Visualization Methods for Adjustment Policies and Work History Related to Machine Learning Models

Abstract

We propose a method to visualize their adjustment process to support the quality evaluation of machine learning models and evaluate model creators’ skills. While many visualization methods for training data and model structure have been published, there are few methods for visualizing information about the creators of models. Active intervention by workers in the model creation process effectively improves accuracy, and visualization of worker information is considered useful for understanding and improving the models. Therefore, we have designed a visualization tool that focuses on the visualization of model modification history and the purpose of each adjustment task. The tool calculates the differences in models during model tuning and visualizes them together with the intention of tuning (e.g., prioritizing model accuracy improvement, considering computational resource limitations, etc.). We present the results of visualizing the work history obtained from the participation records of several machine learning competitions.