Machine-Learning Techniques in Economics: New Tools for Predicting Economic Growth (Paperback) (ISBN-13: 9783319690131)

Offers a guide to how machine learning techniques can improve predictive power in answering economic questions
Provides R codes to help guide the researcher in applying machine learning techniques using the R package

Uses partial dependence plots to tease out non-linear effects of explanatory variables on the dependent variables


Author: Atin Basuchoudhary, James T. Bang, Tinni Sen
ISBN-10: 3319690132
ISBN-13: 9783319690131
Publisher: Springer
Language: English
Published: 01/08/2018
Pages: 94
Format: Paperback
Weight: 0.33lbs
Size: 9.21h x 6.14w x 0.21d