Library · Machine learning & AI
What Random Forests & Tree Ensembles got wrong, from 73 theses
Summary written by a model from the records below: Researchers frequently evaluated tree ensembles across diverse tabular, spatial, and clinical prediction tasks. While widely adopted, these models often encountered severe difficulties with overfitting, class imbalance, extreme value prediction, and competing against simpler linear baselines. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 10 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Tree ensembles underperformed simpler linear and parametric models
Researchers found that random forests and tree ensembles frequently failed to beat linear regression, logistic regression, or elastic net baselines across diverse prediction tasks. Simpler models provided comparable or superior predictive accuracy without the added variance and overfitting seen in tree ensembles.
Lost to a baseline
Random forest cross-validation R2 (0.658) lost to simpler multiple linear regression cross-validation R2 (0.733) due to overfitting.
Lost to a baseline
Random Forest models exhibited poorer performance and greater subgroup disparities compared to simpler logistic regression models.
The application of machine learning and causal inference to improve suicide outcomes among U.S. veterans: a focus on clinic characteristics · Boston University · PhD
Lost to a baseline
Decision tree predictive R2 (0.436 with DRS, 0.260 without for FPC1) and Random Forest (0.479 / 0.370) were lower than Elastic Net (0.550 / 0.450).
Identifying Risk Factors for Cognitive Decline Using Statistical Learning Techniques and Functional Data Analysis · Saskatchewan · Master's
Lost to a baseline
Random Forest classifier (AUC=0.707) was beaten by standard logistic regression (AUC=0.752)
Prediction of second line treatment using comorbidities and patient characteristics in newly diagnosed multiple myeloma patients : a machine learning approach · UT Austin · PhD
Lost to a baseline
Random forest regression models did not significantly outperform multiple linear regression, leading to favoring simpler MLR models.
ANALYZING THE IMPACT OF U.S. CENTRAL COMMAND OPERATIONS AND INVESTMENTS IN ITS AREA OF RESPONSIBILITY: AN OPEN SOURCE DATA APPROACH · Naval Postgraduate School · Master's
Lost to a baseline
Random Forest trained on RAVDESS+MESD had lower external validity on TESS than simple Linear Regression and Lasso.
Creating Links: Building an Educational Platform to Ask Relevant Questions in Education · MIT · PhD
Tried and failed
random forest classification applied to imbalanced tabular student outcome prediction. Outcome: worse than baseline. Reason: lower AUC and specificity compared to regularised linear models and gradient boosting across imbalanced panels
Using Machine Learning to Advance High School Dropout Prediction and Prevention · Penn · PhD
Tried and failed
random forest classification and survival modeling applied to clinical tabular outcome prediction. Outcome: worse than baseline. Reason: complex machine learning models failed to outperform classical logistic regression and Cox proportional hazards baselines
Prediction of second line treatment using comorbidities and patient characteristics in newly diagnosed multiple myeloma patients : a machine learning approach · UT Austin · PhD
Tried and failed
Random forests and k-nearest neighbors classification applied to optimization oracle indicator function approximation. Outcome: worse than baseline. Reason: Non-linear classifiers yielded poor generalization accuracy compared to linear baselines like logistic regression.
Consistency in integer programming · Iowa State · PhD
Tried and failed
decision tree classifiers applied to sparse bag-of-words text classification. Outcome: worse than baseline. Reason: decision trees produced poor and inconsistent classification performance compared to logistic regression
Andromeda in Education: Studies on Student Collaboration and Insight Generation with Interactive Dimensionality Reduction · Virginia Tech · Master's
Tried and failed
random forest regression applied to predicting physical parameters from relaxometry data. Outcome: worse than baseline. Reason: non-linear ensemble failed to outperform a simpler quadratic general linear model baseline
Quantitative Microstructural Imaging for Clinical Use · EPFL · PhD
Tried and failed
random forest regression replacing linear mixed-effects models applied to spatial land use regression modeling. Outcome: worse than baseline. Reason: provided no improvement in predictive accuracy over linear mixed-effects regression
Spatiotemporal patterns and socioeconomic inequalities of noise and sound sources in Accra, Ghana · Imperial · PhD
Lost to a baseline
Random forest (AUC = 0.6345) was outperformed by full logistic regression (AUC = 0.65991) on event status prediction.
ENHANCING NAVAL RETENTION: A STRATEGIC APPROACH TO ALLOCATING SELECTIVE REENLISTMENT BONUSES · Naval Postgraduate School · Master's
Lost to a baseline
Random forest on JDTC data achieved 75.00% test accuracy, beaten slightly by standard logistic regression at 77.08%.
Risk, Need, and Racial Inequality: A Machine Learning Analysis of Rearrest in Juvenile Drug Treatment Courts and Traditional Juvenile Courts · University of Nevada, Reno · PhD
Tried and failed
gradient boosted trees with TF-IDF features applied to short text classification. Outcome: worse than baseline. Reason: linear model handles high-dimensional sparse TF-IDF text features better than decision trees
Urban Housing in the Digital Age: Three Applications of Natural Language Processing · Cornell · PhD
Lost to a baseline
Random forest models including full factor sets failed to outperform simpler multiple linear regression models in explaining variance in evolutionary rate or purifying selection.
The evolution of the angiosperm plastid genome · Oxford · PhD
Random forests were rejected due to opacity and computational overhead compared to interpretable baselines
Authors frequently rejected random forests because they lacked explainability and required substantial computational runtime or memory. In these cases, simpler linear regressions or individual decision trees were selected to maintain transparent feature contributions and lower complexity.
Lost to a baseline
Random Forest classifier consumed approximately 8x more runtime than Decision Trees despite achieving comparable accuracy, precision, and recall.
Dynamic Decomposition and Deployment of the Virtual Network Functions with Microservices using Deep Reinforcement Learning · University College Dublin · PhD
Considered and rejected
Considered and rejected: Rejected Random Forest as the final deployed estimator due to lack of interpretability, high memory footprint, and risk of overfitting on small datasets.
Computational Intelligence for Computer-Aided Design Machine Learning Techniques for Microcontrollers Performance Screenings · Politecnico di Torino · PhD
Considered and rejected
Considered and rejected: Rejected Random Forest and KNN regression for destination factor mapping in favor of Multiple Linear Regression due to severe overfitting and poor interpretability.
Leveraging the subtle : hidden factors in recommender systems · TU Wien · PhD
Considered and rejected
Considered and rejected: Rejected Random Forest classification in mlZ in favor of Decision Trees due to lack of accuracy improvement and increased complexity
Characterizing VNTRs in human populations · Boston University · PhD
Considered and rejected
Considered and rejected: Random forest regression rejected in favor of linear regression due to loss of interpretability, poor extrapolation, and inability to handle memory-bound workloads without full target-state counter inputs.
Compute Overlap Stall (COS): Predicting Performance of Power Management for Shared Memory Codes When Throttling Processors, Memory, and Thread Concurrency · Virginia Tech · Master's
Considered and rejected
Considered and rejected: Rejected Random Forest as the final model due to its high complexity and lack of interpretability compared to Decision Trees.
Exploring Phishing Detection Using Search Engine Optimization and Uniform Resource Locator based Information · Dalhousie · Master's
Lost to a baseline
Random Forest model achieved higher classification accuracy (0.92) than the chosen Logistic Regression/LASSO model (>0.80), but Logistic Regression was chosen for interpretability.
The Cost of Decision-Making: Unraveling the Interplay of Memory, Reinforcement Learning, and Neural Connectivity in Decision-Making · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected Random Forest in favor of Logistic Regression due to logistic regression's superior enlisted AUC (0.836 vs 0.695), interpretability/explainability, and reduced computational complexity.
U.S. ARMY RESERVE RETENTION MODELING FOR MID-LEVEL LEADERS · Naval Postgraduate School · Master's
Considered and rejected
Considered and rejected: Rejected random forest and classification tree models in favor of logistic regression because logistic regression provides clearer interpretability for individual variable effects.
PREDICTING MIDSHIPMEN'S OUTCOMES AT THE UNITED STATES NAVAL ACADEMY · Naval Postgraduate School · Master's
Considered and rejected
Considered and rejected: Random forest regression in favor of multiple linear regression due to similar performance and greater simplicity/interpretability of MLR.
ANALYZING THE IMPACT OF U.S. CENTRAL COMMAND OPERATIONS AND INVESTMENTS IN ITS AREA OF RESPONSIBILITY: AN OPEN SOURCE DATA APPROACH · Naval Postgraduate School · Master's
Considered and rejected
Considered and rejected: Rejected evaluating genetic algorithm fitness using Random Forest instead of Logistic Regression because Random Forest's inherent feature importance mechanisms mitigate redundancy and obscure individual feature contributions during GA optimization.
Non-Invasive cancer detection: computational applications in liquid biopsy and radiomics · University of Trento · PhD
Considered and rejected
Considered and rejected: Rejected standard/complex classifiers (SVMs, Random Forests) for Rim Thickness Curves in glaucoma diagnosis because logistic regression performed equally or better
Leveraging Uncertainties in Medical Prediction Systems · Tübingen · PhD
Considered and rejected
Considered and rejected: Rejected using non-linear and random forest models to focus solely on linear logistic regression of binary outcomes.
Modeling Delays in Foreign-Backed Infrastructure Projects: A BRI Case Study on Inequality, Corruption, and Governance · Harvard · Master's
Considered and rejected
Considered and rejected: Tree-based models (such as XGBoost and Random Forests) and Support Vector Machines were rejected in favor of LASSO regression due to multicollinearity issues, extensive hyperparameter tuning requirements, and lack of automatic feature elimination
Tumor Immunogenicity Unlocked: Multi-omics Models Predict Immunotherapy Response and Survival · Tübingen · PhD
Considered and rejected
Considered and rejected: Rejected machine learning (neural networks/random forests) due to high false-positive rates and uninterpretable feature explanations.
Development of computational tools for variant calling in single-cell RNAseq · Oxford · PhD
Ensembles suffered severe overfitting and poor generalization on high dimensional or sparse data
Tree models formed noisy decision boundaries and failed to generalize when trained on high dimensional feature sets or sparse datasets. Pretrained or unconstrained tree models experienced severe drops in accuracy when tested on external or hold-out validation cohorts.
Tried and failed
random forest classification on low-dimensional biomarker data applied to disease diagnosis prediction. Outcome: overfit. Reason: models formed discontinuous islands and narrow decision bands fitting training noise
Machine-Learning Aided Diagnosis Of Alzheimer's Disease · UT Austin
Tried and failed
random forest regression applied to oceanic tracer distribution modeling. Outcome: overfit. Reason: prone to overfitting on sparse data and underperformed gradient boosting
Environmental pollution as a marine tracer: measuring and modelling anthropogenic lead in the ocean · Imperial · PhD
Tried and failed
Random forest regression on dynamic response features applied to crack depth prediction in concrete. Outcome: overfit. Reason: High-dimensional feature space on concrete dynamic response data led to significant overfitting.
Machine learning (ml) approaches to model interdependencies between dynamic loads and crack propagation · Cranfield · PhD
Tried and failed
random forest for chemical property prediction applied to mass spectral chromatographic data. Outcome: did not generalise. Reason: pretrained random forest model suffered significant accuracy drop when applied to new experimental chromatographic dataset
A Statistical Methods-Based Novel Approach for Fully Automated Analysis of Chromatographic Data · Virginia Tech · Doctorate
Tried and failed
random forest classification without feature selection applied to high-dimensional molecular descriptor dataset. Outcome: overfit. Reason: training on large number of molecular descriptors without selection led to severe overfitting and poor AUC
Enabling Data-Driven Experimentation for High-Performance Polymer Thin Film Formulations · Georgia Tech · PhD
Lost to a baseline
Random Forest classifier test accuracy dropped more severely (from 0.78 to 0.69) compared to Support Vector Machine (stayed at 0.71) when pruned to top four LIME-selected features due to overfitting on the full feature set.
Multiscale Integration of Cross-Modal Subsurface Data for Reservoir Characterization under Label-Constrained Environments · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected complex non-linear regressors (Random Forest, Gradient Boosting) for speech emotion prediction because they overfit the training dataset and sacrificed cross-dataset generalizability.
Creating Links: Building an Educational Platform to Ask Relevant Questions in Education · MIT · PhD
Lost to a baseline
In alternate environment validation, simpler CART and Logistic Regression models (balanced accuracy 79.7% and 78.9%) outperformed Random Forest and AdaBoost models which suffered severe overfitting.
COUNTERING SMALL UNMANNED AIRCRAFT SYSTEMS WITH ADVANCED DATA ANALYSIS AND MACHINE LEARNING · Naval Postgraduate School · Master's
Tried and failed
gradient boosted decision tree classification applied to high-dimensional metabolomics profiles. Outcome: did not generalise. Reason: model failed to perform better than chance on an independent hold-out cohort
DEFINING THE METABOLIC LANDSCAPE OF FIBROLAMELLAR CARCINOMA · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected Tree Parzen Estimators and Random Forests as surrogate models in BO under tight computational budgets due to over-partitioning/overfitting with few evaluations
Feature selection and hyperparameter tuning heuristics produced biased or degraded results
Impurity-based feature importance exhibited structural bias toward continuous features, and automated tree feature selection underperformed standard linear selection methods. Additionally, hyperparameter grid search and genetic tuning often failed to improve performance or even degraded accuracy relative to untuned models.
Tried and failed
random forest mean decrease in impurity applied to feature importance estimation. Reason: biased toward continuous variables and features with many categories
Automated methods to find high-redshift quasars · Imperial · PhD
Tried and failed
random forest package implementation for probability estimation applied to discrete choice consideration set formation. Outcome: overfit. Reason: Default implementation overfitted severely, assigning nearly perfect probability to observed alternatives unlike Ranger
Endogeneity and consideration-set issues in residential location choice models · Imperial · PhD
Lost to a baseline
Random forest variable selection underperformed simpler stepwise AIC and LASSO regression in variable recovery and discrimination/calibration metrics
Enhancing Survival Prediction Models: Insights on Biomarker Inclusion and Model Updating · Johns Hopkins · Master's
Tried and failed
gradient boosted trees with default hyperparameters applied to weakly supervised anomaly detection. Outcome: overfit. Reason: default tree hyperparameters were overly aggressive for weakly supervised learning labels
LOOKING FOR NEW PHYSICS: FROM DARK MATTER TO MACHINE LEARNING · Cornell · PhD
Tried and failed
untuned random forest with few estimators applied to blood biomarker disease classification. Outcome: worse than baseline. Reason: insufficient ensemble size and lack of hyperparameter tuning caused extremely poor classification performance
Machine-Learning Aided Diagnosis of Alzheimer's Disease · UT Austin
Lost to a baseline
Feature selection on gas-phase adiabatic ionization potential using random forest failed to improve performance and slightly worsened ionization potential predictions relative to full feature sets.
Tried and failed
random forest hyperparameter tuning via grid search applied to time series error correction. Outcome: overfit. Reason: overemphasized lag-1 features, degrading downstream simulation accuracy and inflating variance
Tried and failed
random forest Bayesian optimization for configuration tuning applied to runtime configuration optimization. Outcome: worse than baseline. Reason: direct flag-to-runtime mapping struggled to navigate high-dimensional configuration spaces effectively compared to random search
Accelerating regression testing through test environment tuning · UT Austin · PhD
Lost to a baseline
In Experiment 2, Grid Search and Genetic Algorithm tuning of Random Forest (MSE 0.0061, R2 0.8086) lost to the base untuned Random Forest (MSE 0.0042, R2 0.8158).
Using Data Analytics and Machine Learning in Sustainable Forest Management from Remote Sensing Data · York University · Master's
Random forests were outperformed by gradient boosted trees or single decision trees
Random forest models frequently trailed gradient boosted decision trees like XGBoost and LightGBM in accuracy, runtime efficiency, and subgroup calibration. In other instances, random forests performed substantially worse than simpler individual classification trees.
Lost to a baseline
Random Forest (ensemble method) performed substantially worse than the simpler single Classification Tree, achieving only 42.43% agreement with BorealDB versus CT's 84.88%.
Exploiting Overlapping Landsat Scene Classifications and Focal Context to Identify Boreal Disturbance Mapping Uncertainty · York University · Master's
Lost to a baseline
Random Forest classifier (macro F1-score 0.87, accuracy 0.88) lost to standard Decision Tree classifier (macro F1-score 0.94, accuracy 0.94) on EMG gesture recognition.
EXPLORING THE POTENTIAL OF COMPUTER VISION AND MACHINE LEARNING IN ENHANCING THE FUNCTIONALITY OF AN EMG-CONTROLLED PROSTHETIC HAND · Saskatchewan · Master's
Tried and failed
random forest regression applied to satellite retrieval bias correction. Outcome: worse than baseline. Reason: significantly underperformed gradient boosted decision tree algorithms like LightGBM and XGBoost
Lost to a baseline
Random Forest (0.519 accuracy) and Bagging Classifier (0.618 accuracy) underperformed compared to simpler Decision Tree (0.994) and Logistic Regression (0.984).
Measuring and Analyzing Community Resilience During COVID-19 Using Social Media · Virginia Tech · Master's
Lost to a baseline
Smooth Random Forest underperformed DPGBDT 1x100 on the HELOC dataset.
A consumer centred investigation of differentially private risk assessment models in consumer credit · Nottingham · PhD
Lost to a baseline
Gradient boosting (Model 4 R2 0.46–0.92) slightly outperformed random forest (Model 3 R2 0.33–0.98).
Small Effect, Big Picture: Understanding the Role of Transposable Elements, Pleiotropy, and Single-Genes on Maize Genetics and Yield · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected Decision Trees, Random Forests, Support Vector Machines, Regularized Linear Regression, and Multi-Layer Perceptrons in favor of XGBoost for Hurricast prediction due to lower performance and higher training time.
Multimodal Machine Learning for Climate Adaptation · MIT · PhD
Considered and rejected
Considered and rejected: Random forest classifier rejected in favour of XGBoost due to lower test accuracy and inferior prediction calibration across SES and gender subgroups
Understanding Student Outcomes: The Role of Background, Gender, and Schools in Irish Education · University College Dublin · PhD
Regression trees compressed output variance and failed to predict extreme values or tails
Random forest regressions systematically under-predicted low values and over-predicted high values, compressing output variance near distribution extremes. Techniques like tail oversampling failed to resolve prediction smoothing, leading to severe biases and poor probability calibration near extreme thresholds.
Tried and failed
random forest regression on continuous clinical scores applied to blood biomarker disease severity prediction. Reason: compressed output variance caused regression to fail at extreme values, resulting in high false positives
Machine-Learning Aided Diagnosis Of Alzheimer's Disease · UT Austin
Tried and failed
indirect mapping predicting multi-item components via random forests applied to health-related utility score estimation. Reason: systematically under-predicted low scores and over-predicted high scores, inflating the overall predicted mean
DEVELOPING BETTER TREATMENT FOR ALCOHOL USE DISORDERS WITH THREE TOOLS: DECISION THEORY, BIG DATA, AND MACHINE LEARNING · Cornell · PhD
Tried and failed
random forests with tail oversampling and residual fitting applied to extreme value exceedance prediction. Reason: techniques were ineffective at resolving tail smoothing in predictions
Data-Driven Methods for Modeling Emissions and Atmospheric Composition · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected using Random Forest models in favor of XGBoost due to extreme computational overhead and boundary overfitting.
Considered and rejected
Considered and rejected: Decided against Random Forests for surrogate modeling because of large prediction errors on sampled points and lack of reliable predictive variance estimates.
Accelerating HLS Autotuning of Large, Highly-parameterized Reconfigurable SoC Mappings · Penn · PhD
Tried and failed
random forest probability estimation applied to molecular synthetic accessibility prediction. Reason: uncalibrated models yielded poor probability estimates near extreme thresholds
Discovery of synthesisable organic materials · Imperial · PhD
Tried and failed
direct execution time prediction with random forest applied to database query execution time modeling. Outcome: overfit. Reason: over-optimized slow queries at the expense of fast ones; predicting decomposed weight parameters was required
Building Instance Aware Systems using Explicit Performance Modeling · MIT · PhD
Tree ensembles struggled with severe class imbalance and rare outcome detection
Under severe class imbalance, tree models produced noisy predictions with low recall and poor specificity compared to parametric baselines. Feature selection pipelines with boosted trees discarded weakly predictive signals that were essential for detecting rare target classes.
Lost to a baseline
Random forest demographic-only model yielded an artifactually high sensitivity of 0.308 due to rare outcome imbalances, performing erratically compared to logistic regression baselines.
PREDICTING SUICIDE: UTILITY OF SOCIAL DETERMINANTS OF HEALTH AND ACCESS DATA · Johns Hopkins · Doctorate
Lost to a baseline
Non-parametric models (Random Forest and XGBoost) were outperformed by simpler parametric models (Logistic Regression and LDA) in sensitivity and balanced accuracy when using synthetic resampling (e.g., LR achieved 0.60 sensitivity vs RF 0.32 and XGBoost 0.36 under SMOTE).
Evaluating Factors Contributing to Crash Severity Among Older Drivers: Statistical Modeling and Machine Learning Approaches · Virginia Tech · Doctorate
Lost to a baseline
Logit model achieved higher specificity (0.876) on unbalanced rCSI test data than Random Forest (0.348) and XGBoost (0.627)
Essays in Applied Economics Linking Policy, Obesity, and Health Economics · Texas Tech · PhD
Tried and failed
gradient boosted decision trees classification applied to imbalanced fine-grained time-series state prediction. Reason: severe class imbalance produced noisy, scattered predictions with low precision and recall
Understanding and Predicting Sit-Stand Desk Usage Patterns and Willingness among Knowledge Workers: A Data-Driven Approach · Virginia Tech · Master's
Lost to a baseline
Traditional Random Forest and Gradient-Boosted Trees suffered from lower recall (0.304 and 0.607, respectively) on HCM classification with CMR features compared to SVM-RBF (0.857) due to class imbalance.
Interpretable machine learning models for investigating and pre-screening cardiomyopathies with high-dimensional genomic and imaging data · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected machine learning approaches (decision trees and random forest) for sensor-drone assignment due to poor constraint handling, class imbalance, and lack of generalizability
Development of a Natural Disaster Information System for RPAS Mission Planning and Monitoring · Queen's University · PhD
Single decision trees suffered from high variance and severe overfitting
Individual decision and regression trees repeatedly overfitted training data and produced unstable decision thresholds across experimental environments. Researchers consequently rejected standalone trees, choosing instead to implement ensemble methods or constrained models to lower variance.
Considered and rejected
Considered and rejected: Rejected single decision tree modeling due to overfitting on training data, favoring random forest models.
The doctrine of marginal gains in prostate cancer; testing improvement philosophy with multiple randomised interventions. · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected using regression trees alone for predictor generation because regression trees are prone to overfitting and lack the predictive accuracy of random forest regressions.
Considered and rejected
Considered and rejected: Single decision trees (CART) were rejected in favor of random forests due to over-fitting tendencies.
PREDICTING SELF-HARM AND IDENTIFYING CAUSAL RISK FACTORS AMONG ADOLESCENTS WHO HAVE HAD CONTACT WITH U.S. CHILD PROTECTIVE SERVICES · Johns Hopkins · PhD
Considered and rejected
Considered and rejected: Decision tree learning for generalized model development (due to producing variable behavioral thresholds that were overfitted to individual runways)
Incorporation of Causal Factors Affecting Pilot Motivation for Improvement of Airport Runway and Exit Design Modeling · Virginia Tech · Master's
Considered and rejected
Considered and rejected: Rejected single decision trees due to high variance and overfitting, implementing an ensemble Random Forest with constrained tree depth instead.
Combining multiple observations to improve our understanding of forest-snow interactions and inform forest management · University of Washington · PhD
Lost to a baseline
Single Decision Tree for HEA phase classification achieved only 67.16% mean CV accuracy, trailing Gradient Boosting (72.99%).
Accelerated Data-Driven Design of Multi-Component Chemistries for Enhanced Mechanical Performance · University at Buffalo · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Perform a grid search over hyperparameter space instead of random search for the Random Forest and XGBoost Alzheimer's diagnosis models. Blocker: Access to the underlying clinical biomarker dataset used in the thesis.
Machine-Learning Aided Diagnosis Of Alzheimer's Disease · UT Austin
Left open
Evaluate varying decision tree depths and random forest models for embedding subspace mapping in neural network generalization prediction. Blocker: None
DEPENDABLE NEURAL NETWORKS FOR SAFETY CRITICAL TASKS · Johns Hopkins · PhD
Left open
Prospectively test decision tree and random forest models across diverse healthcare systems to evaluate clinical efficacy in childhood genetic epilepsy. Blocker: Requires prospective clinical deployment and access to private electronic medical record data across multiple healthcare systems.
Quantitative Informatics Approaches to Characterize and Predict Childhood Genetic Epilepsies · Penn · PhD
Left open
Evaluate statistical and systematic uncertainty behaviors under Random Forest, SVM, and deep neural network models on student response coding datasets. Blocker: Access to private student physics response datasets used in the thesis.
Evaluating language models applied to student thinking about experiments · Cornell · PhD
Left open
Benchmark healthcare capital project performance by evaluating Random Forests, KNN, and DEA grouping algorithms on larger datasets. Blocker: Requires larger proprietary or restricted healthcare capital project BIM and performance datasets
Novel approaches to benchmark capital project performance : an application to healthcare projects · UT Austin · PhD
Left open
Develop online prediction for the random forest framework in aerodynamic shape optimization and test alternative architectures like GNN, RNN, and GAN. Blocker: Lack of specific evaluation metrics, clear baseline integration details, or designated datasets for the proposed alternative ML architectures.
Multifidelity machine learning methods for flow field prediction and aerodynamic shape optimization · Iowa State · PhD
Left open
Implement decision trees or random forests to combine covert channel detection statistical tests instead of logistic regression. Blocker: None
Real-Time Detection of Storage Covert Channels · Carleton University · Master's
Left open
Evaluate the reduced feature subsets produced by the fuzzy feature selection methods using classifiers like SVM and Random Forest. Blocker: None
An investigation of fuzzy methods and meta learning for feature selection · Nottingham · PhD
Left open
Evaluate alternative surrogate models and acquisition functions beyond Random Forest and Expected Improvement for tuning LSM trees in Onix. Blocker: None
Dynamically tuning LSM tree based databases · Boston University · Master's
Left open
Benchmark co-segregation-based Bayesian linear regression feature selection against standard methods like PCA and random forests for microbial phenotypic GWAS. Blocker: None
Probing Genomic, Metabolic, And Phenotypic Evolution In Microbes Using Comparative And Experimental Evolution Data · Georgia Tech · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Bernardo García Bulle Bueno · MIT · PhD · 2025
Creating Links: Building an Educational Platform to Ask Relevant Questions in Education - Ryan A. Kelton · Naval Postgraduate School · Master's · 2024
ANALYZING THE IMPACT OF U.S. CENTRAL COMMAND OPERATIONS AND INVESTMENTS IN ITS AREA OF RESPONSIBILITY: AN OPEN SOURCE DATA APPROACH - Nicholas Balasus · Harvard · PhD · 2026
Correcting Biases in Satellite Methane Observations: Applications to Landfill Emissions in the United States and Continental-Scale Emissions in Africa - Alexandra Kirin McCoy · Virginia Tech · Master's · 2026
Compute Overlap Stall (COS): Predicting Performance of Power Management for Shared Memory Codes When Throttling Processors, Memory, and Thread Concurrency - Paul Fischer · Tübingen · PhD · 2026
Leveraging Uncertainties in Medical Prediction Systems - Axel Gschwind · Tübingen · PhD · 2026
Tumor Immunogenicity Unlocked: Multi-omics Models Predict Immunotherapy Response and Survival - Daire Crotty · University College Dublin · PhD · 2026
Understanding Student Outcomes: The Role of Background, Gender, and Schools in Irish Education - Robiah Wardah · Queen's University · PhD · 2026
Development of a Natural Disaster Information System for RPAS Mission Planning and Monitoring - Donald Long · Cornell · PhD · 2025
DEFINING THE METABOLIC LANDSCAPE OF FIBROLAMELLAR CARCINOMA - Rebeckah Fussell · Cornell · PhD · 2025
Evaluating language models applied to student thinking about experiments - Isabella A. Tice · Harvard · Master's · 2025
Modeling Delays in Foreign-Backed Infrastructure Projects: A BRI Case Study on Inequality, Corruption, and Governance - Bridget Hayes · Cornell · PhD · 2025
DEVELOPING BETTER TREATMENT FOR ALCOHOL USE DISORDERS WITH THREE TOOLS: DECISION THEORY, BIG DATA, AND MACHINE LEARNING
Checking a claim in this area?
We can run the same search on any method or claim. If nothing turns up, we will say so, and that proves nothing on its own.