Build Decision Trees with Optimal Multivariate Splits

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Documentation for package ‘bsnsing’ version 1.0.1

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bsnsing-package bsnsing: Build Decision Trees with Optimal Multivariate Splits
auto auto
binarize Create Binary Variables by the Classification Target
binarize.factor Create Binary Features based on a Factor Vector
binarize.numeric Create Binary Features based on a Numeric Vector
binarize.y Recode a Variable with Two Unique Values into an 0/1 Vector
BreastCancer BreastCancer
bscontrol Define Parameters for the 'bsnsing' Fit
bslearn Find the Optimal Boolean Rule for Binary Classification
bsnsing Learn a Classification Tree using Boolean Sensing
bsnsing.default Learn a Classification Tree with Boolean Sensing
bsnsing.formula Learn a Classification Tree using Boolean Sensing
GlaucomaMVF GlaucomaMVF
import_external_rules Import split rules from other packages
iris iris
mbsnsing A class that contains multi-class classification model built by bsnsing. Can be used in summary and predict functions.
mbsnsing-class A class that contains multi-class classification model built by bsnsing. Can be used in summary and predict functions.
plot.bsnsing Generate latex code for plotting a bsnsing tree
plot.mbsnsing Generate latex code for plotting an mbsnsing tree
predict.bsnsing Make Predictions with a Fitted 'bsnsing' Model
predict.mbsnsing Make Predictions with a 'bsnsing' Model
print.bscontrol Print the Object of Class 'bscontrol'
print.bsnsing Print the Object of Class 'bsnsing'
print.mbsnsing Print the Object of Class 'mbsnsing'
print.summary.bsnsing Print the Summary of 'bsnsing' Model
print.summary.mbsnsing Print the summary of 'mbsnsing' model fits
rcpp_bslearn C implementation of the bslearn function
ROC_func Plot the ROC curve and calculate the AUC
summary.bsnsing Summarize the bsnsing Model Fits
summary.mbsnsing Summarize mbsnsing Model Fits