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Decision Trees in Machine Learning

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Questions and Answers

What is the process of dividing a node into two or more sub-nodes called?

Merging

Branching

Splitting (correct)

Pruning

What is the topmost node in a decision tree structure that represents the entire population or sample?

Parent Node

Decision Node

Leaf Node

Root Node (correct)

What is a node that does not split further called?

Leaf Node (correct)

Parent Node

Decision Node

Child Node

What is the process of removing sub-nodes of a decision node called?

Pruning Signup and view all the answers

What is a subsection of the entire tree called?

Sub-Tree Signup and view all the answers

What is the node that splits into further sub-nodes called?

Decision Node Signup and view all the answers

What is the primary criterion for selecting the best attribute to split the data in a decision tree?

Information gain Signup and view all the answers

What is the purpose of recursively partitioning the data in a decision tree?

To split the data based on the best attribute Signup and view all the answers

What is a common stopping criterion for building a decision tree?

Maximum depth reached Signup and view all the answers

What is a characteristic of a decision tree that makes it accessible to non-experts?

Easy to understand and interpret Signup and view all the answers

What is a disadvantage of a single decision tree?

Sensitive to small changes in data Signup and view all the answers

What is the primary advantage of using a random forest over a single decision tree?

Improves generalization by averaging the probabilities Signup and view all the answers

What is the primary mechanism by which the random forest algorithm generates a prediction?

By aggregating the predictions from multiple decision trees through voting Signup and view all the answers

What is the role of the decision tree algorithm in the random forest analogy?

To ask friends about their individual travel experience and get one recommendation Signup and view all the answers

What is the outcome of the voting procedure in the random forest algorithm?

A single best place for the trip Signup and view all the answers

How does the random forest algorithm handle the feature importance?

It provides a relative importance score for each feature Signup and view all the answers

What is the application of the random forest algorithm in the context of loan applications?

To identify loyal loan applicants Signup and view all the answers

What is the relationship between the random forest algorithm and the Boruta algorithm?

The random forest algorithm lies at the base of the Boruta algorithm Signup and view all the answers

Decision Trees in Machine Learning

Choose a study mode

Podcast

Questions and Answers

What is the process of dividing a node into two or more sub-nodes called?

What is the topmost node in a decision tree structure that represents the entire population or sample?

What is a node that does not split further called?

What is the process of removing sub-nodes of a decision node called?

What is a subsection of the entire tree called?

What is the node that splits into further sub-nodes called?

What is the primary criterion for selecting the best attribute to split the data in a decision tree?

What is the purpose of recursively partitioning the data in a decision tree?

What is a common stopping criterion for building a decision tree?

What is a characteristic of a decision tree that makes it accessible to non-experts?

What is a disadvantage of a single decision tree?

What is the primary advantage of using a random forest over a single decision tree?

What is the primary mechanism by which the random forest algorithm generates a prediction?

What is the role of the decision tree algorithm in the random forest analogy?

What is the outcome of the voting procedure in the random forest algorithm?

How does the random forest algorithm handle the feature importance?

What is the application of the random forest algorithm in the context of loan applications?

What is the relationship between the random forest algorithm and the Boruta algorithm?

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Decision Trees in Machine Learning

Choose a study mode

Podcast

Questions and Answers

What is the process of dividing a node into two or more sub-nodes called?

What is the topmost node in a decision tree structure that represents the entire population or sample?

What is a node that does not split further called?

What is the process of removing sub-nodes of a decision node called?

What is a subsection of the entire tree called?

What is the node that splits into further sub-nodes called?

What is the primary criterion for selecting the best attribute to split the data in a decision tree?

What is the purpose of recursively partitioning the data in a decision tree?

What is a common stopping criterion for building a decision tree?

What is a characteristic of a decision tree that makes it accessible to non-experts?

What is a disadvantage of a single decision tree?

What is the primary advantage of using a random forest over a single decision tree?

What is the primary mechanism by which the random forest algorithm generates a prediction?

What is the role of the decision tree algorithm in the random forest analogy?

What is the outcome of the voting procedure in the random forest algorithm?

How does the random forest algorithm handle the feature importance?

What is the application of the random forest algorithm in the context of loan applications?

What is the relationship between the random forest algorithm and the Boruta algorithm?

More Like This

Supervised vs

ML High-Level Tools &amp; Algorithms

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