Interpretable Machine Learning with Python

Name: Interpretable Machine Learning with Python
Brand: Packt Publishing
SKU: 1657062

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1681565Interpretable Machine Learning with Pythonhttps://www.gandhi.com.mx/interpretable-machine-learning-with-python-second-edition/phttps://gandhi.vtexassets.com/arquivos/ids/4051520/c6bc2d0c-6ad5-4dee-8bb2-3ae6474a8bd0.jpg?v=638429566842200000739821MXNPackt PublishingInStock/Ebooks/1657062Interpretable Machine Learning with Python739821https://www.gandhi.com.mx/interpretable-machine-learning-with-python-second-edition/phttps://gandhi.vtexassets.com/arquivos/ids/4051520/c6bc2d0c-6ad5-4dee-8bb2-3ae6474a8bd0.jpg?v=638429566842200000InStockMXN99999DIEbook20239781803243627_W3siaWQiOiI5OGJjNjY4OS0zZDBlLTRjMmMtOTc4ZS1jZjZlNWUyMzEyOTkiLCJsaXN0UHJpY2UiOjgyMSwiZGlzY291bnQiOjgyLCJzZWxsaW5nUHJpY2UiOjczOSwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDQtMDhUMTY6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781803243627_<p><strong>A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models</strong></p><h4>Key Features</h4><ul><li>Extract easy-to-understand insights from any machine learning model</li><li>Become well-versed with interpretability techniques to build fairer, safer, and more reliable models</li><li>Lift the lid on the black box of transformer NLP models to improve your deep learning understanding</li></ul><h4>Book Description</h4><p>Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python, Second Edition is the book for you.</p><p>Youll cover the fundamentals of interpretability, its relevance in business, and explore its key aspects and challenges.</p><p>See how white-box models work, compare them to black-box and glass-box models, and examine their trade-offs. Get 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, tabular data, time-series, images, or text.</p><p>In addition to the step-by-step code, this book will also help you interpret model outcomes using many examples. Youll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods youll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. Youll also look under the hood of the latest NLP transformer models using the Language Interpretability Tool.</p><p>By the end of this book, youll understand ML models better and enhance them through interpretability tuning.</p><h4>What you will learn</h4><ul><li>Recognize the importance of interpretability in business</li><li>Study models that are intrinsically interpretable such as linear models, decision trees, Naive Bayes, and glass-box models, such as EBM and Gami-NET</li><li>Become well-versed in interpreting black-box models with model-agnostic methods</li><li>Use monotonic and interaction constraints to make fairer and safer models</li><li>Understand how to mitigate the influence of bias in datasets</li><li>Discover how to make models more reliable with adversarial robustness</li><li>Understand how transformer models work and how to interpret them</li></ul><h4>Who This Book Is For</h4><p>This book is for data scientists, machine learning developers, MLOps engineers, 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 Its 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.</p><h4>Table of Contents</h4><ol><li>Interpretation, Interpretability and Explainability; and why does it all matter?</li><li>Key Concepts of Interpretability</li><li>Interpretation Challenges</li><li>Global Model-agnostic Interpretation Methods</li><li>Local Model-agnostic Interpretation Methods</li><li>Anchor and Counterfactual Explanations</li><li>Visualizing Convolutional Neural Networks</li><li>Understanding NLP Transformers</li><li>Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis</li><li>Feature Selection and Engineering for Interpretability</li><li>Bias Mitigation and Causal Inference Methods</li><li>Feature Selection for Interpretability</li><li>(N.B. Additional chapters to be confirmed upon publication</li></ol>...(*_*)9781803243627_<p><b>A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models. Purchase of the print or Kindle book includes a free eBook in PDF format.</b></p><h2>Key Features</h2><ul><li>Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores</li><li>Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods</li><li>Analyze and extract insights from complex models from CNNs to BERT to time series models</li></ul><h2>Book Description</h2>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, youll 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, youll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.<h2>What you will learn</h2><ul><li>Progress from basic to advanced techniques, such as causal inference and quantifying uncertainty</li><li>Build your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformers</li><li>Use monotonic and interaction constraints to make fairer and safer models</li><li>Understand how to mitigate the influence of bias in datasets</li><li>Leverage sensitivity analysis factor prioritization and factor fixing for any model</li><li>Discover how to make models more reliable with adversarial robustness</li></ul><h2>Who this book is for</h2><p>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. Its 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.</p>...9781803243627_Packt Publishinglibro_electonico_3c2a403e-878f-39b7-bd93-23a331c41242_9781803243627;9781803243627_9781803243627Serg MasísInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/packt-epub-a48f4296-8621-4a24-9fd8-0f1ca0c91e85.epub2023-10-31T00:00:00+00:00Packt Publishing