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1. Decision Trees can be used for Classification Regression O both Classification and Regression O none of the above 2. Decision Trees have: O a root node O leaf nodes O branches O all of the above 3. Decision Trees are prone to: O overfitting underfitting O perfect accuracy O being just like linear models 4. Limiting the growth of a Decision Tree can prevent overfitting, O True O False 5. Random Forests are not Ensembles of Decision Trees O True O False 6. Training is slow on Decision Trees because O the model is so complicated O the model takes a while to grow O the model requires a lot of resources O the training is not slow because of the simplicity of the model (unless its a lot of data) REVIOUS View Questions Studypool 8 X ou learn.codingdojo.com/m/336/9233/63113 jo NAN... Data Gov - Ma. CART Models Qulz: Cart Mo... Search Q 7. Decision trees are simple to understand interpret, and visualize. O True O False 8. An advantage of CART models is: O Decision trees can be unstable because small variations in the data might result in a completely different tree being generated. This is called variance, which needs to be lowered by methods like bagging and boosting O Decision trees implicitly perform variable screening or feature selection. O Decision-tree learners can create over-complex trees that do not generalize the data well. (overfitting) O Decision tree learners create biased trees if some classes dominate 9. CART models can handle both numerical and categorical data True O False 10. CART models can not handle multi-output problems. O True O False 11. Decision trees require relatively little effort from users for data preparation O True O False 12. Nonlinear relationships between parameters do not affect tree performance. O True O False 13. It is not recommended to balance the data sot prior to fitting with the decision tree O True O False 14. Boosting is a technique that can be used to improve the performance of decision tree learning O True O Fake EVIOUS G U > 74F D 37 727 PM false 12. Nonlinear relationships between parameters do not affect tree performance O True O False 13. It is not recommended to balance the data set prior to fitting with the decision tree, O True O False 14. Boosting is a technique that can be used to improve the performance of decision tree earning O True O False 15. Greedy algorithms cannot guarantee to return the globally optimal decision tree. This can be mitigated by training multiple trees where the features and samples are randomly sampled with replacement True O False Submit NEXT PREVIOUS IM M