Hands-On Ensemble Learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
bExplore powerful R packages to create predictive models using ensemble methods/b h4Key Features/h4 ulliImplement machine learning algorithms to build ensemble-efficient models /li liExplore powerful R packages to create predictive models using ensemble methods /li liLearn to build ensemble models o...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2018
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| Ausgabe: | 1 |
| Schlagworte: | |
| Online-Zugang: | UBY01 |
| Zusammenfassung: | bExplore powerful R packages to create predictive models using ensemble methods/b h4Key Features/h4 ulliImplement machine learning algorithms to build ensemble-efficient models /li liExplore powerful R packages to create predictive models using ensemble methods /li liLearn to build ensemble models on large datasets using a practical approach /li /ul h4Book Description/h4 Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques - bagging, random forest, and boosting - then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples. h4What you will learn/h4 ulliCarry out an essential review of re-sampling methods, bootstrap, and jackknife /li liExplore the key ensemble methods: bagging, random forests, and boosting /li liUse multiple algorithms to make strong predictive models /li liEnjoy a comprehensive treatment of boosting methods /li liSupplement methods with statistical tests, such as ROC /li liWalk through data structures in classification, regression, survival, and time series data /li liUse the supplied R code to implement ensemble methods /li liLearn stacking method to combine heterogeneous machine learning models /li /ul h4Who this book is for/h4 This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. |
| Beschreibung: | 1 Online-Ressource (376 Seiten) |
| ISBN: | 9781788629171 |
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| 520 | |a You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples. | ||
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| spelling | Tattar, Prabhanjan Narayanachar 1979- Verfasser (DE-588)1101846275 aut Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques Tattar, Prabhanjan Narayanachar 1 Birmingham Packt Publishing Limited 2018 1 Online-Ressource (376 Seiten) txt rdacontent c rdamedia cr rdacarrier bExplore powerful R packages to create predictive models using ensemble methods/b h4Key Features/h4 ulliImplement machine learning algorithms to build ensemble-efficient models /li liExplore powerful R packages to create predictive models using ensemble methods /li liLearn to build ensemble models on large datasets using a practical approach /li /ul h4Book Description/h4 Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy. Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques - bagging, random forest, and boosting - then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models. By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples. h4What you will learn/h4 ulliCarry out an essential review of re-sampling methods, bootstrap, and jackknife /li liExplore the key ensemble methods: bagging, random forests, and boosting /li liUse multiple algorithms to make strong predictive models /li liEnjoy a comprehensive treatment of boosting methods /li liSupplement methods with statistical tests, such as ROC /li liWalk through data structures in classification, regression, survival, and time series data /li liUse the supplied R code to implement ensemble methods /li liLearn stacking method to combine heterogeneous machine learning models /li /ul h4Who this book is for/h4 This book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. COMPUTERS / Data Processing COMPUTERS / Machine Theory |
| spellingShingle | Tattar, Prabhanjan Narayanachar 1979- Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques COMPUTERS / Data Processing COMPUTERS / Machine Theory |
| title | Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques |
| title_auth | Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques |
| title_exact_search | Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques |
| title_exact_search_txtP | Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques |
| title_full | Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques Tattar, Prabhanjan Narayanachar |
| title_fullStr | Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques Tattar, Prabhanjan Narayanachar |
| title_full_unstemmed | Hands-On Ensemble Learning with R A beginner's guide to combining the power of machine learning algorithms using ensemble techniques Tattar, Prabhanjan Narayanachar |
| title_short | Hands-On Ensemble Learning with R |
| title_sort | hands on ensemble learning with r a beginner s guide to combining the power of machine learning algorithms using ensemble techniques |
| title_sub | A beginner's guide to combining the power of machine learning algorithms using ensemble techniques |
| topic | COMPUTERS / Data Processing COMPUTERS / Machine Theory |
| topic_facet | COMPUTERS / Data Processing COMPUTERS / Machine Theory |
| work_keys_str_mv | AT tattarprabhanjannarayanachar handsonensemblelearningwithrabeginnersguidetocombiningthepowerofmachinelearningalgorithmsusingensembletechniques |