Explanatory model analysis : explore, explain, and examine predictive models / Przemyslaw Biecek, Tomasz Burzykowski.
By: Biecek, Przemyslaw [author.].
Contributor(s): Burzykowski, Tomasz [author.].
Material type:
BookSeries: Publisher: Boca Raton : CRC Press, 2021Copyright date: ©2021Edition: First edition.Description: 1 online resource (xiii, 311 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9780429648731; 0429648731; 9780429651373; 0429651376; 9780429027192; 0429027192; 9780429646096; 0429646097.Subject(s): Mathematical models | BUSINESS & ECONOMICS / Statistics | COMPUTERS / Artificial Intelligence | COMPUTERS / Computer Vision & Pattern RecognitionDDC classification: 511/.8 Online resources: Taylor & Francis | OCLC metadata license agreement Summary: Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.
"A Chapman & Hall Book" -- title page.
1. Introduction. -- 2. Prediction Understanding. -- 3. Model Understanding. -- 4. Model Fidelity. -- 5. Other Topics.
Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.
OCLC-licensed vendor bibliographic record.
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