This repo contains all datasets used in Boxer demo. Some other useful resources are listed as follows:
| Dataset Name | Description | Related Use case | Source |
|---|---|---|---|
| Iris | Boxer demo uses Iris dataset by default. The dataset contains 3 classes of 50 instances each | Boxer demo | https://archive.ics.uci.edu/ml/datasets/iris |
| Imputation | The Mushroom dataset that considers a testing set of 2,000 (of 8,124) randomly selected instances. | Model Selection | https://archive.ics.uci.edu/ml/datasets/mushroom |
| IMDB Confidence | The data consists of 5044 movies with 27 features, however, 25% is sequestered for final assessment. A stratified sampling of 200 movies per class is held out from the 3756 for testing. | Model Selection and Tuning | https://www.kaggle.com/suchitgupta60/imdb-data |
| recid | This dataset is used for fair learning: the Broward County recidivism dataset, popularized by ProPublica. The data set contains 6,172 instances and 14 numeric features (created by one-hot encoding the categorical features in the initial seven feature data set). 20% are held for testing. | Fairness Assessment | https://github.com/algofairness/fairness-comparison |
| date-12000-strat | The dataset is the TCP collection of historical documents. It took a random sample of 12,000 documents, and held out 30% using stratified sampling. While the testing set is balanced (1,800 per class), the training set is highly skewed (only 15% before 1642) | Bias and Data Discovery | ... |
| fuzz-mod-5-02 | The data set is a collection of 554 plays written in the Early Modern Period (1470-1660). Five linguistic features are used. It contains four kinds of plays : Comedy, History, Tragedy and Tragicomedy. | Feature Sensitivity Testing | ... |
| tcp-tree-select-9-10 | This dataset considers a corpus of 59,989 documents from a historical literary collection and the data counts the 500 most common English words in each document. | Model Selection and Data Discovery | ... |
| (continuous) wine quality | The dataset is used for wine quality classification, which requires classifying the quality of a wine from its properties. | (Continuous) Hyper parameter Tuning | https://archive.ics.uci.edu/ml/datasets/wine+quality |
| (contunuous) heart disease | The dataset is a standard data set used in machine learning education. Classifiers are trained to predict if a patient is likely to develop a disease (binary decision). | (Continuous) Model Selection and Calibration Analysis | https://archive.ics.uci.edu/ml/datasets/heart+disease |
| (continuous) income | The dataset comes from income classification benchmark dataset from that has been downsampled. Classifiers determine whether an individual’s income is above a certain level. | (Continuous) Model Selection | https://archive.ics.uci.edu/ml/datasets/census+income |
| (continuous) cifar-sampled-scaling | The datset is created based on CIFAR 100 computer vision benchmark using Tensorflow. The data set has 100 classes, and the trained classifier produces a distribution over these classes as its decision.A binary classifier has been created for a “meta-class” which combines 5 of the main classes. This datasets aims to classify flowers, which can be any one of 5 of the original classes. Because the test set contains all 100 classes, it is quite imbalanced: flowers are only 5% of the total instances | (Continuous) Model Selection and Detail Examination | https://github.com/mattdutson/ml-vis |
| (continuous) cdate-2500 | This dataset considers a corpus of 59,989 documents from a historical literary collection: Text Creation Partnership (TCP) transcriptions of the Early English Books Online (EEBO). The data counts the 500 most common English words in each document. For the experiment, we took a random sample of 2500 documents, and held out 30% using stratified sampling. | (Continuous) Data Examination | ... |