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Ensemble Machine Learning Cookbook : Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python.
This book uses a recipe-based approach to showcase the power of machine learning algorithms to build ensemble models using Python libraries. Through this book, you will be able to pick up the code, understand in depth how it works, execute and implement it efficiently. This will be a desk reference...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Title Page; Copyright and Credits; About Packt; Foreword; Contributors; Preface; Table of Contents; Chapter 1: Get Closer to Your Data; Introduction; Data manipulation with Python; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Analyzing, visualizing, and treating missing values; How to do it ... ; How it works ... ; There's more ... ; See also; Exploratory data analysis; How to do it ... ; How it works ... ; There's more ... ; See also; Chapter 2: Getting Started with Ensemble Machine Learning; Introduction to ensemble machine learning; Max-voting; Getting ready
- How to do it ... How it works ... ; There's more ... ; Averaging; Getting ready; How to do it ... ; How it works ... ; Weighted averaging; Getting ready; How to do it ... ; How it works ... ; See also; Chapter 3: Resampling Methods; Introduction to sampling; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; k-fold and leave-one-out cross-validation; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Bootstrapping; Getting ready; How to do it ... ; How it works ... ; See also; Chapter 4: Statistical and Machine Learning Algorithms; Technical requirements
- Multiple linear regressionGetting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Logistic regression; Getting ready; How to do it ... ; How it works ... ; See also; Naive Bayes; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Decision trees; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Support vector machines; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Chapter 5: Bag the Models with Bagging; Introduction; Bootstrap aggregation; Getting ready; How to do it ... ; How it works ... ; See also
- Ensemble meta-estimatorsBagging classifiers; How to do it ... ; How it works ... ; There's more ... ; See also; Bagging regressors; Getting ready; How to do it ... ; How it works ... ; See also; Chapter 6: When in Doubt, Use Random Forests; Introduction to random forests; Implementing a random forest for predicting credit card defaults using scikit-learn; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Implementing random forest for predicting credit card defaults using H2O; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also
- Chapter 7: Boosting Model Performance with BoostingIntroduction to boosting; Implementing AdaBoost for disease risk prediction using scikit-learn; Getting ready; How to do it ... ; How it works ... ; There's more ... ; See also; Implementing a gradient boosting machine for disease risk prediction using scikit-learn; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Implementing the extreme gradient boosting method for glass identification using XGBoost with scikit-learn ; Getting ready ... ; How to do it ... ; How it works ... ; There's more ... ; See also; Chapter 8: Blend It with Stacking