Alternative Methods via Random Forest to Identify Interactions in a General Framework and Variable Importance in the Context of Value-Added Models

Alternative Methods via Random Forest to Identify Interactions in a General Framework and Variable Importance in the Context of Value-Added Models

abstract: This work presents two complementary studies that propose heuristic methods to capture characteristics of data using the ensemble learning method of random forest. The first study is motivated by the problem in education of determining teacher effectiveness in student achievement. Value-ad...

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Bibliographic Details
Other Authors: Valdivia, Arturo (Author)
Format: Doctoral Thesis
Language:English
Published: 2013
Subjects:
Statistics
Data Mining
Interactions
Random Forest
Statistical Learning
Value Added Models
Variable Importance
Online Access:http://hdl.handle.net/2286/R.I.20819

Internet

http://hdl.handle.net/2286/R.I.20819

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