Feature Importance

The DIGITS dataset consists of 1797 8×8 grayscale images (1439 for training and 360 for testing) of handwritten digits. Source: Differentially Private Variational Dropout Image Source: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html

The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species. Two of the three species were collected in the Gaspé Peninsula "all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus".

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. Source: UCI Machine Learning Repository Wine Dataset Image Source: https://archive.ics.uci.edu/ml/datasets/Wine

What do the instances in this dataset represent? The instances represent hospitalized patient records diagnosed with diabetes. Are there recommended data splits? No recommendation. The standard train-test split could be used. Can use three-way holdout split (i.e., train-validation-test) when doing model selection. Does the dataset contain data that might be considered sensitive in any way? Yes. The dataset contains information about the age, gender, and race of the patients. Additional Information The dataset represents ten years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes. Information was extracted from the database for encounters that satisfied the following criteria. (1) It is an inpatient encounter (a hospital admission). (2) It is a diabetic encounter, that is, one during which any kind of diabetes was entered into the system as a diagnosis. (3) The length of stay was at least 1 day and at most 14 days. (4) Laboratory tests were performed during the encounter. (5) Medications were administered during the encounter. The data contains such attributes as patient number, race, gender, age, admission type, time in hospital, medical specialty of admitting physician, number of lab tests performed, HbA1c test result, diagnosis, number of medications, diabetic medications, number of outpatient, inpatient, and emergency visits in the year before the hospitalization, etc. Has Missing Values? Yes

The dataset contains the hotel demand and revenue of 8 major tourist destinations in the US (e.g., Los Angeles, Orlando ...). The dataset contains sales, daily occupancy, demand, and revenue of the upper-middle class hotels. We also gathered dynamic exogenous variables such as the state’s closure/open policy to enrich our dataset. Specifically, we gathered numerious static features such as the number of hospitals, GPD, and population.

The simply-CLEVR dataset aims to provide a benchmark dataset that can be used for transparent quantitative evaluation of explanation methods (aka heatmaps/XAI methods). It is made of simple Visual Question Answering (VQA) questions, which are derived from the original CLEVR task, and where each question is accompanied by two Ground Truth Masks that serve as a basis for evaluating explanations on the input image. Source: https://github.com/ahmedmagdiosman/simply-clevr-dataset Image Source: https://github.com/ahmedmagdiosman/simply-clevr-dataset