1. Climate Model Simulation Crashes: Given Latin hypercube samples of 18 climate model input parameter values, predict climate model simulation crashes and determine the parameter value combinations that cause the failures.
2. Connectionist Bench (Sonar, Mines vs. Rocks): The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
3. Forest Fires: This is a difficult regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data (see details at: http://www.dsi.uminho.pt/~pcortez/forestfires).
4. Function Finding: Cases collected mostly from investigations in physical science; intention is to evaluate function-finding algorithms
5. Glass Identification: From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc)
6. Ionosphere: Classification of radar returns from the ionosphere
7. Low Resolution Spectrometer: From IRAS data -- NASA Ames Research Center
8. Musk (Version 1): The goal is to learn to predict whether new molecules will be musks or non-musks
9. Robot Execution Failures: This dataset contains force and torque measurements on a robot after failure detection. Each failure is characterized by 15 force/torque samples collected at regular time intervals
10. Water Treatment Plant: Multiple classes predict plant state
11. Wine: Using chemical analysis determine the origin of wines
12. Yacht Hydrodynamics: Delft data set, used to predict the hydodynamic performance of sailing yachts from dimensions and velocity.