Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two...

Area of Science:

• Statistics
• Machine Learning
• Data Science

Background:

• Attrition analysis is crucial in many fields.
• Traditional methods for handling missing data may not capture complex selection models.
• Machine learning techniques offer potential for advanced attrition analysis.

Purpose of the Study:

• To evaluate the performance of classification and regression trees (CART) and random forest methods for generating inverse sampling weights in attrition analysis.
• To compare these machine learning approaches with traditional methods like multiple imputation and complete case analysis.
• To assess the utility of these novel techniques in handling missing data within selection models.

Main Methods:

• The study employed two simulations to compare different methods.
• Classification and Regression Trees (CART) and random forest algorithms were used to generate inverse sampling weights.
• Performance was evaluated against multiple imputation and complete case techniques.

Main Results:

• Weights derived from pruned CART analyses demonstrated strong performance.
• These CART-derived weights showed favorable bias and efficiency compared to other methods.
• Initial results indicate the potential of machine learning for improved attrition analysis.

Conclusions:

• Machine learning techniques, specifically CART, show significant potential for improving attrition analysis by generating effective inverse sampling weights.
• Pruned CART analyses offer a robust alternative to traditional methods for handling missing data in complex selection models.
• Applied researchers can benefit from incorporating these advanced machine learning techniques into their missing data analysis strategies.