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.
- ✗
Boosting
Why it's wrong here
Boosting builds models sequentially; random forest is not boosting.
- ✗
Stacking
Why it's wrong here
Stacking combines different algorithms, not a single algorithm like random forest.
- ✗
Voting
Why it's wrong here
Voting aggregates predictions, but random forest internally uses voting among its own trees.
- ✓
Bagging
Why this is correct
Random forest uses bagging (bootstrap aggregating) to create multiple decision trees.
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