Pros and Cons of Decision Trees

It eliminates the need for pruning in decision trees

It guarantees accurate predictions for each data point

It reduces variance compared with an ordinary tree (correct)

It ensures sequential growth of trees for better model fit

Boosting reduces variance compared with an ordinary tree, while bagging does not

Bagging guarantees slow learning, while boosting allows for faster learning

Boosting uses sequential growth, while bagging grows trees independently (correct)

Bagging applies several weak learners one after the other, while boosting grows trees in parallel

It guarantees accurate predictions for each data point

It reduces variance compared with an ordinary tree

It applies several weak-learners one after the other (correct)

It grows trees independently from each other in parallel

<p>It is useful for various regression &amp; classification techniques, especially for trees (A)</p> Signup and view all the answers

<p>It is known for its ability to handle missing data and its speed (D)</p> Signup and view all the answers