ArticleEpidemiology (Cambridge, Mass.)2019
Can Hyperparameter Tuning Improve the Performance of a Super Learner?: A Case Study.
Article in Epidemiology (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
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- Calibration of Low-Cost LoRaWAN-Based IoT Air Quality Monitors Using the Super Learner Ensemble: A Case Study for Accurate Particulate Matter Measurement.Sensors (Basel, Switzerland) · 2025Article
- Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database.Medical & biological engineering & computing · 2024Article
- Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort.Medical & biological engineering & computing · 2024Article
- Machine Learning Models for Parkinson Disease: Systematic Review.JMIR medical informatics · 2024Review
- Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery.Nature communications · 2023Article
- A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study.Journal of medical Internet research · 2023Article
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- Contrast phase recognition in liver computer tomography using deep learning.Scientific reports · 2022Article
- Pruning-based oversampling technique with smoothed bootstrap resampling for imbalanced clinical dataset of Covid-19.Journal of King Saud University. Computer and information sciences · 2022Article
- Predicting the presence of depressive symptoms in the HIV-HCV co-infected population in Canada using supervised machine learning.BMC medical research methodology · 2022Article
- Hyperspectral Image Analysis of Colon Tissue and Deep Learning for Characterization of Health care.Journal of environmental and public health · 2022Article
- The use of narrative electronic prescribing instructions in pharmacoepidemiology: A scoping review for the International Society for Pharmacoepidemiology.Pharmacoepidemiology and drug safety · 2021Article
- Electronic phenotyping of health outcomes of interest using a linked claims-electronic health record database: Findings from a machine learning pilot project.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Implementation of convolutional neural network approach for COVID-19 disease detection.Physiological genomics · 2020Article
- Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach.International journal of environmental research and public health · 2020Article
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5 authors.
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Abstract
backgroundSuper learning is an ensemble machine learning approach used increasingly as an alternative to classical prediction techniques. When implementing super learning, however, not tuning the hyperparameters of the algorithms in it may adversely affect the performance of the super learner.
methodsIn this case study, we used data from a Canadian electronic prescribing system to predict when primary care physicians prescribed antidepressants for indications other than depression. The analysis included 73,576 antidepressant prescriptions and 373 candidate predictors. We derived two super learners: one using tuned hyperparameter values for each machine learning algorithm identified through an iterative grid search procedure and the other using the default values. We compared the performance of the tuned super learner to that of the super learner using default values ("untuned") and a carefully constructed logistic regression model from a previous analysis.
resultsThe tuned super learner had a scaled Brier score (R) of 0.322 (95% [confidence interval] CI = 0.267, 0.362). In comparison, the untuned super learner had a scaled Brier score of 0.309 (95% CI = 0.256, 0.353), corresponding to an efficiency loss of 4% (relative efficiency 0.96; 95% CI = 0.93, 0.99). The previously-derived logistic regression model had a scaled Brier score of 0.307 (95% CI = 0.245, 0.360), corresponding to an efficiency loss of 5% relative to the tuned super learner (relative efficiency 0.95; 95% CI = 0.88, 1.01).
conclusionsIn this case study, hyperparameter tuning produced a super learner that performed slightly better than an untuned super learner. Tuning the hyperparameters of individual algorithms in a super learner may help optimize performance.
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