Search Results for author:
Found 12 papers, 7 papers with code
Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023
Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and bioinformatics.
DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine
This paper presents DimVis, a visualization tool that employs supervised Explainable Boosting Machine (EBM) models (trained on user-selected data of interest) as an interpretation assistant for DR projections.
Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling
As deep neural networks are more commonly deployed in high-stakes domains, their black-box nature makes uncertainty quantification challenging.
Pre-registration for Predictive Modeling
Amid rising concerns of reproducibility and generalizability in predictive modeling, we explore the possibility and potential benefits of introducing pre-registration to the field.
DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps
As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML.
MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels
Stacking (or stacked generalization) is an ensemble learning method with one main distinctiveness from the rest: even though several base models are trained on the original data set, their predictions are further used as input data for one or more metamodels arranged in at least one extra layer.
HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques
Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model.
VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Trees
Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees.
FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
Despite that, while several visual analytics tools exist to monitor and control the different stages of the ML life cycle (especially those related to data and algorithms), feature engineering support remains inadequate.
VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
The challenge is exacerbated by the fact that most ML models are complex internally, and training involves trial-and-error processes that could remarkably affect the predictive result.
StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
Our system, StackGenVis, assists users in dynamically adapting performance metrics, managing data instances, selecting the most important features for a given data set, choosing a set of top-performant and diverse algorithms, and measuring the predictive performance.
t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains.
