1. Annealing: Steel annealing data
2. 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.
3. Coil 1999 Competition Data: This data set is from the 1999 Computational Intelligence and Learning (COIL) competition. The data contains measurements of river chemical concentrations and algae densities.
4. 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.
5. Cylinder Bands: Used in decision tree induction for mitigating process delays known as "cylinder bands" in rotogravure printing
6. 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).
7. Glass Identification: From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc)
8. Ionosphere: Classification of radar returns from the ionosphere
9. Musk (Version 1): The goal is to learn to predict whether new molecules will be musks or non-musks
10. QSAR aquatic toxicity: Data set containing values for 8 attributes (molecular descriptors) of 546 chemicals used to predict quantitative acute aquatic toxicity towards Daphnia Magna..
11. QSAR fish toxicity: Data set containing values for 6 attributes (molecular descriptors) of 908 chemicals used to predict quantitative acute aquatic toxicity towards the fish Pimephales promelas (fathead minnow).
12. Water Treatment Plant: Multiple classes predict plant state
13. Wine: Using chemical analysis determine the origin of wines
14. Yacht Hydrodynamics: Delft data set, used to predict the hydodynamic performance of sailing yachts from dimensions and velocity.