Decision Trees

What is entropy calculation in decision trees?

• Entropy calculation in decision trees is a method to calculate the number of features in a dataset

• Entropy calculation in decision trees is a method to quantify the amount of uncertainty or randomness in a dataset, which helps in making decisions about splitting nodes during the tree-building process.

• Entropy calculation in decision trees is only applicable to linear regression models

• Entropy calculation in decision trees is used to measure the accuracy of the model

Explain the process of feature selection in decision trees.

• Feature selection in decision trees involves randomly selecting features without evaluating their importance.

• Feature selection in decision trees requires selecting the features with the least importance to the predictive model.

• The process of feature selection in decision trees involves evaluating each feature's importance and selecting the ones that contribute the most to the tree's predictive power.

• Feature selection in decision trees means selecting all available features without considering their predictive power.

What are the components of a decision tree?

• nodes, branches, leaves

• twigs

• edges

• trunk

What does CART stand for in decision trees?

• Classification and Regression Forests

• Categorical Analysis and Regression Techniques

• Cluster Analysis and Regression Trees

• Classification and Regression Trees

What is C4.5 in the context of decision trees?

• C4.5 is a software for editing images

• C4.5 is a type of tree bark

• C4.5 is a mathematical equation used in physics

• C4.5 is an algorithm for constructing decision trees.

How is entropy used in decision tree algorithms?

• Entropy is used to calculate the information gain for each attribute in decision tree algorithms.

• Entropy is used to calculate the accuracy of decision tree algorithms.

• Entropy is used to sort the data in decision tree algorithms.

• Entropy is used to determine the number of nodes in decision tree algorithms.

Discuss the importance of feature selection in decision tree models.

• Feature selection has no impact on model interpretability in decision tree models

• Feature selection is not necessary in decision tree models

• Feature selection increases model complexity in decision tree models

• Feature selection is crucial in decision tree models to enhance model accuracy, reduce complexity, and improve interpretability.

Differentiate between classification and regression trees (CART) in decision trees.

• Classification trees are for continuous target variables, regression trees are for categorical target variables.

• Classification trees are for categorical target variables, regression trees are for continuous target variables.

• Classification trees are used for regression problems, regression trees are used for classification problems.

• Classification trees are for continuous target variables, regression trees are for ordinal target variables.

What are the advantages of using C4.5 algorithm in decision tree construction?

• C4.5 algorithm requires manual pruning of the tree

• C4.5 algorithm can only handle discrete attributes

• C4.5 algorithm cannot handle missing values

• C4.5 algorithm can handle both continuous and discrete attributes, handle missing values, and automatically prune the tree.

Explain the concept of pruning in decision trees.

• Pruning in decision trees involves adding more branches to increase the tree's complexity.

• Pruning in decision trees refers to the process of randomly selecting branches to keep in the tree.

• Pruning in decision trees involves reducing the size of the tree by removing unnecessary branches that do not contribute significantly to the tree's predictive power.

• Pruning in decision trees has no impact on the accuracy of the model.