iForest: Interpreting Random Forests via Visual Analytics

By Xun Zhao, Yanhong Wu, Dik Lun Lee, Weiwei Cui. 2019

181 citations recorded by the source corpus.

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. The ensemble nature of the model helps random forests outperform any individual decision tree. However, it also leads to a poor model interpretability, which significantly hinders the model from being used in fields that require transparent and explainable predictions, such as medical diagnosis and financial fraud detection. The interpretation challenges stem from the variety and complexity of the contained decision trees. Each decision tree has its unique structure and properties, such as the features used in the tree and the feature threshold in each tree node. Thus, a data input may lead to a variety of decision paths. To understand how a final prediction is achieved, it is desired to understand and compare all decision paths in the context of all tree structures, which is a huge challenge for any users. In this paper, we propose a visual analytic system aiming at interpreting random forest models and predictions. In addition to providing users with all the tree information, we summarize the decision paths in random forests, which eventually reflects the working mechanism of the model and reduces users' mental burden of interpretation. To demonstrate the effectiveness of our system, two usage scenarios and a qualitative user study are conducted.

Subfields: Artificial Intelligence, Biophysics, Computer Vision and Pattern Recognition

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Citations in the corpus

ViCE: visual counterfactual explanations for machine learning models (2020, methodology): “For example, Manifold [24] enables the comparison of data distribution at different levels of granularity; RuleMatrix [14] visualizes extracted rules for a given model; iForest [25] and EnsembleMatrix [19] attempt to explain ensemble models.”
The What-If Tool: Interactive Probing of Machine Learning Models (2019, methodology): “Another system, iForest [35], has similar capabilities as WIT, but is solely for use with random forest models.”
A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “…contribute to classification and regression models’ predictions by computing the situational importance of features (local or global). iForest (Zhao et al., 2019) is an interactive visualization system that helps users interpret random forests model by revealing relations between input…”