# A Visual Technique to Analyze Flow of Information in a Machine Learning System

## TL;DR

This paper introduces a visual Sankey Diagram-based technique to analyze and understand the flow of information in machine learning systems, aiding in diagnostics and comparison of classifiers.

## Contribution

It proposes a novel visualization approach tailored for ML systems to improve understanding of data flow, model training, and prediction diagnostics.

## Key findings

- Effective visualization of data flow in ML systems
- Enhanced diagnostic capabilities for model predictions
- Facilitates comparison of multiple classifiers

## Abstract

Machine learning (ML) algorithms and machine learning based software systems implicitly or explicitly involve complex flow of information between various entities such as training data, feature space, validation set and results. Understanding the statistical distribution of such information and how they flow from one entity to another influence the operation and correctness of such systems, especially in large-scale applications that perform classification or prediction in real time. In this paper, we propose a visual approach to understand and analyze flow of information during model training and serving phases. We build the visualizations using a technique called Sankey Diagram - conventionally used to understand data flow among sets - to address various use cases of in a machine learning system. We demonstrate how the proposed technique, tweaked and twisted to suit a classification problem, can play a critical role in better understanding of the training data, the features, and the classifier performance. We also discuss how this technique enables diagnostic analysis of model predictions and comparative analysis of predictions from multiple classifiers. The proposed concept is illustrated with the example of categorization of millions of products in the e-commerce domain - a multi-class hierarchical classification problem.