This document describes some regression data sets available at LIACC. These data sets can be downloaded and they are provided in a format ready for use with the RT tree induction system. Through the use of some available scripts they can also be used with Cubist, Mars and CART. More information on the format of the files included for each problem can be found here.
Abalone
This data set can be used to obtain a model to predict the age of abalone from physical measurements. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task.  Other measurements, which are easier to obtain, are used to predict the number of rings which determines the age.
•	Source: UCI machine learning repository  (http://www.ics.uci.edu/~mlearn/MLSummary.html).
•	Characteristics: 4177 cases, 8 attributes (1 nominal, 7 continuous).
•	Download : abalone.tar.gz (??? bytes)
Ailerons 
This data set addresses a control problem, namely flying a F16 aircraft. The attributes describe the status of the aeroplane, while the goal is to predict the control action on the ailerons of the aircraft.
•	Source: Experiments of Rui Camacho (rcamacho@garfield.fe.up.pt).
•	Characteristics: 13750 cases, 40 attributes (0 nominal, 40 continuous).
•	Download : ailerons.tar.gz (??? bytes)
Elevators
This data set is also obtained from the task of controlling a F16 aircraft, although the target variable and attributes are different. In this case the goal variable is related to an action taken on the elevators of the aircraft.
•	Source: Experiments of Rui Camacho (rcamacho@garfield.fe.up.pt).
•	Characteristics: 16559 cases, 18 attributes (0 nominal, 18 continuous).
•	Download : elevators.tar.gz (??? bytes)
2Dplanes
This is an artificial data set described in Breiman et al. (1984, p.238).  The cases are generated using the following method:
Generate the values of the 10 attributes independently using the following probabilities:

Obtain the value of the target variable Y using the rule:

•	Source: Breiman et al. (1984, p.238). 
•	Characteristics: 40768 cases, 10 attributes (0 nominal, 10 continuous).
•	Download : cart.tar.gz (??? bytes)
Pole
This is a commercial application described in Weiss & Indurkhya (1995). The data describes a telecommunication problem. No further information is available.
•	Source: The data can be obtained in http://www.cs.su.oz.au/~nitin.
•	Characteristics: 9065 cases, 48 attributes (0 nominal, 48 continuous).
•	Download : pole.tar.gz (??? bytes)
Fried
This is an artificial data set used in Friedman (1991) and also described in Breiman (1996,p.139).  The cases are generated using the following method:
Generate the values of 10 attributes, X1, …, X10 independently each of which uniformly distributed over [0,1]. Obtain the value of the target variable Y using the equation:

•	Source: Breiman (1996, p.139). The actual cases used in our experiments can be obtained by contacting me (ltorgo@ncc.up.pt).
•	Characteristics: 40768 cases, 10 attributes (0 nominal, 10 continuous).
•	Download : fried.tar.gz (??? bytes)
Mv
This is an artificial data set with dependencies between the attribute values.  The cases are generated using the following method:
X1 :	uniformly distributed over [-5,5]
X2 :	uniformly distributed over [-15,-10]
X3 :	IF (X1 > 0) THEN X3 = green
              ELSE X3 = red with probability 0.4 and X4=brown with prob. 0.6
X4 : 	IF (X3=green) THEN X4=X1+2X2
              ELSE X4=X1/2 with prob. 0.3, and X4=X2/2 with prob. 0.7
X5 : 	uniformly distributed over [-1,1]
X6 :	X6=X4,    where  is uniformly distribute over [0,5]
X7 :	X7=yes with prob. 0.3 and X7=no with prob. 0.7
X8 :	IF (X5 < 0.5) THEN X8 = normal ELSE X8 = large
X9 :	uniformly distributed over [100,500]
X10 : 	uniformly distributed integer over the interval [1000,1200]

Obtain the value of the target variable Y using the rules:
IF (X2 > 2 ) THEN Y = 35 – 0.5 X4
ELSE IF (-2  X4  2) THEN Y = 10 – 2 X1
ELSE IF (X7 = yes) THEN Y = 3 –X1/X4
ELSE IF (X8 = normal) THEN Y = X6 + X1
ELSE Y = X1/2
•	Source: Contact me (ltorgo@ncc.up.pt).
•	Characteristics: 40768 cases, 10 attributes (3 nominal, 7 continuous).
•	Download : mv.tar.gz (??? bytes)
Kinematics
This is data set is concerned with the forward kinematics of an 8 link robot arm. Among the existing variants of this data set we have used the variant 8nm, which is known to be highly non-linear and medium noisy.
•	Source: DELVE repository of data.
•	Characteristics: 8192 cases, 8 attributes (0 nominal, 8 continuous).
•	Download : kinematics.tar.gz (??? bytes)
CompAct and CompAct(s)
The Computer Activity databases are a collection of computer systems activity measures. The data was collected from a Sun Sparcstation 20/712 with 128 Mbytes of memory running in a multi-user university department. Users would typically be doing a large variety of tasks ranging from accessing the internet, editing files or running very cpu-bound programs. The data was collected continuously on two separate occasions. On both occassions, system activity was gathered every 5 seconds. The final dataset is taken from both occasions with equal numbers of observations coming from each collection epoch.
System measures used:
   1. lread - Reads (transfers per second ) between system memory and user memory.
   2. lwrite - writes (transfers per second) between system memory and user memory.
   3. scall - Number of system calls of all types per second.
   4. sread - Number of system read calls per second.
   5. swrite - Number of system write calls per second . 
   6. fork - Number of system fork calls per second. 
   7. exec - Number of system exec calls per second. 
   8. rchar - Number of characters transferred per second by system read calls.
   9. wchar - Number of characters transfreed per second by system write calls. 
  10. pgout - Number of page out requests per second.
  11. ppgout - Number of pages, paged out per second. 
  12. pgfree - Number of pages per second placed on the free list. 
  13. pgscan - Number of pages checked if they can be freed per second.
  14. atch - Number of page attaches (satisfying a page fault by reclaiming a page in memory) per second.
  15. pgin - Number of page-in requests per second.
  16. ppgin - Number of pages paged in per second.
  17. pflt - Number of page faults caused by protection errors (copy-on-writes). 
  18. vflt - Number of page faults caused by address translation. 
  19. runqsz - Process run queue size.
  20. freemem - Number of memory pages available to user processes.
  21. freeswap - Number of disk blocks available for page swapping. 
  22. usr - Portion of time (%) that cpus run in user mode.
  23. sys - Portion of time (%) that cpus run in system mode.
  24. wio - Portion of time (%) that cpus are idle waiting for block IO.
  25. idle - Portion of time (%) that cpus are otherwise idle.
The two different regression tasks obtained from these databases are:
CompAct 
Predict usr, the portion of time that cpus run in user mode from all attributes 1-21.
CompAct(s) 
Predict usr using a restricted number (excluding the paging information (10-18)
•	Source: DELVE repository of data.
•	Characteristics: Both data sets contain 8192 cases. For ComptAct we have 22 continuous attributes, while for CompAct(s) the cases are described by 8 continuous variables.
•	Download : compact.tar.gz (??? bytes)
House(16H) and House(8L)
This database was designed on the basis of data provided by US Census Bureau [http://www.census.gov] (under Lookup Access [http://www.census.gov/cdrom/lookup]: Summary Tape File 1). The data were collected as part of the 1990 US census. These are mostly counts cumulated at different survey levels. For the purpose of this data set a level State-Place was used. Data from all states was obtained. Most of the counts were changed into appropriate proportions. 
There are 4 different data sets obtained from this database: 
House(8H)
House(8L)
House(16H)
House(16L)
These are all concerned with predicting the median price of the house in the region based on demographic composition and a state of housing market in the region. A number in the name signifies the number of attributes of the data set. A following letter denotes a very rough approximation to the difficulty of the task. For Low task difficulty, more correlated attributes were chosen as signified by univariate smooth fit of that input on the target. Tasks with High difficulty have had their attributes chosen to make the modelling more difficult due to higher variance or lower correlation of the inputs to the target.
•	Source: DELVE repository of data.
•	Characteristics: Both data sets contain 22784 cases. For House(16H) we have 16 continuous attributes, while for House(8L) the cases are described by 8 continuous variables.
•	Download : census.tar.gz (??? bytes)
Auto-Mpg
This dataset was obtained from the UCI machine learning repository (http://www.ics.uci.edu/~mlearn/MLSummary.html).
The dataset at UCI had originally 9 attributes (the first being the goal variable, the mpg attribute). The last attribute (the car model) is unique for all instances so I've REMOVED IT!
I've also moved the first attribute (the goal variable) to the end, so now is the last attribute, just for compatibility with my dataset format.
All other things are as in UCI.
In the repository they referred an original dataset were apparently there were 8 more instances, but they were removed because they had the "mpg" attribute (the goal) with unknown value.
The attributes brief description goes as follows :
    1. cylinders:     multi-valued discrete	# the number of cylinders
    2. displacement:  continuous		# ?
    3. horsepower:    continuous		# the power of the engine
    4. weight:        continuous		# obvious meaning
    5. acceleration:  continuous		# obvious meaning
    6. model year:    multi-valued discrete	# obvious meaning
    7. origin:        multi-valued discrete	# obvious meaning
    8. mpg:           continuous		# I think is the fuel consumption (miles per gallon)
•	Source: UCI repository of data.
•	Characteristics: 398 cases; 4 continuous variables; 3 nominal vars..
•	Download : auto.tar.gz (??? bytes)
Auto-Price
This data set consists of three types of entities:
(a)	the specification of an auto in terms of various characteristics;
(b)	its assigned insurance risk rating,;
(c)	its normalized losses in use as compared to other cars.  
The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale.  Actuarians call this process "symboling".  A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.The third factor is the relative average loss payment per insured vehicle year.  This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc...), and represents the average loss per car per year.
- Note: Several of the attributes in the database could be used as a "class" attribute.
The original data (from the UCI repository) (http://www.ics.uci.edu/~mlearn/MLSummary.html) has 205 instances described by 26 attributes :
- 15 continuous
- 1 integer
- 10 nominal
The following provides more information on these attributes:
1. symboling:                 -3, -2, -1, 0, 1, 2, 3.
  2. normalized-losses:        continuous from 65 to 256.
  3. make:                     alfa-romero, audi, bmw, chevrolet, dodge, honda,                               isuzu, jaguar, mazda, mercedes-benz, mercury,                               mitsubishi, nissan, peugot, plymouth, porsche,                               renault, saab, subaru, toyota, volkswagen, volvo
  4. fuel-type:                diesel, gas.
  5. aspiration:               std, turbo.
  6. num-of-doors:             four, two.
  7. body-style:               hardtop, wagon, sedan, hatchback, convertible.
  8. drive-wheels:             4wd, fwd, rwd.
  9. engine-location:          front, rear.
 10. wheel-base:               continuous from 86.6 120.9.
 11. length:                   continuous from 141.1 to 208.1.
 12. width:                    continuous from 60.3 to 72.3.
 13. height:                   continuous from 47.8 to 59.8.
 14. curb-weight:              continuous from 1488 to 4066.
 15. engine-type:              dohc, dohcv, l, ohc, ohcf, ohcv, rotor.
 16. num-of-cylinders:         eight, five, four, six, three, twelve, two.
 17. engine-size:              continuous from 61 to 326.
 18. fuel-system:              1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.
 19. bore:                     continuous from 2.54 to 3.94.
 20. stroke:                   continuous from 2.07 to 4.17.
 21. compression-ratio:        continuous from 7 to 23.
 22. horsepower:               continuous from 48 to 288.
 23. peak-rpm:                 continuous from 4150 to 6600.
 24. city-mpg:                 continuous from 13 to 49.
 25. highway-mpg:              continuous from 16 to 54.
 26. price:                    continuous from 5118 to 45400.
The original data also has some missing attribute values denoted by "?" :
   Attribute #:   Number of instances missing a value:
   2.             41
   6.             2
   19.            4
   20.            4
   22.            2
   23.            2
   26.            4

I’ve changed the original data in the following way :
- All instances with unknowns were removed giving 159 instances.
- The goal variable is "price"
- All nominal attributes (10) were removed.
•	Source: UCI repository of data (http://www.ics.uci.edu/~mlearn/MLSummary.html).
•	Characteristics: 159 cases; 14 continuous variables; 1 nominal vars..
•	Download : price.tar.gz (??? bytes)
Housing
This data set concerns the task of predicting housing values in areas of Boston. The used variables and their meaning are the following:
1. CRIM      per capita crime rate by town
2. ZN        proportion of residential land zoned for lots over 25,000 sq.ft.
3. INDUS     proportion of non-retail business acres per town
4. CHAS      Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
5. NOX       nitric oxides concentration (parts per 10 million)
6. RM        average number of rooms per dwelling
7. AGE       proportion of owner-occupied units built prior to 1940
8. DIS       weighted distances to five Boston employment centres
9. RAD       index of accessibility to radial highways
10. TAX      full-value property-tax rate per $10,000
11. PTRATIO  pupil-teacher ratio by town
12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
13. LSTAT    % lower status of the population
14. MEDV     Median value of owner-occupied homes in $1000's

•	Source: UCI repository of data (http://www.ics.uci.edu/~mlearn/MLSummary.html).
•	Characteristics: 506 cases; 13 continuous variables
•	Download : housing.tar.gz (??? bytes)
Diabetes
This data set concerns the study of the factors affecting patterns of insulin-dependent diabetes mellitus in children. The objective is to investigate the dependence of the level of serum C-peptide on the various other factors in order to understand the patterns of residual insulin secretion. The response measurement is the logarithm of C-peptide concentration (pmol/ml) at the diagnosis, and the predictor measurements age and base deficit, a measure of acidity.

•	Source: Book Generalized Additive Models (p.304) by Hastie & Tibshirani, Chapman & Hall.
•	Characteristics: 43 cases; 2 continuous variables
•	Download : diabetes.tar.gz (??? bytes)
Pyrimidines
The task consists of Learning Quantitative Structure Activity Relationships (QSARs). The Inhibition of Dihydrofolate Reductase by Pyrimidines.The data are described in:
King, Ross .D., Muggleton, Steven., Lewis, Richard. and Sternberg, Michael.J.E. Drug Design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. 
•	Source: ?.
•	Characteristics: 74 cases; 27 continuous variables
•	Download : pyrim.tar.gz (??? bytes)
Triazines
The problem is to learn a regression equation/rule/tree to predict the activity from the descriptive structural attributes.
The data and methodology is described in detail in:
- King, Ross .D., Hurst, Jonathan. D.,  and Sternberg, Michael.J.E. A comparison of artificial intelligence methods for modelling QSARs Applied Artificial Intelligence, 1994 (in press).
- Hurst, Jonathan. D., King, Ross .D. and Sternberg, Michael.J.E. Quantitative Structure-Activity Relationships by neural networks and  inductive logic programming: 2. The inhibition of dihydrofolate reductase by triazines. Journal of Computer Aided Molecular Design, 1994 (in press).
•	Source: ?.
•	Characteristics: 186 cases; 60 continuous variables
•	Download : triazines.tar.gz (??? bytes)
Machine-Cpu
The problem concerns Relative CPU Performance Data. More information can be obtained in the UCI Machine Learning repository (http://www.ics.uci.edu/~mlearn/MLSummary.html).
The used attributes are :
MYCT: machine cycle time in nanoseconds (integer)
MMIN: minimum main memory in kilobytes (integer)
MMAX: maximum main memory in kilobytes (integer)
CACH: cache memory in kilobytes (integer)
CHMIN: minimum channels in units (integer)
CHMAX: maximum channels in units (integer)
PRP: published relative performance (integer) (target variable)

•	Source: UCI repository (http://www.ics.uci.edu/~mlearn/MLSummary.html).
•	Characteristics: 209 cases; 6 continuous variables
•	Download : machine.tar.gz (??? bytes)
Servo
This data concerns a robot control problem. Here are some comments by Ross Quinlan obtained from the UCI repository. 
"I seem to remember that the data was from a simulation of a servo system involving a servo amplifier, a motor, a lead screw/nut, and a sliding carriage of some sort.  It may have been on of the translational axes of a robot on the 9th floor of the AI lab.  In any case, the output value is almost certainly a rise time, or the time required for the system to respond to a step change in a position set point."
 (Quinlan, ML'93)
"This is an interesting collection of data provided by Karl Ulrich.  It covers an extremely non-linear phenomenon - predicting the rise time of a servomechanism in terms of two (continuous) gain settings and two (discrete) choices of mechanical linkages."

•	Source: UCI repository (http://www.ics.uci.edu/~mlearn/MLSummary.html).
•	Characteristics: 167 cases; 4 nominal variables
•	Download : servo.tar.gz (??? bytes)
Wisconsin Breast Cancer
Predicting Time To Recur (field 3 in recurrent records).
Each record represents follow-up data for one breast cancer case.  These are consecutive patients seen by Dr. Wolberg since 1984, and include only those cases exhibiting invasive breast cancer and no evidence of distant metastases at the time of diagnosis. 
The first 30 features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass.  They describe characteristics of the cell nuclei present in the image. A few of the images can be found at http://www.cs.wisc.edu/~street/images/
The separation described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree.  Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.
The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].
The Recurrence Surface Approximation (RSA) method is a linear programming model which predicts Time To Recur using both recurrent and nonrecurrent cases.
This database is also available through the UW CS ftp server:
ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WPBC/

Attribute information
1) ID number
2) Outcome (R = recur, N = nonrecur)
3) Time (recurrence time if field 2 = R, disease-free time if field 2= N)
4-33) Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry 
j) fractal dimension ("coastline approximation" - 1)
The mean, standard error, and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features.  For instance, field 4 is Mean Radius, field 14 is Radius SE, field 24 is Worst Radius.
Values for features 4-33 are recoded with four significant digits.
34) Tumor size - diameter of the excised tumor in centimeters
35) Lymph node status - number of positive axillary lymph nodes observed at time of surgery

My personal Notes :
- I've removed the four instances with unknown values of the last attribute
- I've exchanged the attribute position of attributes n.3 (Time) and n.35 (Lymph node).
- I've removed the attribute outcome as it is the class attribute if the problem is treated as a classification one.

•	Source: UCI repository (http://www.ics.uci.edu/~mlearn/MLSummary.html).
•	Characteristics: 194 cases; 32 continuous variables
•	Download : wisconsin.tar.gz (??? bytes)