Data Science, Analytics and Machine Learning with R

Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning.

In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear.

《Data Science, Analytics and Machine Learning with R》解釋了數據挖掘和機器學習技術的原則，並強調應用和多變量建模的重要性。該書強調每種技術的基礎知識，並提供逐步代碼和來自醫學和健康、生物學、工程學、技術和相關科學領域的實際示例數據。示例使用最新的R語言語法，並使用廣泛且當前的包。代碼腳本有詳盡的註釋，讓讀者清楚了解每個命令的作用。對於數據收集，讀者將學習如何從頭開始建立自己的機器人。

此外，整個章節專注於空間分析的概念，讓讀者能夠通過地理參考數據（例如流行病學研究中的數據）和一些基本的統計技術來建立自己的地圖。其他章節涵蓋了集成和提升建模以及GLMM（廣義線性混合模型）估計，包括線性和非線性模型。