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

Machine Learning Toolbox for Social Scientists provides a comprehensive guide to predictive methods with complementary statistical tools, covering nonparametric methods, data exploration, penalized regressions, model selection, and more. It is targeted at students and researchers with no advanced statistical background, making it an effective tool for teaching in the social science and business fields.

Format: Hardback
Length: 586 pages
Publication date: 22 September 2023
Publisher: Taylor & Francis Ltd

Machine Learning Toolbox for Social Scientists is a comprehensive guide that encompasses predictive methods complemented by statistical tools, making it largely self-contained. Inferential statistics serves as the traditional framework for data analytics courses in social science and business fields, particularly in Economics and Finance. This book goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. It also introduces numerous modern statistical tools complementary to predictive methods, which are not readily found in econometrics textbooks. These tools include 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.

The book is specifically designed for students and researchers with no advanced statistical background, but rather coming from the tradition of inferential statistics. Its modern statistical methods make it highly effective for teaching in the social science and business fields.

Key Features:

The book is structured for individuals who have received training in a traditional statistics curriculum.

It includes a comprehensive initial section that discusses the differences in estimation and prediction for those trained for causal analysis.

The book develops a background framework for Machine learning applications from Nonparametric methods, making it accessible to a wide range of readers.

SVM and NN are explained in a simple and straightforward manner, without excessive detail, ensuring self-sufficiency.

Nonparametric time-series predictions are covered in a separate section, providing valuable insights for future research and applications.

In conclusion, Machine Learning Toolbox for Social Scientists is an essential resource for students and researchers seeking to leverage machine learning and statistical techniques in their social science and business studies. With its comprehensive coverage, intuitive explanations, and practical applications, this book empowers individuals to unlock the power of data analysis and make informed decisions based on empirical evidence.

Weight: 1380g
Dimension: 184 x 261 x 34 (mm)
ISBN-13: 9781032463957