社会科学家的机器学习工具箱：带R的应用预测分析

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.

社会科学家的机器学习工具箱涵盖了预测方法和互补的统计“工具”，使其基本上是独立的。推理统计学是社会科学和商业领域，尤其是经济学和金融学中大多数数据分析课程的传统框架。本书提供的新组织超越了标准的机器学习代码应用程序，为社会科学和商科学生可以遵循的新预测方法提供了直观的背景。这本书还添加了许多其他现代统计工具，以补充“计量经济学”教科书中无法轻易找到的预测方法：非参数方法、预测模型的数据探索、惩罚回归、稀疏模型选择、降维方法、非参数时间序列预测、图形网络分析、算法优化方法，具有不平衡数据的分类等。本书针对

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.