Search Results for author:
Found 10 papers, 8 papers with code
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.
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.
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.
HUMAP: Hierarchical Uniform Manifold Approximation and Projection
Dimensionality reduction (DR) techniques help analysts understand patterns in high-dimensional spaces.
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.
Xtreaming: an incremental multidimensional projection technique and its application to streaming data
To the best of our knowledge, it is the first methodology that is capable of evolving a projection to faithfully represent new emerging structures without the need to store all data, providing reliable results for efficiently and effectively projecting streaming data.
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.
A Grid-based Method for Removing Overlaps of Dimensionality Reduction Scatterplot Layouts
Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous visualization tool for analyzing multidimensional datasets.
