DA0-002 Data Analysis Practice Question
Exhibit
{"model_type": "random_forest", "n_estimators": 100, "max_depth": 5, "criterion": "gini"}Refer to the exhibit. Which type of ensemble method is being used?
⚠ Common exam trap
CompTIA often tests the distinction between bagging and boosting by showing parallel vs. sequential training diagrams, and the trap here is confusing the parallel bootstrap resampling with the sequential error-correction approach of boosting.
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
✓
Bagging
The exhibit shows multiple base models (Model 1, Model 2, Model 3) trained in parallel on bootstrap samples of the data, and their predictions are combined via averaging (regression) or majority voting (classification). This parallel training with resampled data and equal-weight aggregation is the defining characteristic of bagging (Bootstrap Aggregating).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Boosting
Why it's wrong here
Boosting trains weak learners sequentially, each correcting the previous model's errors through reweighting; the exhibit shows independent learners trained in parallel and combined by voting or averaging. It is tempting because boosting also combines multiple models, and would be correct where misclassified records are progressively upweighted across iterations.
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Stacking
Why it's wrong here
Stacking trains a meta-learner on base-model outputs, so the exhibit would show a second-level model consuming predictions. It is tempting because stacking genuinely combines heterogeneous learners, but it is the right choice only when a meta-learner is trained; the exhibit shows aggregation without one.
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Voting
Why it's wrong here
Voting aggregates predictions by majority or averaging without fitting a meta-model, so it fails when the exhibit shows a meta-learner trained on base outputs. It is tempting because voting is a genuine ensemble combiner, but it is correct only when no second-level learner is fitted.
- ✓
Bagging
Why this is correct
Bagging trains multiple instances of the same algorithm on bootstrap samples drawn with replacement, then aggregates their predictions by voting or averaging. This parallel, variance-reducing structure distinguishes it from boosting, which trains sequentially on reweighted data.
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