Machine learning evaluation: towards reliable and responsible AI
As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to pro...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2025
|
| Schlagworte: | |
| Online-Zugang: | DE-12 DE-634 DE-92 Volltext |
| Zusammenfassung: | As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and scikit-learn are available on the book's website |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 07 Nov 2024) |
| Beschreibung: | 1 Online-Ressource (xviii, 407 Seiten) |
| ISBN: | 9781009003872 |
| DOI: | 10.1017/9781009003872 |
Internformat
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV050097825 | ||
| 003 | DE-604 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 241216s2025 xx o|||| 00||| eng d | ||
| 020 | |a 9781009003872 |c Online |9 978-1-009-00387-2 | ||
| 024 | 7 | |a 10.1017/9781009003872 |2 doi | |
| 035 | |a (ZDB-20-CBO)CR9781009003872 | ||
| 035 | |a (DE-599)BVBBV050097825 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-12 |a DE-92 |a DE-634 | ||
| 082 | 0 | |a 006.31 | |
| 100 | 1 | |a Japkowicz, Nathalie |0 (DE-588)14398425X |4 aut | |
| 245 | 1 | 0 | |a Machine learning evaluation |b towards reliable and responsible AI |c Nathalie Japkowicz, Zois Boukouvalas |
| 264 | 1 | |a Cambridge |b Cambridge University Press |c 2025 | |
| 300 | |a 1 Online-Ressource (xviii, 407 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a Title from publisher's bibliographic system (viewed on 07 Nov 2024) | ||
| 520 | |a As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and scikit-learn are available on the book's website | ||
| 650 | 4 | |a Machine learning / Evaluation | |
| 650 | 4 | |a Artificial intelligence / Moral and ethical aspects | |
| 700 | 1 | |a Boukouvalas, Zois |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781316518861 |
| 856 | 4 | 0 | |u https://doi.org/10.1017/9781009003872?locatt=mode:legacy |x Verlag |z URL des Erstveröffentlichers |3 Volltext |
| 912 | |a ZDB-20-CBO | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035435018 | |
| 966 | e | |u https://doi.org/10.1017/9781009003872?locatt=mode:legacy |l DE-12 |p ZDB-20-CBO |q BSB_PDA_CBO |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/9781009003872?locatt=mode:legacy |l DE-634 |p ZDB-20-CBO |q BTU_PDA_CBO |x Verlag |3 Volltext | |
| 966 | e | |u https://doi.org/10.1017/9781009003872?locatt=mode:legacy |l DE-92 |p ZDB-20-CBO |q FHN_PDA_CBO |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1818609576949317632 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Japkowicz, Nathalie Boukouvalas, Zois |
| author_GND | (DE-588)14398425X |
| author_facet | Japkowicz, Nathalie Boukouvalas, Zois |
| author_role | aut aut |
| author_sort | Japkowicz, Nathalie |
| author_variant | n j nj z b zb |
| building | Verbundindex |
| bvnumber | BV050097825 |
| collection | ZDB-20-CBO |
| ctrlnum | (ZDB-20-CBO)CR9781009003872 (DE-599)BVBBV050097825 |
| dewey-full | 006.31 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.31 |
| dewey-search | 006.31 |
| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1017/9781009003872 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000zc 4500</leader><controlfield tag="001">BV050097825</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">241216s2025 xx o|||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781009003872</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-009-00387-2</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1017/9781009003872</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-20-CBO)CR9781009003872</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV050097825</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-12</subfield><subfield code="a">DE-92</subfield><subfield code="a">DE-634</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">006.31</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Japkowicz, Nathalie</subfield><subfield code="0">(DE-588)14398425X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine learning evaluation</subfield><subfield code="b">towards reliable and responsible AI</subfield><subfield code="c">Nathalie Japkowicz, Zois Boukouvalas</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cambridge</subfield><subfield code="b">Cambridge University Press</subfield><subfield code="c">2025</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xviii, 407 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Title from publisher's bibliographic system (viewed on 07 Nov 2024)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and scikit-learn are available on the book's website</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning / Evaluation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence / Moral and ethical aspects</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Boukouvalas, Zois</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781316518861</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1017/9781009003872?locatt=mode:legacy</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-20-CBO</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-035435018</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009003872?locatt=mode:legacy</subfield><subfield code="l">DE-12</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">BSB_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009003872?locatt=mode:legacy</subfield><subfield code="l">DE-634</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">BTU_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1017/9781009003872?locatt=mode:legacy</subfield><subfield code="l">DE-92</subfield><subfield code="p">ZDB-20-CBO</subfield><subfield code="q">FHN_PDA_CBO</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV050097825 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-16T15:00:45Z |
| institution | BVB |
| isbn | 9781009003872 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035435018 |
| open_access_boolean | |
| owner | DE-12 DE-92 DE-634 |
| owner_facet | DE-12 DE-92 DE-634 |
| physical | 1 Online-Ressource (xviii, 407 Seiten) |
| psigel | ZDB-20-CBO ZDB-20-CBO BSB_PDA_CBO ZDB-20-CBO BTU_PDA_CBO ZDB-20-CBO FHN_PDA_CBO |
| publishDate | 2025 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Cambridge University Press |
| record_format | marc |
| spelling | Japkowicz, Nathalie (DE-588)14398425X aut Machine learning evaluation towards reliable and responsible AI Nathalie Japkowicz, Zois Boukouvalas Cambridge Cambridge University Press 2025 1 Online-Ressource (xviii, 407 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 07 Nov 2024) As machine learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable machine learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and scikit-learn are available on the book's website Machine learning / Evaluation Artificial intelligence / Moral and ethical aspects Boukouvalas, Zois aut Erscheint auch als Druck-Ausgabe 9781316518861 https://doi.org/10.1017/9781009003872?locatt=mode:legacy Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Japkowicz, Nathalie Boukouvalas, Zois Machine learning evaluation towards reliable and responsible AI Machine learning / Evaluation Artificial intelligence / Moral and ethical aspects |
| title | Machine learning evaluation towards reliable and responsible AI |
| title_auth | Machine learning evaluation towards reliable and responsible AI |
| title_exact_search | Machine learning evaluation towards reliable and responsible AI |
| title_full | Machine learning evaluation towards reliable and responsible AI Nathalie Japkowicz, Zois Boukouvalas |
| title_fullStr | Machine learning evaluation towards reliable and responsible AI Nathalie Japkowicz, Zois Boukouvalas |
| title_full_unstemmed | Machine learning evaluation towards reliable and responsible AI Nathalie Japkowicz, Zois Boukouvalas |
| title_short | Machine learning evaluation |
| title_sort | machine learning evaluation towards reliable and responsible ai |
| title_sub | towards reliable and responsible AI |
| topic | Machine learning / Evaluation Artificial intelligence / Moral and ethical aspects |
| topic_facet | Machine learning / Evaluation Artificial intelligence / Moral and ethical aspects |
| url | https://doi.org/10.1017/9781009003872?locatt=mode:legacy |
| work_keys_str_mv | AT japkowicznathalie machinelearningevaluationtowardsreliableandresponsibleai AT boukouvalaszois machinelearningevaluationtowardsreliableandresponsibleai |