Machine Learning Essentials: Practical Guide in R
暫譯: 機器學習基礎：R 語言實用指南
Alboukadel Kassambara
- 出版商: W. W. Norton
- 出版日期: 2018-03-10
- 售價: $2,010
- 貴賓價: 9.5 折 $1,910
- 語言: 英文
- 頁數: 210
- 裝訂: Paperback
- ISBN: 1986406857
- ISBN-13: 9781986406857
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相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
商品描述
Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques.
This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models.
The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers.
Key features:
This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models.
The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers.
Key features:
- Covers machine learning algorithm and implementation
- Key mathematical concepts are presented
- Short, self-contained chapters with practical examples.
商品描述(中文翻譯)
從每天記錄的大型多變量數據中發現知識，需要專門的機器學習技術。
本書提供了一本易於使用的實用指南，使用 R 語言計算最受歡迎的機器學習方法，以探索現實世界的數據集，以及建立預測模型。
本書的主要部分包括：A) 無監督學習方法，使用聚類和主成分方法探索和發現大型多變量數據集中的知識。您將學習層次聚類、k-means、主成分分析和對應分析方法。B) 回歸分析，使用線性回歸和非線性回歸策略預測定量結果值。C) 分類技術，使用邏輯回歸、判別分析、朴素貝葉斯分類器和支持向量機預測定性結果值。D) 進階機器學習方法，使用 k 最近鄰方法、決策樹模型、集成方法（如 bagging、隨機森林和提升）建立穩健的回歸和分類模型。E) 模型選擇方法，自動選擇最佳的預測變數組合以建立最佳預測模型。這些方法包括最佳子集選擇方法、逐步回歸和懲罰回歸（如 ridge、lasso 和彈性網回歸模型）。我們還介紹了基於主成分的回歸方法，這在數據包含多個相關的預測變數時非常有用。F) 模型驗證和評估技術，用於測量預測模型的性能。G) 模型診斷，用於檢測和修正預測模型中的潛在問題。本書介紹了這些任務的基本原則，並提供了許多 R 語言的範例。本書為學生和研究人員提供了數據挖掘的堅實指導。
主要特點：
- 涵蓋機器學習算法和實現
- 介紹關鍵數學概念
- 短小、獨立的章節，附有實用範例。
