Preparing structured data sets for machine learning
A technique for automated preparation of tabular data for machine learning, including options for machine learning derived infill, feature importance evaluations, and/or dimensionality reduction. Validation data sets may be consistently prepared to training data sets based on properties of the train...
Gespeichert in:
1. Verfasser: | |
---|---|
Format: | Patent |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | Teague, Nicholas John |
description | A technique for automated preparation of tabular data for machine learning, including options for machine learning derived infill, feature importance evaluations, and/or dimensionality reduction. Validation data sets may be consistently prepared to training data sets based on properties of the training data saved in a metadata database. Additional data sets may be consistently prepared to training data sets based on properties of the training data saved in a returned metadata database such as for use in generating predictions from the trained ML system. Returned data sets may be prepared for oversampling of labels with lower frequency occurrence. Columns of a training data set are evaluated for appropriate categories of transformations, with the composition of transformation function applications designated by a defined tree of transformation category assignments to transformation primitives. Composition of transformation trees and their associated transformation functions may optionally be custom defined by a user. |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US11861462B2</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US11861462B2</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US11861462B23</originalsourceid><addsrcrecordid>eNrjZDAOKEotSCzKzEtXKC4pKk0uKS1KTVFISSxJVChOLSlWSMsvUshNTM7IzEtVyElNLMoDquRhYE1LzClO5YXS3AyKbq4hzh66qQX58anFBYnJqXmpJfGhwYaGFmaGJmZGTkbGxKgBAB49LMo</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Preparing structured data sets for machine learning</title><source>esp@cenet</source><creator>Teague, Nicholas John</creator><creatorcontrib>Teague, Nicholas John</creatorcontrib><description>A technique for automated preparation of tabular data for machine learning, including options for machine learning derived infill, feature importance evaluations, and/or dimensionality reduction. Validation data sets may be consistently prepared to training data sets based on properties of the training data saved in a metadata database. Additional data sets may be consistently prepared to training data sets based on properties of the training data saved in a returned metadata database such as for use in generating predictions from the trained ML system. Returned data sets may be prepared for oversampling of labels with lower frequency occurrence. Columns of a training data set are evaluated for appropriate categories of transformations, with the composition of transformation function applications designated by a defined tree of transformation category assignments to transformation primitives. Composition of transformation trees and their associated transformation functions may optionally be custom defined by a user.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240102&DB=EPODOC&CC=US&NR=11861462B2$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240102&DB=EPODOC&CC=US&NR=11861462B2$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Teague, Nicholas John</creatorcontrib><title>Preparing structured data sets for machine learning</title><description>A technique for automated preparation of tabular data for machine learning, including options for machine learning derived infill, feature importance evaluations, and/or dimensionality reduction. Validation data sets may be consistently prepared to training data sets based on properties of the training data saved in a metadata database. Additional data sets may be consistently prepared to training data sets based on properties of the training data saved in a returned metadata database such as for use in generating predictions from the trained ML system. Returned data sets may be prepared for oversampling of labels with lower frequency occurrence. Columns of a training data set are evaluated for appropriate categories of transformations, with the composition of transformation function applications designated by a defined tree of transformation category assignments to transformation primitives. Composition of transformation trees and their associated transformation functions may optionally be custom defined by a user.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZDAOKEotSCzKzEtXKC4pKk0uKS1KTVFISSxJVChOLSlWSMsvUshNTM7IzEtVyElNLMoDquRhYE1LzClO5YXS3AyKbq4hzh66qQX58anFBYnJqXmpJfGhwYaGFmaGJmZGTkbGxKgBAB49LMo</recordid><startdate>20240102</startdate><enddate>20240102</enddate><creator>Teague, Nicholas John</creator><scope>EVB</scope></search><sort><creationdate>20240102</creationdate><title>Preparing structured data sets for machine learning</title><author>Teague, Nicholas John</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US11861462B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>Teague, Nicholas John</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Teague, Nicholas John</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Preparing structured data sets for machine learning</title><date>2024-01-02</date><risdate>2024</risdate><abstract>A technique for automated preparation of tabular data for machine learning, including options for machine learning derived infill, feature importance evaluations, and/or dimensionality reduction. Validation data sets may be consistently prepared to training data sets based on properties of the training data saved in a metadata database. Additional data sets may be consistently prepared to training data sets based on properties of the training data saved in a returned metadata database such as for use in generating predictions from the trained ML system. Returned data sets may be prepared for oversampling of labels with lower frequency occurrence. Columns of a training data set are evaluated for appropriate categories of transformations, with the composition of transformation function applications designated by a defined tree of transformation category assignments to transformation primitives. Composition of transformation trees and their associated transformation functions may optionally be custom defined by a user.</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | eng |
recordid | cdi_epo_espacenet_US11861462B2 |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
title | Preparing structured data sets for machine learning |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T08%3A43%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=Teague,%20Nicholas%20John&rft.date=2024-01-02&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS11861462B2%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |