Hands-On Ensemble Learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

Prabhanjan Narayanachar Tattar

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商品描述

Explore powerful R packages to create predictive models using ensemble methods

Key Features

  • Implement machine learning algorithms to build ensemble-efficient models
  • Explore powerful R packages to create predictive models using ensemble methods
  • Learn to build ensemble models on large datasets using a practical approach

Book Description

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.

What you will learn

  • Carry out an essential review of re-sampling methods, bootstrap, and jackknife
  • Explore the key ensemble methods: bagging, random forests, and boosting
  • Use multiple algorithms to make strong predictive models
  • Enjoy a comprehensive treatment of boosting methods
  • Supplement methods with statistical tests, such as ROC
  • Walk through data structures in classification, regression, survival, and time series data
  • Use the supplied R code to implement ensemble methods
  • Learn stacking method to combine heterogeneous machine learning models

Who this book is for

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. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.

Table of Contents

  1. Introduction to Ensemble Techniques
  2. Bootstrapping
  3. Bagging
  4. Random Forests
  5. The Bare Bones Boosting Algorithms
  6. Boosting Refinements
  7. The General Ensemble Technique
  8. Ensemble Diagnostics
  9. Ensembling Regression Models
  10. Ensembling Survival Models
  11. Ensembling Time Series Models
  12. What's Next?

商品描述(中文翻譯)

探索強大的 R 套件,使用集成方法創建預測模型

主要特點



  • 實施機器學習算法,構建高效的集成模型

  • 探索強大的 R 套件,使用集成方法創建預測模型

  • 學習使用實用方法在大數據集上構建集成模型


書籍描述


集成技術用於結合兩個或多個相似或不相似的機器學習算法,以創建更強大的模型。這樣的模型具有更高的預測能力,可以提高數據集的準確性。


《使用 R 進行實踐集成學習》從重要的統計重抽樣方法開始。然後,您將深入研究集成技術的核心三部曲 - bagging、random forest 和 boosting - 然後學習如何使用流行的 R 套件在大數據集上提供更高的準確性。您將學習如何使用不同的機器學習算法結合模型預測,以構建集成模型。此外,您還將探索如何提高集成模型的性能。


通過閱讀本書,您將學習如何結合機器學習算法以減少常見問題,並通過真實世界的示例構建簡單高效的集成模型。


您將學到什麼



  • 進行重要的重抽樣方法、自助法和杰克刀法的評估

  • 探索關鍵的集成方法:bagging、random forests 和 boosting

  • 使用多個算法構建強大的預測模型

  • 全面介紹 boosting 方法

  • 使用統計測試(如 ROC)補充方法

  • 深入研究分類、回歸、生存和時間序列數據的數據結構

  • 使用提供的 R 代碼實施集成方法

  • 學習堆疊方法以結合異質機器學習模型


適合閱讀對象


本書適合數據科學家或機器學習開發人員,希望通過使用 R 建立集成模型來實施機器學習技術。您將學習如何結合不同的機器學習算法進行高效的數據處理。具備機器學習技術的基本知識和 R 編程知識將是一個額外的優勢。


目錄



  1. 集成技術簡介

  2. 自助法

  3. Bagging

  4. 隨機森林

  5. 基本的 boosting 算法

  6. Boosting 的改進

  7. 通用集成技術

  8. 集成診斷

  9. 集成回歸模型

  10. 集成生存模型

  11. 集成時間序列模型

  12. 下一步是什麼?