Question: When using a Random Forest algorithm for a classification problem, which parameter adjustment is most likely to prevent overfitting? Decrease the minimum number of samples

When using a Random Forest algorithm for a classification problem, which parameter adjustment is most likely to prevent overfitting?
Decrease the minimum number of samples required to split an internal node.
Increase the number of features to consider at each split.
Decrease the max depth of trees.
Increase the number of trees.

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