Learn Bagging, Stacking, and Boosting Methods with Use Cases
E-Book, Englisch, 136 Seiten, eBook
ISBN: 978-1-4842-5940-5
Verlag: APRESS
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)
Use ensemble learning techniques and models to improve your machine learning results.
Ensemble Learning for AI Developers
starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook.
What You Will LearnUnderstand the techniques and methods utilized in ensemble learningUse bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias
Enhance your machine learning architecture with ensemble learning
Who This Book Is ForData scientists and machine learning engineers keen on exploring ensemble learning
Kumar / Jain
Ensemble Learning for AI Developers jetzt bestellen!
Ensemble Learning for AI Developers
starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook.
What You Will LearnUnderstand the techniques and methods utilized in ensemble learningUse bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias
Enhance your machine learning architecture with ensemble learning
Who This Book Is ForData scientists and machine learning engineers keen on exploring ensemble learning
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Weitere Infos & Material
Chapter 1: Why Ensemble Techniques Are Needed.- Chapter 2: Mix Training Data.- Chapter 3: Mix Models.- Chapter 4: Mix Combinations.- Chapter 5: Use Ensemble Learning Libraries.- Chapter 6: Tips and Best Practices.
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