DEPRECATED - This repository has been archived and is no longer maintained. The code is provided for historical reference and may contain unpatched or unknown vulnerabilities. It should not be used in production systems.
The research done to evaluate how the predictivity of models are effected by error in either the training or the test set is simple to describe conceptually. Benchmark datasets are downloaded from reputable sources. Then the datasets are split into training and test sets. Randomized error is added and then models created on both error laden and native training sets. Those models are used to predict both error laden and native test sets. Differences in standard statistics commonly used to assess predictivity are observed.
More information on the benchmark datasets used in this experiment is available in DataSets/README.md
More information on running the experiment is documented in src/Features/README.md
More information on the results from a single run of the experiment are available in Results/README.md