國立虎尾科技大學 |

登入

回首頁

Interpretable machine learning with Python : = build explainable, fair, and robust high-performance models with hands-on, real-world examples /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Interpretable machine learning with Python :/ Serg Masís ; forewords by Aleksander Molak, Denis Rothman.
其他題名:	build explainable, fair, and robust high-performance models with hands-on, real-world examples /
其他題名:	Build explainable, fair, and robust high-performance models with hands-on, real-world examples
作者:	Masís, Serg.
其他作者:	Rothman, Denis.
出版者:	Birmingham, UK :Packt Publishing Ltd., : c2023.,
面頁冊數:	xxvii, 576 p. :ill. ; : 24 cm.;
標題:	Data mining. -
ISBN:	9781803235424 (pbk.) :

Interpretable machine learning with Python : = build explainable, fair, and robust high-performance models with hands-on, real-world examples /
Masís, Serg.

Interpretable machine learning with Python :build explainable, fair, and robust high-performance models with hands-on, real-world examples /Build explainable, fair, and robust high-performance models with hands-on, real-world examplesSerg Masís ; forewords by Aleksander Molak, Denis Rothman. - 2nd ed. - Birmingham, UK :Packt Publishing Ltd.,c2023. - xxvii, 576 p. :ill. ;24 cm. - Expert insight. - 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.

ISBN: 9781803235424 (pbk.) :NT1629Subjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.73.P98

Dewey Class. No.: 005.13/3 / M397

Interpretable machine learning with Python : = build explainable, fair, and robust high-performance models with hands-on, real-world examples /
LDR:02409cam a2200193 a 4500 001 1136168
008 241218s2023 enka b 001 0 eng d
020 $a 9781803235424 (pbk.) : $c NT1629
035 $a (OCoLC)1407573405
040 $a TL $b eng $c TL $d NFU $e AACR2
050 # 4 $a QA76.73.P98
082 0 4 $a 005.13/3 $b M397 $2 23
100 1 $a Masís, Serg. $3 1458379
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.
246 3 0 $a Build explainable, fair, and robust high-performance models with hands-on, real-world examples
250 $a 2nd ed.
260 # $a Birmingham, UK : $b Packt Publishing Ltd., $c c2023.
300 $a xxvii, 576 p. : $b ill. ; $c 24 cm.
490 1 $a Expert insight
504 $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 Data mining. $3 528622
650 # 0 $a Python (Computer program language) $3 566246
650 # 0 $a Machine learning. $3 561253
700 1 # $a Rothman, Denis. $3 1458381
700 1 # $a Molak, Aleksander. $3 1458380
830 0 $a Expert insight. $3 1406965