Interpretable Machine Learning with Python
Learn to build interpretable high-performance models with hands-on real-world examples
Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation
methods, and leverage them to build fairer, safer, and more reliable models
Key Features
Learn how to extract easy-to-understand insights from any machine learning model
Become well-versed with interpretability techniques to build fairer, safer, and more reliable models
Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models
Book DescriptionDo you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models. Les mer
Key Features
Learn how to extract easy-to-understand insights from any machine learning model
Become well-versed with interpretability techniques to build fairer, safer, and more reliable models
Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models
Book DescriptionDo you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models. Les mer
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Leveringstid: Sendes innen 21 dager
Paperback
Legg i
Paperback
Legg i
Vår pris:
641,-
(Paperback)
Fri frakt!
Leveringstid: Sendes innen 21 dager
Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation
methods, and leverage them to build fairer, safer, and more reliable models
Key Features
Learn how to extract easy-to-understand insights from any machine learning model
Become well-versed with interpretability techniques to build fairer, safer, and more reliable models
Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models
Book DescriptionDo you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models.
The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you'll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.
By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.
What you will learn
Recognize the importance of interpretability in business
Study models that are intrinsically interpretable such as linear models, decision trees, and Naive Bayes
Become well-versed in interpreting models with model-agnostic methods
Visualize how an image classifier works and what it learns
Understand how to mitigate the influence of bias in datasets
Discover how to make models more reliable with adversarial robustness
Use monotonic constraints to make fairer and safer models
Who this book is forThis book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.
Key Features
Learn how to extract easy-to-understand insights from any machine learning model
Become well-versed with interpretability techniques to build fairer, safer, and more reliable models
Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models
Book DescriptionDo you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models.
The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you'll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.
By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.
What you will learn
Recognize the importance of interpretability in business
Study models that are intrinsically interpretable such as linear models, decision trees, and Naive Bayes
Become well-versed in interpreting models with model-agnostic methods
Visualize how an image classifier works and what it learns
Understand how to mitigate the influence of bias in datasets
Discover how to make models more reliable with adversarial robustness
Use monotonic constraints to make fairer and safer models
Who this book is forThis book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.
Table of Contents
Interpretation, Interpretability and Explainability; and why does it all matter?
Key Concepts of Interpretability
Interpretation Challenges
Fundamentals of Feature Importance and Impact
Global Model-Agnostic Interpretation Methods
Local Model-Agnostic Interpretation Methods
Anchor and Counterfactual Explanations
Visualizing Convolutional Neural Networks
Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
Feature Selection and Engineering for Interpretability
Bias Mitigation and Causal Inference Methods
Monotonic Constraints and Model Tuning for Interpretability
Adversarial Robustness
What's Next for Machine Learning Interpretability?
Interpretation, Interpretability and Explainability; and why does it all matter?
Key Concepts of Interpretability
Interpretation Challenges
Fundamentals of Feature Importance and Impact
Global Model-Agnostic Interpretation Methods
Local Model-Agnostic Interpretation Methods
Anchor and Counterfactual Explanations
Visualizing Convolutional Neural Networks
Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis
Feature Selection and Engineering for Interpretability
Bias Mitigation and Causal Inference Methods
Monotonic Constraints and Model Tuning for Interpretability
Adversarial Robustness
What's Next for Machine Learning Interpretability?
Serg Masis has been at the confluence of the internet, application development, and analytics for the last two decades. Currently,
he's a Climate and Agronomic Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food
security. Before that role, he co-founded a startup, incubated by Harvard Innovation Labs, that combined the power of cloud
computing and machine learning with principles in decision-making science to expose users to new places and events. Whether
it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing
link between data and decision-making - and machine learning interpretation helps bridge this gap more robustly.