BinaryQuantileRegression
Codebase for the paper "Estimation and Applications of Quantiles in Deep Binary Classification" in IEEE Transactions on Artificial Intelligence
This archive contains all the relevant code for the paper "Estimation and Applications of Quantiles and in Deep Binary Classification"
The BQR_Walkthrough notebook has a walkthrough of all the topics presented in the main paper for a single dataset. Code for individual experiments can be found in the Individual_Experiments folder.
The dataset folder contains most of the datasets used. Some have been omitted to save space, namely Redshift and the image archives.
Links to the image archives can be found in the ImageResults folder.
The redshift data can be obtained from here : https://www.sdss.org/dr16/data_access/ You will have to create a Casjob to pull the required data. The range of redshift values is 0 to 7. The features selected were: modelMag u’, ’modelMag g’, ’modelMag r’, ’modelMag i’, ’modelMag z’, ’modelMag ug’, ’modelMag gr’, ’modelMag ri’, ’modelMag iz’, ’fiberMag u’, ’fiberMag g’, ’fiberMag r’, ’fiberMag i’, ’fiberMag z’, ’fiberMag ug’, ’fiberMag gr’, ’fiberMag ri’, ’fiberMag iz’, ’petroR50 rr’, ’petroR90 zz’, ’ri’, ’iz’, ’dered u’, ’dered g’, ’dered r’, ’dered i’, ’dered z’, ’petroMag uu’, ’petroMag gg’, ’petroMag rr’, ’petroMag ii’, ’petroMag zz’
Refer to the dataset_params file for details on how to use the datasets in the notebooks