makes them more susceptible to overfitting without careful tuning or large datasets.
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
✓
Non-parametric models are generally more flexible
Non-parametric models, such as k-nearest neighbors or decision trees, do not assume a fixed functional form for the data, allowing them to capture complex, non-linear relationships. This flexibility makes them well-suited for datasets where the underlying distribution is unknown or highly irregular, but it also increases the risk of overfitting if not properly regularized.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Non-parametric models are generally more flexible
Why this is correct
Non-parametric models can fit complex patterns.
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Parametric models have lower bias than non-parametric models
Why it's wrong here
Parametric models often have higher bias.
- ✓
Parametric models train faster than non-parametric models
Why this is correct
Non-parametric models often require more computation.
- ✗
Non-parametric models are less prone to overfitting
Why it's wrong here
Non-parametric models can overfit easily.
- ✓
Parametric models typically require less training data
Why this is correct
Parametric models have fixed number of parameters.
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