Search Results for author: Yaron Shaposhnik

Found 3 papers, 1 papers with code

A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations

no code implementations4 Jun 2021 Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang

We propose a framework for such decisions, including a globally interpretable machine learning model, an interactive visualization of it, and several types of summaries and explanations for any given decision.

BIG-bench Machine Learning Interpretable Machine Learning

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization

2 code implementations8 Dec 2020 Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik

In this work, our main goal is to understand what aspects of DR methods are important for preserving both local and global structure: it is difficult to design a better method without a true understanding of the choices we make in our algorithms and their empirical impact on the lower-dimensional embeddings they produce.

Data Visualization Dimensionality Reduction

An Interpretable Model with Globally Consistent Explanations for Credit Risk

no code implementations30 Nov 2018 Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang

We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment.

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