Judgment Analysis in the Medical Domain: Making a Fair Comparison Between Logistic Regression and Fast & Frugal Models
(English)Article in journal (Refereed) Submitted
Using participant data from the medical domain, the robustness of logistic regression (LR) with different cue inclusion levels and two fast and frugal (F&F) models in terms of predictive accuracy and frugality were tested. Two data sets based on judgments of verbally described patients were used: Heart failure (66 analysts), and Hyperlipidemia (38 analysts). In both data sets, when the models were cross-validated, there was a significant decrease in predictive accuracy for all models, especially when all cues were used in LR. The other models had about equal predictive accuracy, also when comparisons were made with actual diagnoses, with a slight advantage for LR in the Heart failure study. LR using the 5% inclusion level was more frugal than F&F. These results emphasize the importance of using cross-validation and of choosing the proper significance levels for cue inclusion and when comparing different judgment models.
Logistic regression, fast and frugal, cross-validation, fit, prediction, frugality
Psychology (excluding Applied Psychology)
Research subject Psychology
IdentifiersURN: urn:nbn:se:su:diva-57071OAI: oai:DiVA.org:su-57071DiVA: diva2:414262