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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing Ltd,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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 to implement a wide range of tasks and solve ... |
| Beschreibung: | Technical requirements |
| Beschreibung: | 1 online resource (327 pages) |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 1789132509 9781789132502 |
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| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 500 | |a Technical requirements | ||
| 520 | |a 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 to implement a wide range of tasks and solve ... | ||
| 504 | |a Includes bibliographical references. | ||
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
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| 776 | 0 | 8 | |i Print version: |a Sarkar, Dipayan. |t Ensemble Machine Learning Cookbook : Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python. |d Birmingham : Packt Publishing Ltd, ©2019 |z 9781789136609 |
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| contents | 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 |
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| publisher | Packt Publishing Ltd, |
| record_format | marc |
| spelling | Sarkar, Dipayan. Ensemble Machine Learning Cookbook : Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python. Birmingham : Packt Publishing Ltd, 2019. 1 online resource (327 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. 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 Technical requirements 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 to implement a wide range of tasks and solve ... Includes bibliographical references. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage automatique. Python (Langage de programmation) Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc COMPUTERS General. bisacsh Machine learning fast Python (Computer program language) fast Natarajan, Vijayalakshmi. Print version: Sarkar, Dipayan. Ensemble Machine Learning Cookbook : Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python. Birmingham : Packt Publishing Ltd, ©2019 9781789136609 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2016357 Volltext |
| spellingShingle | Sarkar, Dipayan Ensemble Machine Learning Cookbook : Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python. 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 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage automatique. Python (Langage de programmation) Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc COMPUTERS General. bisacsh Machine learning fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh96008834 |
| title | Ensemble Machine Learning Cookbook : Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python. |
| title_auth | Ensemble Machine Learning Cookbook : Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python. |
| title_exact_search | Ensemble Machine Learning Cookbook : Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python. |
| title_full | Ensemble Machine Learning Cookbook : Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python. |
| title_fullStr | Ensemble Machine Learning Cookbook : Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python. |
| title_full_unstemmed | Ensemble Machine Learning Cookbook : Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python. |
| title_short | Ensemble Machine Learning Cookbook : |
| title_sort | ensemble machine learning cookbook over 35 practical recipes to explore ensemble machine learning techniques using python |
| title_sub | Over 35 Practical Recipes to Explore Ensemble Machine Learning Techniques Using Python. |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Apprentissage automatique. Python (Langage de programmation) Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc COMPUTERS General. bisacsh Machine learning fast Python (Computer program language) fast |
| topic_facet | Machine learning. Python (Computer program language) Apprentissage automatique. Python (Langage de programmation) Natural language & machine translation. Neural networks & fuzzy systems. Artificial intelligence. COMPUTERS General. Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2016357 |
| work_keys_str_mv | AT sarkardipayan ensemblemachinelearningcookbookover35practicalrecipestoexploreensemblemachinelearningtechniquesusingpython AT natarajanvijayalakshmi ensemblemachinelearningcookbookover35practicalrecipestoexploreensemblemachinelearningtechniquesusingpython |