Yeh (1998) describes a collection of data sets from different sources that can be used for modeling the compressive strength of concrete formulations as a functions of their ingredients and age.
These data consist of information on 4331 jobs in a high performance computing environment. Seven attributes were recorded for each job along with a discrete class describing the execution time.
Hill, LaPan, Li and Haney (2007) develop models to predict which cells in a high content screen were well segmented. The data consists of 119 imaging measurements on 2019. The original analysis used 1009 for training and 1010 as a test set (see the column called Case).
This data set was used to develop a model for predicting compounds' probability of causing hepatic injury (i.e. liver damage). This data set consisted of 281 unique compounds; 376 predictors were measured or computed for each. The response was categorical (either "None", "Mild" or "Severe" ),and was highly unbalanced.
This pharmaceutical data set was used to develop a model for predicting compounds' permeability. In short, permeability is the measure of a molecule's ability to cross a membrane. The body, for example, has notable membranes between the body and brain, known as the blood-brain barrier, and between the gut and body in the intestines. These membranes help the body guard critical regions from receiving undesirable or detrimental substances. For an orally taken drug to be effective in the brain, it first must pass through the intestinal wall and then must pass through the blood-brain barrier in order to be present for the desired neurological target. Therefore, a compound's ability to permeate relevant biological membranes is critically important to understand early in the drug discovery process. Compounds that appear to be effective for a particular disease in research screening experiments, but appear to be poorly permeable may need to be altered in order improve permeability, and thus the compound's ability to reach the desired target. Identifying permeability problems can help guide chemists towards better molecules.
Tetko et al. (2001) and Huuskonen (2000) investigated a set of compounds with corresponding experimental solubility values using complex sets of descriptors. They used linear regression and neural network models to estimate the relationship between chemical structure and solubility. For our analyses, we will use 1267 compounds and a set of more understandable descriptors that fall into one of three groups: 208 binary "fingerprints" that indicate the presence or absence of a particular chemical sub-structure, 16 count descriptors (such as the number of bonds or the number of Bromine atoms) and 4 continuous descriptors (such as molecular weight or surface area).
Washington University conducted a clinical study to determine if biological measurements made from cerebrospinal fluid (CSF) can be used to diagnose or predict Alzheimer's disease (Craig-Schapiro et al. 2011). These data are a modified version of the values used for the publication.
The Abalone data consist of data from 4177 abalones. The data consist of measurements of the type (male, female and infant), the longest shell measurement, the diameter, height and several weights (whole, shucked, viscera and shell). The outcome is the number of rings. The age of the abalone is the number of rings plus 1.5.
The http://fueleconomy.gov website, run by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy and the U.S. Environmental Protection Agency, lists different estimates of fuel economy for passenger cars and trucks. For each vehicle, various characteristics are recorded such as the engine displacement or number of cylinders. Along with these values, laboratory measurements are made for the city and highway miles per gallon (MPG) of the car.
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