dtgini: Runs a Decision Tree Gini Index and Decision Tree Information...
In abcsFrederick/BMDK: Biomarker Discovery Kit
Description
Usage
Arguments
Value
Description
This procedure uses each feature to perform a one-node split of all N samples
in the parent node into 2 daughter nodes using N-1 cut points, as in a
decision tree. This procedure's goal is to find the most optimal split to
identify between cases and controls for each feature. To do this, this function
identifies the smallest weighted Gini Index value and smallest weighted
Information Gain value for each feature.
Usage
1
dtgini(dat)
Arguments
dat
a list containing 3 elements: case, a list of case/control statuses;
feat, a matrix of normalized feature data; maxfeat, a list of max features
from each column in feat
Value
finalresults a list of Gini Indices
abcsFrederick/BMDK documentation built on Aug. 9, 2021, 2:19 p.m.
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Description Usage Arguments Value
Description
This procedure uses each feature to perform a one-node split of all N samples in the parent node into 2 daughter nodes using N-1 cut points, as in a decision tree. This procedure's goal is to find the most optimal split to identify between cases and controls for each feature. To do this, this function identifies the smallest weighted Gini Index value and smallest weighted Information Gain value for each feature.
Usage
1 | dtgini(dat)
|
Arguments
dat |
a list containing 3 elements: case, a list of case/control statuses; feat, a matrix of normalized feature data; maxfeat, a list of max features from each column in feat |
Value
finalresults a list of Gini Indices
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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