Addressing Bias in Visualization Recommenders by Identifying Trends in Training Data: Improving VizML Through a Statistical Analysis of the Plotly Community Feed. (arXiv:2203.04937v1 [cs.IR])

Machine learning is a promising approach to visualization recommendation due
to its high scalability and representational power. Researchers can create a
neural network to predict visualizations from input data by training it over a
corpus of datasets and visualization examples. However, these machine learning
models can reflect trends in their training data that may negatively affect
their performance. Our research project aims to address training bias in
machine learning visualization recommendation systems by identifying trends in
the training data through …

analysis arxiv bias community data plotly statistical training training data trends visualization