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Towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) MIg analyZeR (mizr) Package

Our work towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) aims to provide plots, tools, methods, and strategies to extract insights out of various machine learning (ML) and Artificial Intelligence (AI) data.Included in this software is the MIg analyZeR (mizr) R software package that produces various plots. It was initially developed within the Multimodal Information Group (MIG) at the National Institute of Standards and Technology (NIST).This software is documented, configured to be installed as an R package, and comes with an example SEMAIT script with an example (system, dataset, metrics, score) ML tuple set that we constructed ourselves.

About this Dataset

Updated: 2024-02-22
Metadata Last Updated: 2023-03-10 00:00:00
Date Created: N/A
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Data Provided by:
analysis software
Dataset Owner: N/A

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Table representation of structured data
Title Towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) MIg analyZeR (mizr) Package
Description Our work towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) aims to provide plots, tools, methods, and strategies to extract insights out of various machine learning (ML) and Artificial Intelligence (AI) data.Included in this software is the MIg analyZeR (mizr) R software package that produces various plots. It was initially developed within the Multimodal Information Group (MIG) at the National Institute of Standards and Technology (NIST).This software is documented, configured to be installed as an R package, and comes with an example SEMAIT script with an example (system, dataset, metrics, score) ML tuple set that we constructed ourselves.
Modified 2023-03-10 00:00:00
Publisher Name National Institute of Standards and Technology
Contact mailto:peter.fontana@nist.gov
Keywords analysis software , artificial intelligence , machine learning , design of experiments
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