Answer: (iii) Random subsets of the training data

Explanation — Bagging (Bootstrap Aggregating):

• Given training set $D$ of size $n$, Bagging creates $M$ bootstrap samples $D_m$: each by sampling $n$ points with replacement → random subset (≈63.2% unique per bag, rest out-of-bag).
• Train one base learner $f_m$ on each $D_m$ (e.g., decision trees in Random Forest).
• Aggregate: classification by majority vote, regression by averaging: $\hat{f}_{bag}(x)=\frac1M\sum f_m(x)$.
• Purpose: reduces variance, avoids overfitting, parallelizable.

Contrast:

• (i) same dataset — no diversity, defeats ensemble purpose
• (ii) only test data — would be leakage, never train on test
• (iv) feature-reduced only — that is feature bagging / Random Subspace part of Random Forest (both row + column sampling), but primary definition is row bootstrap.

So (iii) is core.