Final Exam

1.What is the primary goal of the cost-complexity function, $R_{\alpha}(T) = R(T) + \alpha|T|$ ?

2.As the complexity parameter, $\alpha$ , increases during the pruning process, what is the expected effect on the resulting optimal subtree?

3.In the context of 'Weakest Link Pruning', the 'effective alpha' for an internal node `t` is calculated using the formula $g(t) = \frac{R(t) - R(T_t)}{|T_t| - 1}$ . What does this value represent?

4.When performing weakest link pruning, the algorithm identifies the internal node with the smallest $g(t)$ value to prune next. True or False?

5.After calculating the effective alpha ( $g(t)$ ) for all internal nodes, Cost-Complexity Pruning generates a sequence of subtrees. What is the defining characteristic of this sequence?

6.How is the final, optimal subtree selected from the sequence generated by Cost-Complexity Pruning?

7.When plotting cross-validated error against alpha, the 'One Standard Error Rule' suggests choosing which model?

8.Pruning a decision tree typically the model's bias and its variance.

9.How does Cost-Complexity Pruning (a post-pruning technique) generally compare to pre-pruning (e.g., setting `max_depth`) in terms of finding an optimal tree?

10.In Scikit-learn, the `cost_complexity_pruning_path` method of a `DecisionTreeClassifier` returns a dictionary containing two key lists. What are they?

11.When using Scikit-learn's implementation of CCP, after you have used cross-validation to find the optimal `ccp_alpha`, what is your next step?

12.Cost-Complexity Pruning is applicable to both classification and regression trees.

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Cost-Complexity Tree Pruning Deep-Dive

Cost-Complexity Tree Pruning Deep-Dive

Final Exam

1.What is the primary goal of the cost-complexity function, $R_{\alpha}(T) = R(T) + \alpha|T|$ ?

2.As the complexity parameter, $\alpha$ , increases during the pruning process, what is the expected effect on the resulting optimal subtree?

3.In the context of 'Weakest Link Pruning', the 'effective alpha' for an internal node `t` is calculated using the formula $g(t) = \frac{R(t) - R(T_t)}{|T_t| - 1}$ . What does this value represent?

4.When performing weakest link pruning, the algorithm identifies the internal node with the smallest $g(t)$ value to prune next. True or False?

5.After calculating the effective alpha ( $g(t)$ ) for all internal nodes, Cost-Complexity Pruning generates a sequence of subtrees. What is the defining characteristic of this sequence?

6.How is the final, optimal subtree selected from the sequence generated by Cost-Complexity Pruning?

7.When plotting cross-validated error against alpha, the 'One Standard Error Rule' suggests choosing which model?

8.Pruning a decision tree typically the model's bias and its variance.

9.How does Cost-Complexity Pruning (a post-pruning technique) generally compare to pre-pruning (e.g., setting `max_depth`) in terms of finding an optimal tree?

10.In Scikit-learn, the `cost_complexity_pruning_path` method of a `DecisionTreeClassifier` returns a dictionary containing two key lists. What are they?

11.When using Scikit-learn's implementation of CCP, after you have used cross-validation to find the optimal `ccp_alpha`, what is your next step?

12.Cost-Complexity Pruning is applicable to both classification and regression trees.

Final Exam

1.What is the primary goal of the cost-complexity function, Rα(T)=R(T)+α∣T∣R_{\alpha}(T) = R(T) + \alpha|T|Rα​(T)=R(T)+α∣T∣?

2.As the complexity parameter, α\alphaα, increases during the pruning process, what is the expected effect on the resulting optimal subtree?

3.In the context of 'Weakest Link Pruning', the 'effective alpha' for an internal node t is calculated using the formula g(t)=R(t)−R(Tt)∣Tt∣−1g(t) = \frac{R(t) - R(T_t)}{|T_t| - 1}g(t)=∣Tt​∣−1R(t)−R(Tt​)​. What does this value represent?

4.When performing weakest link pruning, the algorithm identifies the internal node with the smallest g(t)g(t)g(t) value to prune next. True or False?

5.After calculating the effective alpha (g(t)g(t)g(t)) for all internal nodes, Cost-Complexity Pruning generates a sequence of subtrees. What is the defining characteristic of this sequence?

6.How is the final, optimal subtree selected from the sequence generated by Cost-Complexity Pruning?

7.When plotting cross-validated error against alpha, the 'One Standard Error Rule' suggests choosing which model?

8.Pruning a decision tree typically ________ the model's bias and ________ its variance.

9.How does Cost-Complexity Pruning (a post-pruning technique) generally compare to pre-pruning (e.g., setting max_depth) in terms of finding an optimal tree?

10.In Scikit-learn, the cost_complexity_pruning_path method of a DecisionTreeClassifier returns a dictionary containing two key lists. What are they?

11.When using Scikit-learn's implementation of CCP, after you have used cross-validation to find the optimal ccp_alpha, what is your next step?

12.Cost-Complexity Pruning is applicable to both classification and regression trees.

1.What is the primary goal of the cost-complexity function, $R_{\alpha}(T) = R(T) + \alpha|T|$ ?

2.As the complexity parameter, $\alpha$ , increases during the pruning process, what is the expected effect on the resulting optimal subtree?

3.In the context of 'Weakest Link Pruning', the 'effective alpha' for an internal node `t` is calculated using the formula $g(t) = \frac{R(t) - R(T_t)}{|T_t| - 1}$ . What does this value represent?

4.When performing weakest link pruning, the algorithm identifies the internal node with the smallest $g(t)$ value to prune next. True or False?

5.After calculating the effective alpha ( $g(t)$ ) for all internal nodes, Cost-Complexity Pruning generates a sequence of subtrees. What is the defining characteristic of this sequence?

8.Pruning a decision tree typically the model's bias and its variance.

9.How does Cost-Complexity Pruning (a post-pruning technique) generally compare to pre-pruning (e.g., setting `max_depth`) in terms of finding an optimal tree?

10.In Scikit-learn, the `cost_complexity_pruning_path` method of a `DecisionTreeClassifier` returns a dictionary containing two key lists. What are they?

11.When using Scikit-learn's implementation of CCP, after you have used cross-validation to find the optimal `ccp_alpha`, what is your next step?