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Area of Science:

  • Machine Learning
  • Behavioral Sciences
  • Psychometrics

Background:

  • Recursive partitioning (decision trees, CART) is valuable for nonlinear and interactive effects in behavioral science.
  • The standard algorithm's greedy approach can lead to overfitting, particularly with small sample sizes.
  • Chance associations in data can reduce the reliability and generalizability of predictive models.

Purpose of the Study:

  • To propose and evaluate a novel reliability-based cost function for recursive partitioning.
  • To enhance the selection of reliable predictors and improve model stability.
  • To mitigate the impact of chance associations in small sample sizes.

Main Methods:

  • Developed and simulated two reliability-based cost functions for recursive partitioning.
  • Compared the performance of the new cost functions against the standard CART algorithm.
  • Applied the methods to real-world data on depression and suicidal ideation from the National Longitudinal Survey of Youth.

Main Results:

  • Reliability-based cost functions demonstrated benefits, especially in smaller samples.
  • These functions increase the likelihood of selecting more reliable variables.
  • Potential drawback: may overlook important associations with lower reliability predictors.

Conclusions:

  • Reliability-based cost functions offer a valuable enhancement to recursive partitioning for behavioral science applications.
  • These methods improve predictive model stability and reduce overfitting in smaller datasets.
  • Careful consideration is needed regarding the trade-off between predictor reliability and potential overlooked associations.