Machine Learning Toolbox for Social Scientists: Applied Predictive Analytics with R

Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical "tools" that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in "econometrics" textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targeted at students and researchers who have no advanced statistical background, but instead coming from the tradition of "inferential statistics". The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields.

Key Features:

• The book is structured for those who have been trained in a traditional statistics curriculum.
• There is one long initial section that covers the differences in "estimation" and "prediction" for people trained for causal analysis.
• The book develops a background framework for Machine learning applications from Nonparametric methods.
• SVM and NN simple enough without too much detail. It's self-sufficient.
• Nonparametric time-series predictions are new and covered in a separate section.
• Additional sections are added: Penalized Regressions, Dimension Reduction Methods, and Graphical Methods have been increasing in their popularity in social sciences.

《社會科學家的機器學習工具箱》涵蓋了具有互補統計“工具”的預測方法，使其在很大程度上成為一本自成一體的書籍。推論統計是社會科學和商業領域（尤其是經濟學和金融學）大多數數據分析課程的傳統框架。本書提供的新組織超越了標準的機器學習代碼應用，為社會科學和商業學生提供了可以遵循的新預測方法的直觀背景。本書還增加了許多現代統計工具，這些工具與預測方法相輔相成，在“計量經濟學”教科書中很難找到：非參數方法、預測模型的數據探索、罰則回歸、稀疏模型選擇、降維方法、非參數時間序列預測、圖形網絡分析、算法優化方法、不平衡數據的分類等等。本書針對沒有高級統計背景，而是來自“推論統計”傳統的學生和研究人員。本書提供的現代統計方法使其在社會科學和商業領域的教學中能夠有效使用。

主要特點：
- 本書的結構適合接受傳統統計課程培訓的人。
- 有一個長篇的初始部分，介紹了對於接受因果分析培訓的人來說，“估計”和“預測”的差異。
- 本書從非參數方法開發了一個機器學習應用的背景框架。
- SVM和NN的介紹足夠簡單，不涉及太多細節。它是自給自足的。
- 非參數時間序列預測是一個新的主題，有一個獨立的章節介紹。
- 增加了其他章節：罰則回歸、降維方法和圖形方法在社會科學中越來越受歡迎。

Yigit Aydede is a Sobey Professor of Economics at Saint Mary's University, Halifax, Nova Scotia, Canada. He is a founder member of the Research Portal on Machine Learning for Social and Health Policy, a joint initiative by a group of researchers from Saint Mary's and Dalhousie universities

Yigit Aydede是加拿大新斯科舍省哈利法克斯聖瑪麗大學的Sobey經濟學教授。他是聖瑪麗大學和達爾豪斯大學的一群研究人員共同發起的機器學習應用於社會和健康政策研究門戶網站的創始成員之一。