46 gina_agnostic 1 **Author**: **Source**: Unknown - Date unknown **Please cite**: Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch) Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php Modified by TunedIT (converted to ARFF format) GINA is digit recognition database The task of GINA is handwritten digit recognition. For the "agnostic learning track" we chose the problem of separating two-digit odd numbers from two-digit even numbers. Only the unit digit is informative for that task, therefore at least 1/2 of the features are distracters. Additionally, the pixels that are almost always blank were removed and the pixel order was randomized to hide the feature identity. This is a two class classification problem with sparse continuous input variables, in which each class is composed of several clusters. It is a problem with heterogeneous classes. Data type: non-sparse Number of features: 970 Number of examples and check-sums: Pos_ex Neg_ex Tot_ex Check_sum Train 1550 1603 3153 164947945.00 Valid 155 160 315 16688946.00 This dataset contains samples from both training and validation datasets. 1 ARFF 2014-10-06T23:56:01 Public https://test.openml.org/data/v1/download/46/gina_agnostic.arff 46 label study_14 public active 2024-01-10 13:49:30 e8b42f51e0a453e4b77c24f8e2569948