Question: Question 2 When we use RandomForestClassifier ( ) from sklearn, do we need to set hyperparameters values to control the tree structure from being too

Question 2
When we use RandomForestClassifier() from sklearn, do we need to set hyperparameters values to control the tree structure from being too complex? (Multiple answers)
No, bagging can deal with overfitting
No, overfitting trees are build to gain better performance after averaging the aggregated result
Yes, adjusting hyperparameters can help balancing the trade-off between bias and variance
Yes, as tree structure is easy to be overfitted,
Question 2 When we use RandomForestClassifier ( )

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