Decision Trees in Machine Learning

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?