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Books > Computing & IT > Applications of computing > Artificial intelligence > Knowledge-based systems / expert systems
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Interpretable Machine Learning with Python - Learn to build interpretable high-performance models with hands-on real-world examples (Paperback)
Loot Price: R1,544
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Interpretable Machine Learning with Python - Learn to build interpretable high-performance models with hands-on real-world examples (Paperback)
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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.
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