bsnsing-package: bsnsing: Build Decision Trees with Optimal Multivariate...

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bsnsing: Build Decision Trees with Optimal Multivariate Splits

Description

The bsnsing package provides functions for building a decision tree classifier and making predictions. It solves a mixed-integer programming (MIP) model to maximize the Gini reduction at each node split, and each node split rule can utilize multiple input variables. Benchmarking experiments on 75 open data sets suggest that bsnsing trees are the most capable of discriminating new cases compared to trees trained by other decision tree codes including the rpart, C50, party and tree packages in R. Compared to other optimal decision tree packages, including DL8.5, OSDT, GOSDT and indirectly more, bsnsing stands out in its training speed, ease of use and broader applicability without losing in prediction accuracy. For more information, please check out the paper https://arxiv.org/abs/2205.15263, to be published in INFORMS Journal on Computing.

The ENUM algorithm

In this package, the MIP model is solved using the implicit enumeration (ENUM) algorithm. The experimental version at https://github.com/profyliu/bsnsing/ is able to use external solvers such as GUROBI, CPLEX and lpSolve (via specifying the opt.solver option in the bsnsing function). All benchmarking experiments were run using the C implementation of the ENUM algorithm, i.e., opt.solver = 'enum_c', which is the default setting.

More data sets

Several data frames (i.e., auto, iris, GlaucomaMVF and BreastCancer) used in the example code are included in this package. More two-class and multi-class classification data sets can be found at https://github.com/profyliu/bsnsing/.

Learn functions

The learn (train) functions include bsnsing, bsnsing.formula and bsnsing.default.

Predict functions

The predict functions include: predict.bsnsing and predict.mbsnsing.

Plot functions

A bsnsing object (tree) can be plotted into a PDF file, or in the form of latex code, by the function plot.bsnsing. The ROC curve can be plotted using the function ROC_func.

Auxilliary functions

Here is a list of internal functions of the package that are open for end users. summary.bsnsing summary.mbsnsing, binarize, binarize.numeric, binarize.factor, binarize.y, bslearn, bscontrol

Author(s)

Yanchao Liu

bsnsing documentation built on July 4, 2022, 1:06 a.m.