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# Decision Tree Structure and Building Steps Quiz

Created by
@TantalizingThulium

### What is the purpose of leaf nodes in a decision tree?

• Split each decision node into two or more subnodes
• Prune unnecessary branches in the tree
• Represent prediction outputs for the model (correct)
• Determine the Gini impurity of a node
• ### In a regression tree, what is used to predict the outcome in a region?

• Mean of the training observations (correct)
• Number of decision nodes
• Most commonly occurring class in the region
• Gini impurity value
• ### What is the initial node in a decision tree called?

• Regression node
• Leaf node
• Root node (correct)
• Pruning node
• ### Which type of problems can tree-based methods be applied to?

<p>Regression and classification problems</p> Signup and view all the answers

### What does bagging refer to in the context of building trees?

<p>Bootstrap aggregating to reduce variance</p> Signup and view all the answers

### How are decision nodes navigated in a decision tree?

<p>Via if-then rules</p> Signup and view all the answers

### What is the purpose of making predictions in the region Rj of a classification tree?

<p>To classify observations into classes based on majority vote in that region</p> Signup and view all the answers

### How is the split level determined for each rectangle R in the context of decision trees?

<p>By computing the quantity I(R) based on class proportions</p> Signup and view all the answers

### What is the advantage of decision trees over other regression and classification approaches?

<p>Ease of interpretation and explanation</p> Signup and view all the answers

### When would linear regression outperform regression trees according to the text?

<p>When the relationship between predictors and response is linear</p> Signup and view all the answers

### What can result from a small change in the training data when using decision trees?

<p>A big change in the tree structure</p> Signup and view all the answers

### How are decision trees different from linear regression with regard to handling qualitative predictors?

<p>Decision trees do not need dummy variables for qualitative predictors</p> Signup and view all the answers

## Study Notes

### Decision Trees

• A decision tree starts with a single root node and consists of decision nodes, which split into two or more subnodes based on features of a data set.
• The tree is navigated via if-then rules, with leaf nodes representing prediction outputs for the model.

### Tree-Based Methods

• Can be applied to regression and classification problems.
• Involve stratifying or segmenting the predictor space into simple regions.
• Predictions are made using the mean or mode of the training observations in each region.

### Regression Trees

• Used for predicting continuous outcomes (e.g. baseball players' salaries).
• Basic steps:
• Divide the predictor space into distinct and non-overlapping regions.
• Make the same prediction for every observation in each region, which is the mean of the response values for the training observations in that region.

### Classification Trees

• Used for predicting categorical outcomes (e.g. wine ratings).
• Basic steps:
• Divide the predictor space into distinct and non-overlapping regions.
• Make the same prediction for every observation in each region, which is the category with the majority of observations in that region.

### Gini Impurity

• A measure used to decide on the split level in a tree.
• Calculated as I(R) = 1 - sum (p_k)^2, where p_k is the proportion of observations in a rectangle R that belong to class k.
• Used to compare the reduction in this measure across all splits and predictor variables.

• Easy to explain and interpret.
• Can handle qualitative predictors without dummy variables.
• Generally have lower predictive accuracy than other approaches.
• Can be non-robust to changes in the training data.

### Decision Trees vs. Linear Regression

• If the relationship between predictors and response is linear, linear regression may outperform decision trees.
• If the relationship is non-linear, decision trees may outperform classical approaches.
• Decision trees can use a feature multiple times in the same model.

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## Description

Test your knowledge on the structure and building steps of a decision tree, including Gini impurity, pruning, bagging, and random forests. Learn about how decision trees start with a root node and navigate through decision nodes based on if-then rules.

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