"Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data" - results of machine learning experiments
For all experiments the first level folder hierarchy indicates the method used. Where parameter tuning is performed, the second level folders indicate algorithm parameters . Each experiment output is stored in a xz compressed text file in JSON format. In experiments measuring the learning curves ( training-* ), each results file describes: * experiment setup (algorithm, number of subsets, down-sampled class size)* list of training set sizes * performance measure statistics for all subsets at each training size (flat list) including min, median and max score, and median deviation from median (mad), given for both test and training set instance
Resource information
- Data purpose
- Research dataset
- Task
- Artificial Intelligence and Image Processing
- Subject
- Applied Computer Science, Artificial Intelligence and Image Processing, FOS: Computer and information sciences, FOS: Computer and information sciences
- Dataset size
- 334745600 Bytes
- DOI
- 10.25405/data.ncl.10043060
- Credit basis
- Creators in DataCite DOI metadata
- Publisher
- Newcastle University
- Upstream last modified
- Sep 20, 2020
- Access
- Files remain with the original publisher
- Last checked
- Nov 4, 2026
- License
- CC0
- Last record update
- Nov 4, 2026
About this resource
For all experiments the first level folder hierarchy indicates the method used. Where parameter tuning is performed, the second level folders indicate algorithm parameters . Each experiment output is stored in a xz compressed text file in JSON format. In experiments measuring the learning curves ( training-* ), each results file describes: * experiment setup (algorithm, number of subsets, down-sampled class size)* list of training set sizes * performance measure statistics for all subsets at each training size (flat list) including min, median and max score, and median deviation from median (mad), given for both test and training set instances In parameter tuning experiments ( prediction-multi-* ), each results file contains: * experiment setup (method / algorithm, number of CV repeats, number of model runs)* imputer parameters (not important, kept constant in all experiments) * classifier parameters (for random forest) * true class for each instance * class predictions by the median model from each CV-repeat * class probabilities estimated by the median model from each CV-repeat * performance measure statistics for each CV-repeat including min, median and max score, and median deviation from median (mad) In RFE experiments ( prediction-multi-rfe-* ) the results additionally include: * scores for all RFE steps for each CV-repeat * number of times each feature was selected (across all folds and CV-repeats)