A) Nonparametric B) Nonlinear C) Robust against multicollinearity D) All of the above A) Nonparametric B) Nonlinear C) Robust against multicollinearity D) All of the above

Exam 4: Classification and Regression Trees in R

In a classification tree, there may be more than one path to the same terminal value of the outcome variable. This attribute of classification trees is labeled ________________.

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Question 1

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Equifinality.
Classification trees but not regression trees may be equifinal, which means multiple paths may lead to the same level of the categorical outcome (e.g., "High" rather than "Medium" or "Low". Equifinality supports a more complex understanding of the dynamics among variables and may be helpful for theory exploration and construction.

The process of simplifying a decision tree solution by removing nodes is called __________________.

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Question 2

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Pruning.
Pruning is an important part of arriving at optimal solutions using decision tree analysis. Some tree algorithms build pruning in, not requiring explicit steps on the part of the researcher.

In decision tree analysis, predictor variables may be nominal, ordinal, or continuous in level.

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(True/False)

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Question 3

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True

Interaction effects are handled automatically by decision trees, so there is no need to add explicit interaction terms as one would in OLS regression.

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Question 4

Name the most common approach used to address the problem of overfitting and failure to generalize in decision tree analysis? ____________________________________

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Question 5

What are the two major types of decision tree? _ and ____ trees.

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Question 6

The label for the first or top node in a decision tree is the ______________ node.

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Question 7

In a decision tree, what is a label for one of the outcome values?

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Question 8

The same predictor variable may appear in different paths in a decision tree and even more than once in the same path.

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Question 9

In decision tree analysis, it is not necessary to create dummy variables for categorical variables or to interpret results in terms of reference categories.

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Question 10

In a decision tree, paths through the tree to an outcome value represent:

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Question 11

What is a way decision tree analysis might complement OLS regression analysis?

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Question 12

Decision trees are

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Question 13

What is true of sample size in relation to decision tree analysis?

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Question 14

Selecting salient variables prior to running the analysis is more important in decision trees than in OLS regression.

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Question 15

Showing 1 - 15 of 15

In a classification tree, there may be more than one path to the same terminal value of the outcome variable. This attribute of classification trees is labeled ________________.

The process of simplifying a decision tree solution by removing nodes is called __________________.

In decision tree analysis, predictor variables may be nominal, ordinal, or continuous in level.

Interaction effects are handled automatically by decision trees, so there is no need to add explicit interaction terms as one would in OLS regression.

Name the most common approach used to address the problem of overfitting and failure to generalize in decision tree analysis? ____________________________________

What are the two major types of decision tree? _ and ____ trees.

The label for the first or top node in a decision tree is the ______________ node.

In a decision tree, what is a label for one of the outcome values?

The same predictor variable may appear in different paths in a decision tree and even more than once in the same path.

In decision tree analysis, it is not necessary to create dummy variables for categorical variables or to interpret results in terms of reference categories.

In a decision tree, paths through the tree to an outcome value represent:

What is a way decision tree analysis might complement OLS regression analysis?

Decision trees are

What is true of sample size in relation to decision tree analysis?

Selecting salient variables prior to running the analysis is more important in decision trees than in OLS regression.

Using and Abusing Data Analytics in Social Science

Statistical Analytics With R Part 1

Statistical Analytics With R Part 2

Random Forests

Modeling and Machine Learning

Neural Network Models and Deep Learning

Network Analysis

Text Analytics

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Exam 4: Classification and Regression Trees in R

In a classification tree, there may be more than one path to the same terminal value of the outcome variable. This attribute of classification trees is labeled ________________.

The process of simplifying a decision tree solution by removing nodes is called __________________.

In decision tree analysis, predictor variables may be nominal, ordinal, or continuous in level.

Interaction effects are handled automatically by decision trees, so there is no need to add explicit interaction terms as one would in OLS regression.

Name the most common approach used to address the problem of overfitting and failure to generalize in decision tree analysis? ____________________________________

What are the two major types of decision tree? _______________________ and __________________________ trees.

The label for the first or top node in a decision tree is the ______________ node.

In a decision tree, what is a label for one of the outcome values?

The same predictor variable may appear in different paths in a decision tree and even more than once in the same path.

In decision tree analysis, it is not necessary to create dummy variables for categorical variables or to interpret results in terms of reference categories.

In a decision tree, paths through the tree to an outcome value represent:

What is a way decision tree analysis might complement OLS regression analysis?

Decision trees are

What is true of sample size in relation to decision tree analysis?

Selecting salient variables prior to running the analysis is more important in decision trees than in OLS regression.

Using and Abusing Data Analytics in Social Science

Statistical Analytics With R Part 1

Statistical Analytics With R Part 2

Random Forests

Modeling and Machine Learning

Neural Network Models and Deep Learning

Network Analysis

Text Analytics

Filters

What are the two major types of decision tree? _ and ____ trees.