# Using Shape Metrics to Describe 2D Data Points

arXiv: https://arxiv.org/abs/2201.11857 · HTML: https://mlsanity.com/paper/2201.11857/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)

## Built on by (most cited first, 1 of 1)

- [Shape Proportions and Sphericity in n Dimensions](https://mlsanity.com/paper/2208.06292/) (2022)

## Abstract (arXiv)

Traditional machine learning (ML) algorithms, such as multiple regression, require human analysts to make decisions on how to treat the data. These decisions can make the model building process subjective and difficult to replicate for those who did not build the model. Deep learning approaches benefit by allowing the model to learn what features are important once the human analyst builds the architecture. Thus, a method for automating certain human decisions for traditional ML modeling would help to improve the reproducibility and remove subjective aspects of the model building process. To that end, we propose to use shape metrics to describe 2D data to help make analyses more explainable and interpretable. The proposed approach provides a foundation to help automate various aspects of model building in an interpretable and explainable fashion. This is particularly important in applications in the medical community where the `right to explainability' is crucial. We provide various simulated data sets ranging from probability distributions, functions, and model quality control checks (such as QQ-Plots and residual analyses from ordinary least squares) to showcase the breadth of this approach.