Balancing accuracy gains with other objectives such as interpretability is a key challenge
when building decision trees. However, this process is difficult to automate because it …

Decision trees are an extremely popular machine learning technique. Unfortunately,
overfitting in decision trees still remains an open issue that sometimes prevents achieving …

As an ensemble model that consists of many independent decision trees, random forests
generate predictions by feeding the input to internal trees and summarizing their outputs …

Decision trees are an intuitive yet powerful tool for performing predictive data analysis in
data mining. In order to generate an adequate predictive model from a data set, a data …

Abstract Summary treeheatr is an R package for creating interpretable decision tree
visualizations with the data represented as a heatmap at the tree's leaf nodes. The …

In the field of information visualization, the concept of “tasks” is an essential component of
theories and methodologies for how a visualization researcher or a practitioner understands …

As a popular machine learning model, decision trees classify and generalize well, but face
challenges in engineering applications: 1) Sensitivity to perturbations and lack of …

Visual analytics enables the coupling of machine learning models and humans in a tightly
integrated workflow, addressing various analysis tasks. Each task poses distinct demands to …

This paper introduces a brand new tree library treelite 1. The library is a toolbox to facilitate
easy deployment of models and accelerate prediction performance. It has a Python wrapper …

We present a system for the interactive construction and analysis of decision trees that
enables domain experts to bring in domain specific knowledge. We identify different user …