Abstract
Two univariate split methods are proposed for the construction of classification trees with multyway splits named CRUISE (Classification Rule with Unbiased Interaction Selection and Estimation). A major strenght of the univariate split methods is that they have negligible bias in variable selection, both when the variables differ in the number of splits they offer and when they differ in number of missing values. This is an advantage because inference from the tree sructures can be adversely affected by selection bias. These methods also improve interpretability of trees by reducing tree depht. Application of CRUISE algorithms to Fisher’s Iris data is to predict the variety of an Iris flower based on its petal and sepal lenght and widht. Results show that it only takes one variable to do so. Therefoce, the new methods are highly competitive in terms of computational speed and classification accuracy of future observation.
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Claims 4
Claim · model-written summary
CRUISE used one variable to predict Iris flower variety from petal and sepal measurements.
IrisOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Application of CRUISE algorithms to Fisher’s Iris data is to predict the variety of an Iris flower based on its petal and sepal lenght and widht. Results show that it only takes one variable to do so.”
Claim · model-written summary
The univariate split methods had negligible variable-selection bias.
- Outcome
- bias in variable selectione0
- Direction
- negligiblee0
- Comparator
- variables differ in the number of splits they offere0
Not reportedValue
Verbatim
e0“A major strenght of the univariate split methods is that they have negligible bias in variable selection, both when the variables differ in the number of splits they offer and when they differ in number of missing values.”
Claim · model-written summary
The methods improved tree interpretability by reducing depth.
Not reportedComparatorValue
Verbatim
e0“These methods also improve interpretability of trees by reducing tree depht.”
Claim · model-written summary
The methods were judged competitive in computational speed and classification accuracy.
Not reportedComparatorValue
Verbatim
e0“the new methods are highly competitive in terms of computational speed and classification accuracy of future observation.”