Final answer:
Random Forest classifiers randomly sample data, are individually insensitive to data changes, and do not need to be strongly correlated.
Explanation:
The correct statement about Random Forest classifiers is:
- The trees within the forest randomly sample from the dataset with replacement, which contributes to the diversity of the trees.
- These trees within the forest are individually insensitive to data changes, as each tree is trained independently.
- Contrary to the statement provided, the trees within the forest do not have to be strongly correlated; in fact, their independence is crucial for the effectiveness of Random Forest.
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