Interpretable machine learning with Python: build explainable, fair, and robust high-performance models with hands-on, real-world examples
Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Weitere beteiligte Personen: | , |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing Ltd.
2023
|
| Ausgabe: | Second edition. |
| Schriftenreihe: | Expert insight
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781803235424/?ar |
| Zusammenfassung: | Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data. This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples. |
| Beschreibung: | Includes bibliographical references and index |
| Umfang: | 1 Online-Ressource (606 Seiten) illustrations |
| ISBN: | 9781803235424 |
Internformat
MARC
| LEADER | 00000cam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-098491660 | ||
| 003 | DE-627-1 | ||
| 005 | 20240228122102.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231127s2023 xx |||||o 00| ||eng c | ||
| 020 | |a 9781803235424 |9 978-1-80323-542-4 | ||
| 035 | |a (DE-627-1)098491660 | ||
| 035 | |a (DE-599)KEP098491660 | ||
| 035 | |a (ORHE)9781803235424 | ||
| 035 | |a (DE-627-1)098491660 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 082 | 0 | |a 005.13/3 |2 23/eng/20231107 | |
| 100 | 1 | |a Masís, Serg |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Interpretable machine learning with Python |b build explainable, fair, and robust high-performance models with hands-on, real-world examples |c Serg Masís ; forewords by Aleksander Molak, Denis Rothman |
| 250 | |a Second edition. | ||
| 264 | 1 | |a Birmingham, UK |b Packt Publishing Ltd. |c 2023 | |
| 300 | |a 1 Online-Ressource (606 Seiten) |b illustrations | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Expert insight | |
| 500 | |a Includes bibliographical references and index | ||
| 520 | |a Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data. This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples. | ||
| 650 | 0 | |a Machine learning | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Data mining | |
| 650 | 4 | |a Apprentissage automatique | |
| 650 | 4 | |a Python (Langage de programmation) | |
| 650 | 4 | |a Exploration de données (Informatique) | |
| 700 | 1 | |a Molak, Aleksander |e MitwirkendeR |4 ctb | |
| 700 | 1 | |a Rothman, Denis |e MitwirkendeR |4 ctb | |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781803235424/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-098491660 |
|---|---|
| _version_ | 1821494936586944512 |
| adam_text | |
| any_adam_object | |
| author | Masís, Serg |
| author2 | Molak, Aleksander Rothman, Denis |
| author2_role | ctb ctb |
| author2_variant | a m am d r dr |
| author_facet | Masís, Serg Molak, Aleksander Rothman, Denis |
| author_role | aut |
| author_sort | Masís, Serg |
| author_variant | s m sm |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)098491660 (DE-599)KEP098491660 (ORHE)9781803235424 |
| dewey-full | 005.13/3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.13/3 |
| dewey-search | 005.13/3 |
| dewey-sort | 15.13 13 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | Second edition. |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03244cam a22004452 4500</leader><controlfield tag="001">ZDB-30-ORH-098491660</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228122102.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231127s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781803235424</subfield><subfield code="9">978-1-80323-542-4</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)098491660</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP098491660</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781803235424</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)098491660</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">005.13/3</subfield><subfield code="2">23/eng/20231107</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Masís, Serg</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Interpretable machine learning with Python</subfield><subfield code="b">build explainable, fair, and robust high-performance models with hands-on, real-world examples</subfield><subfield code="c">Serg Masís ; forewords by Aleksander Molak, Denis Rothman</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Second edition.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham, UK</subfield><subfield code="b">Packt Publishing Ltd.</subfield><subfield code="c">2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (606 Seiten)</subfield><subfield code="b">illustrations</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Expert insight</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data. This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Python (Computer program language)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Data mining</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage automatique</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Python (Langage de programmation)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Exploration de données (Informatique)</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Molak, Aleksander</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rothman, Denis</subfield><subfield code="e">MitwirkendeR</subfield><subfield code="4">ctb</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781803235424/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-098491660 |
| illustrated | Illustrated |
| indexdate | 2025-01-17T11:22:18Z |
| institution | BVB |
| isbn | 9781803235424 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (606 Seiten) illustrations |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Packt Publishing Ltd. |
| record_format | marc |
| series2 | Expert insight |
| spelling | Masís, Serg VerfasserIn aut Interpretable machine learning with Python build explainable, fair, and robust high-performance models with hands-on, real-world examples Serg Masís ; forewords by Aleksander Molak, Denis Rothman Second edition. Birmingham, UK Packt Publishing Ltd. 2023 1 Online-Ressource (606 Seiten) illustrations Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Expert insight Includes bibliographical references and index Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models. Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps. In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data. This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples. Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) Molak, Aleksander MitwirkendeR ctb Rothman, Denis MitwirkendeR ctb |
| spellingShingle | Masís, Serg Interpretable machine learning with Python build explainable, fair, and robust high-performance models with hands-on, real-world examples Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) |
| title | Interpretable machine learning with Python build explainable, fair, and robust high-performance models with hands-on, real-world examples |
| title_auth | Interpretable machine learning with Python build explainable, fair, and robust high-performance models with hands-on, real-world examples |
| title_exact_search | Interpretable machine learning with Python build explainable, fair, and robust high-performance models with hands-on, real-world examples |
| title_full | Interpretable machine learning with Python build explainable, fair, and robust high-performance models with hands-on, real-world examples Serg Masís ; forewords by Aleksander Molak, Denis Rothman |
| title_fullStr | Interpretable machine learning with Python build explainable, fair, and robust high-performance models with hands-on, real-world examples Serg Masís ; forewords by Aleksander Molak, Denis Rothman |
| title_full_unstemmed | Interpretable machine learning with Python build explainable, fair, and robust high-performance models with hands-on, real-world examples Serg Masís ; forewords by Aleksander Molak, Denis Rothman |
| title_short | Interpretable machine learning with Python |
| title_sort | interpretable machine learning with python build explainable fair and robust high performance models with hands on real world examples |
| title_sub | build explainable, fair, and robust high-performance models with hands-on, real-world examples |
| topic | Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) |
| topic_facet | Machine learning Python (Computer program language) Data mining Apprentissage automatique Python (Langage de programmation) Exploration de données (Informatique) |
| work_keys_str_mv | AT masisserg interpretablemachinelearningwithpythonbuildexplainablefairandrobusthighperformancemodelswithhandsonrealworldexamples AT molakaleksander interpretablemachinelearningwithpythonbuildexplainablefairandrobusthighperformancemodelswithhandsonrealworldexamples AT rothmandenis interpretablemachinelearningwithpythonbuildexplainablefairandrobusthighperformancemodelswithhandsonrealworldexamples |