Package 'mlbench'

Convert an mlbench object to a dataframe

Description

Converts x (which is basically a list) to a dataframe.

Usage

## S3 method for class 'mlbench'
as.data.frame(x, row.names=NULL, optional=FALSE, ...)

Arguments

x	Object of class "mlbench".
row.names, optional, ...	currently ignored.

Examples

p <- mlbench.xor(5)
p
as.data.frame(p)

---

Bayes classifier

Description

Returns the decision of the (optimal) Bayes classifier for a given data set. This is a generic function, i.e., there are different methods for the various mlbench problems.

If the classes of the problem do not overlap, then the Bayes decision is identical to the true classification, which is implemented as the dummy function bayesclass.noerr (which simply returns z$classes and is used for all problems with disjunct classes).

Usage

bayesclass(z)

Arguments

z	An object of class "mlbench".

Examples

# 6 overlapping classes
p <- mlbench.2dnormals(500,6)
plot(p)

plot(p$x, col=as.numeric(bayesclass(p)))

---

Boston Housing Data

Description

Housing data for 506 census tracts of Boston from the 1970 census. The data frame BostonHousing contains the original data by Harrison and Rubinfeld (1978), the dataf rame BostonHousing2 the corrected version with additional spatial information (see references below).

Usage

data("BostonHousing", package = "mlbench")
data("BostonHousing2", package = "mlbench")

Format

The original data are 506 observations on 14 variables, medv being the target variable:

crim	per capita crime rate by town
zn	proportion of residential land zoned for lots over 25,000 sq.ft
indus	proportion of non-retail business acres per town
chas	Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
nox	nitric oxides concentration (parts per 10 million)
rm	average number of rooms per dwelling
age	proportion of owner-occupied units built prior to 1940
dis	weighted distances to five Boston employment centres
rad	index of accessibility to radial highways
tax	full-value property-tax rate per USD 10,000
ptratio	pupil-teacher ratio by town
b	$1000(B - 0.63)^2$ where $B$ is the proportion of blacks by town
lstat	percentage of lower status of the population
medv	median value of owner-occupied homes in USD 1000's

The corrected data set has the following additional columns:

cmedv	corrected median value of owner-occupied homes in USD 1000's
town	name of town
tract	census tract
lon	longitude of census tract
lat	latitude of census tract

Source

The original data were taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and no longer seem to be available from the UC Irvine Machine Learning Repository (now at https://archive.ics.uci.edu/). The corrected data were taken from Statlib at https://lib.stat.cmu.edu/datasets/. See Statlib and references there for details on the corrections. Both were converted to R format by Friedrich Leisch.

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Gilley OW, Pace RK (1996). “On the Harrison and Rubinfeld Data.” Journal of Environmental Economics and Management, 31(3), 403–405. ISSN 0095-0696. doi:10.1006/jeem.1996.0052. [Provided corrections and examined censoring.].

Harrison D, Rubinfeld DL (1978). “Hedonic Housing Prices and the Demand for Clean Air.” Journal of Environmental Economics and Management, 5(1), 81–102. ISSN 0095-0696. doi:10.1016/0095-0696(78)90006-2.

Pace RK, Gilley OW (1997). “Using the Spatial Configuration of the Data to Improve Estimation.” The Journal of Real Estate Finance and Economics, 14, 333–340. doi:10.1023/A:1007762613901. [Added georeferencing and spatial estimation.].

Examples

data("BostonHousing", package = "mlbench")
summary(BostonHousing)

data("BostonHousing2", package = "mlbench")
summary(BostonHousing2)

---

Wisconsin Breast Cancer Database

Description

The objective is to identify each of a number of benign or malignant classes. Samples arrive periodically as Dr. Wolberg reports his clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself. Each variable except for the first was converted into 11 primitive numerical attributes with values ranging from 0 through 10. There are 16 missing attribute values. See cited below for more details.

Usage

data("BreastCancer", package = "mlbench")

Format

A data frame with 699 observations on 11 variables, one being a character variable, 9 being ordered or nominal, and 1 target class.

[,1]	Id	Sample code number
[,2]	Cl.thickness	Clump Thickness
[,3]	Cell.size	Uniformity of Cell Size
[,4]	Cell.shape	Uniformity of Cell Shape
[,5]	Marg.adhesion	Marginal Adhesion
[,6]	Epith.c.size	Single Epithelial Cell Size
[,7]	Bare.nuclei	Bare Nuclei
[,8]	Bl.cromatin	Bland Chromatin
[,9]	Normal.nucleoli	Normal Nucleoli
[,10]	Mitoses	Mitoses
[,11]	Class	Class

Source

• Creator: Dr. WIlliam H. Wolberg (physician); University of Wisconsin Hospital ;Madison; Wisconsin; USA

• Donor: Olvi Mangasarian ([email protected])

• Received: David W. Aha ([email protected])

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Evgenia Dimitriadou in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Breast Cancer Wisconsin (Original) data set is available from doi:10.24432/C5HP4Z.

References

1. Wolberg WH, Mangasarian OL (1990). “Multisurface Method of Pattern Separation for Medical Diagnosis Applied to Breast Cytology.” Proceedings of the National Academy of Sciences, 87(23), 9193–9196. doi:10.1073/pnas.87.23.9193.
- Size of data set: only 369 instances (at that point in time)
- Collected classification results: 1 trial only
- Two pairs of parallel hyperplanes were found to be consistent with 50% of the data
- Accuracy on remaining 50% of dataset: 93.5%
- Three pairs of parallel hyperplanes were found to be consistent with 67% of data
- Accuracy on remaining 33% of dataset: 95.9%

2. Zhang J (1992). “Selecting Typical Instances in Instance-Based Learning.” In Sleeman D, Edwards P (eds.), Machine Learning Proceedings 1992, 470–479. Morgan Kaufmann, San Francisco (CA). ISBN 978-1-55860-247-2. doi:10.1016/B978-1-55860-247-2.50066-8.
- Size of data set: only 369 instances (at that point in time)
- Applied 4 instance-based learning algorithms
- Collected classification results averaged over 10 trials
- Best accuracy result:
- 1-nearest neighbor: 93.7%
- trained on 200 instances, tested on the other 169
- Also of interest:
- Using only typical instances: 92.2% (storing only 23.1 instances)
- trained on 200 instances, tested on the other 169

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Examples

data("BreastCancer", package = "mlbench")
summary(BreastCancer)

---

Primate splice-junction gene sequences (DNA)

Description

It consists of 3,186 data points (splice junctions). The data points are described by 180 indicator binary variables and the problem is to recognize the 3 classes (ei, ie, neither), i.e., the boundaries between exons (the parts of the DNA sequence retained after splicing) and introns (the parts of the DNA sequence that are spliced out).

The StaLog dna dataset is a processed version of the Irvine database described below. The main difference is that the symbolic variables representing the nucleotides (only A,G,T,C) were replaced by 3 binary indicator variables. Thus the original 60 symbolic attributes were changed into 180 binary attributes. The names of the examples were removed. The examples with ambiguities were removed (there was very few of them, 4). The StatLog version of this dataset was produced by Ross King at Strathclyde University. For original details see the Irvine database documentation.

The nucleotides A,C,G,T were given indicator values as follows:

	A -> 1 0 0
	C -> 0 1 0
	G -> 0 0 1
	T -> 0 0 0

Hint. Much better performance is generally observed if attributes closest to the junction are used. In the StatLog version, this means using attributes A61 to A120 only.

Usage

data("DNA", package = "mlbench")

Format

A data frame with 3,186 observations on 180 variables, all nominal and a target class.

Source

• Source:
  - all examples taken from Genbank 64.1 (ftp site: genbank.bio.net)
  - categories "ei" and "ie" include every "split-gene" for primates in Genbank 64.1
  - non-splice examples taken from sequences known not to include a splicing site
  

• Donor: G. Towell, M. Noordewier, and J. Shavlik, towell,[email protected], [email protected]

These data have been taken from:

• ftp.stams.strath.ac.uk/pub/Statlog

and were converted to R format by Evgenia Dimitriadou.

References

Noordewier MO, Towell GG, Shavlik JW (1990). “Training Knowledge-Based Neural Networks to Recognize Genes in DNA Sequences.” In Proceedings of the 4th International Conference on Neural Information Processing Systems, series NIPS'90, 530–536. ISBN 1558601848.

Towell GG (1991). Symbolic Knowledge and Neural Networks: Insertion, Refinement, and Extraction. Ph.D. thesis, University of Wisconsin Madison.

Towell GG, Craven MW, Shavlik JW (1991). “Constructive Induction in Knowledge-Based Neural Networks.” In Birnbaum LA, Collins GC (eds.), Machine Learning Proceedings 1991, 213–217. Morgan Kaufmann, San Francisco (CA). ISBN 978-1-55860-200-7. doi:10.1016/B978-1-55860-200-7.50046-5.

Towell GG, Shavlik JW (1991). “Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules.” In Proceedings of the 5th International Conference on Neural Information Processing Systems, series NIPS'91, 977–984. ISBN 1558602224.

Examples

data("DNA", package = "mlbench")
summary(DNA)

---

Glass Identification Database

Description

A data frame with 214 observation containing examples of the chemical analysis of 7 different types of glass. The problem is to forecast the type of class on basis of the chemical analysis. The study of classification of types of glass was motivated by criminological investigation. At the scene of the crime, the glass left can be used as evidence (if it is correctly identified!).

Usage

data("Glass", package = "mlbench")

Format

A data frame with 214 observations on 10 variables:

[,1]	RI	refractive index
[,2]	Na	Sodium
[,3]	Mg	Magnesium
[,4]	Al	Aluminum
[,5]	Si	Silicon
[,6]	K	Potassium
[,7]	Ca	Calcium
[,8]	Ba	Barium
[,9]	Fe	Iron
[,10]	Type	Type of glass (class attribute)

Source

• Creator: B. German, Central Research Establishment, Home Office Forensic Science Service, Aldermaston, Reading, Berkshire RG7 4PN

• Donor: Vina Spiehler, Ph.D., DABFT, Diagnostic Products Corporation

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Friedrich Leisch in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Glass Identification data set is available from doi:10.24432/C5WW2P.

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Examples

data("Glass", package = "mlbench")
summary(Glass)

---

United States Congressional Voting Records 1984

Description

This data set includes votes for each of the U.S. House of Representatives Congressmen on the 16 key votes identified by the CQA. The CQA lists nine different types of votes: voted for, paired for, and announced for (these three simplified to yea), voted against, paired against, and announced against (these three simplified to nay), voted present, voted present to avoid conflict of interest, and did not vote or otherwise make a position known (these three simplified to an unknown disposition).

Usage

data("HouseVotes84", package = "mlbench")

Format

A data frame with 435 observations on 17 variables:

1	Class Name: 2 (democrat, republican)
2	handicapped-infants: 2 (y,n)
3	water-project-cost-sharing: 2 (y,n)
4	adoption-of-the-budget-resolution: 2 (y,n)
5	physician-fee-freeze: 2 (y,n)
6	el-salvador-aid: 2 (y,n)
7	religious-groups-in-schools: 2 (y,n)
8	anti-satellite-test-ban: 2 (y,n)
9	aid-to-nicaraguan-contras: 2 (y,n)
10	mx-missile: 2 (y,n)
11	immigration: 2 (y,n)
12	synfuels-corporation-cutback: 2 (y,n)
13	education-spending: 2 (y,n)
14	superfund-right-to-sue: 2 (y,n)
15	crime: 2 (y,n)
16	duty-free-exports: 2 (y,n)
17	export-administration-act-south-africa: 2 (y,n)

Source

• Source: Congressional Quarterly Almanac, 98th Congress, 2nd session 1984, Volume XL: Congressional Quarterly Inc., ington, D.C., 1985

• Donor: Jeff Schlimmer ([email protected])

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Friedrich Leisch in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Congressional Voting Records data set is available from doi:10.24432/C5C01P.

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Examples

data("HouseVotes84", package = "mlbench")
summary(HouseVotes84)

---

Johns Hopkins University Ionosphere database

Description

This radar data was collected by a system in Goose Bay, Labrador. This system consists of a phased array of 16 high-frequency antennas with a total transmitted power on the order of 6.4 kilowatts. See the paper for more details. The targets were free electrons in the ionosphere. "good" radar returns are those showing evidence of some type of structure in the ionosphere. "bad" returns are those that do not; their signals pass through the ionosphere.

Received signals were processed using an autocorrelation function whose arguments are the time of a pulse and the pulse number. There were 17 pulse numbers for the Goose Bay system. Instances in this databse are described by 2 attributes per pulse number, corresponding to the complex values returned by the function resulting from the complex electromagnetic signal. See cited below for more details.

Usage

data("Ionosphere", package = "mlbench")

Format

A data frame with 351 observations on 35 independent variables, some numerical and 2 nominal, and one last defining the class.

Source

• Source: Space Physics Group; Applied Physics Laboratory; Johns Hopkins University; Johns Hopkins Road; Laurel; MD 20723

• Donor: Vince Sigillito ([email protected])

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Evgenia Dimitriadou in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Ionosphere data set is available from doi:10.24432/C5W01B.

References

Sigillito VG, Wing S, Hutton LV, Baker KL (1989). “Classification of Radar Returns from the Ionosphere Using Neural Networks.” Johns Hopkins APL Technical Digest, 10(3), 262–266. https://secwww.jhuapl.edu/techdigest/content/techdigest/pdf/V10-N03/10-03-Sigillito_Class.pdf.

They investigated using backprop and the perceptron training algorithm on this database. Using the first 200 instances for training, which were carefully split almost 50% positive and 50% negative, they found that a "linear" perceptron attained 90.7%, a "non-linear" perceptron attained 92%, and backprop an average of over 96% accuracy on the remaining 150 test instances, consisting of 123 "good" and only 24 "bad" instances. (There was a counting error or some mistake somewhere; there are a total of 351 rather than 350 instances in this domain.) Accuracy on "good" instances was much higher than for "bad" instances. Backprop was tested with several different numbers of hidden units (in [0,15]) and incremental results were also reported (corresponding to how well the different variants of backprop did after a periodic number of epochs).

David Aha ([email protected]) briefly investigated this database. He found that nearest neighbor attains an accuracy of 92.1%, that Ross Quinlan's C4 algorithm attains 94.0% (no windowing), and that IB3 (Aha and Kibler, IJCAI-1989) attained 96.7% (parameter settings: 70% and 80% for acceptance and dropping respectively).

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Examples

data("Ionosphere", package = "mlbench")
summary(Ionosphere)

---

Letter Image Recognition Data

Description

The objective is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20,000 unique stimuli. Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15. We typically train on the first 16000 items and then use the resulting model to predict the letter category for the remaining 4000. See the article cited below for more details.

Usage

data("LetterRecognition", package = "mlbench")

Format

A data frame with 20,000 observations on 17 variables, the first is a factor with levels A-Z, the remaining 16 are numeric.

[,1]	lettr	capital letter
[,2]	x.box	horizontal position of box
[,3]	y.box	vertical position of box
[,4]	width	width of box
[,5]	high	height of box
[,6]	onpix	total number of on pixels
[,7]	x.bar	mean x of on pixels in box
[,8]	y.bar	mean y of on pixels in box
[,9]	x2bar	mean x variance
[,10]	y2bar	mean y variance
[,11]	xybar	mean x y correlation
[,12]	x2ybr	mean of $x^2 y$
[,13]	xy2br	mean of $x y^2$
[,14]	x.ege	mean edge count left to right
[,15]	xegvy	correlation of x.ege with y
[,16]	y.ege	mean edge count bottom to top
[,17]	yegvx	correlation of y.ege with x

Source

• Creator: David J. Slate

• Odesta Corporation; 1890 Maple Ave; Suite 115; Evanston, IL 60201

• Donor: David J. Slate ([email protected]) (708) 491-3867

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Friedrich Leisch in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Letter Recognition data set is available from doi:10.24432/C5ZP40.

References

Frey PW, Slate DJ (1991). “Letter Recognition Using Holland-Style Adaptive Classifiers.” Machine Learning, 6, 161–182. doi:10.1007/BF00114162.

The research for this article investigated the ability of several variations of Holland-style adaptive classifier systems to learn to correctly guess the letter categories associated with vectors of 16 integer attributes extracted from raster scan images of the letters. The best accuracy obtained was a little over 80%. It would be interesting to see how well other methods do with the same data.

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Examples

data("LetterRecognition", package = "mlbench")
summary(LetterRecognition)

---

2-dimensional Gaussian Problem

Description

Each of the cl classes consists of a 2-dimensional Gaussian. The centers are equally spaced on a circle around the origin with radius r.

Usage

mlbench.2dnormals(n, cl=2, r=sqrt(cl), sd=1)

Arguments

n	number of patterns to create
cl	number of classes
r	radius at which the centers of the classes are located
sd	standard deviation of the Gaussians

Value

Returns an object of class "bayes.2dnormals" with components

x	input values
classes	factor vector of length n with target classes

Examples

# 2 classes
p <- mlbench.2dnormals(500,2)
plot(p)
# 6 classes
p <- mlbench.2dnormals(500,6)
plot(p)

---

Cassini: A 2 Dimensional Problem

Description

The inputs of the cassini problem are uniformly distributed on a 2-dimensional space within 3 structures. The 2 external structures (classes) are banana-shaped structures and in between them, the middle structure (class) is a circle.

Usage

mlbench.cassini(n, relsize=c(2,2,1))

Arguments

n	number of patterns to create
relsize	relative size of the classes (vector of length 3)

Value

Returns an object of class "mlbench.cassini" with components

x	input values
classes	vector of length n with target classes

Author(s)

Evgenia Dimitriadou and Andreas Weingessel

Examples

p <- mlbench.cassini(5000)
plot(p)

---

Circle in a Square Problem

Description

The inputs of the circle problem are uniformly distributed on the d-dimensional cube with corners $\{\pm 1\}$. This is a 2-class problem: The first class is a d-dimensional ball in the middle of the cube, the remainder forms the second class. The size of the ball is chosen such that both classes have equal prior probability 0.5.

Usage

mlbench.circle(n, d=2)

Arguments

n	number of patterns to create
d	dimension of the circle problem

Value

Returns an object of class "mlbench.circle" with components

x	input values
classes	factor vector of length n with target classes

Examples

# 2d example
p<-mlbench.circle(300,2)
plot(p)
#
# 3d example
p<-mlbench.circle(300,3)
plot(p)

---

Cuboids: A 3 Dimensional Problem

Description

The inputs of the cuboids problem are uniformly distributed on a 3-dimensional space within 3 cuboids and a small cube in the middle of them.

Usage

mlbench.cuboids(n, relsize=c(2,2,2,1))

Arguments

n	number of patterns to create
relsize	relative size of the classes (vector of length 4)

Value

Returns an object of class "mlbench.cuboids" with components

x	input values
classes	vector of length n with target classes

Author(s)

Evgenia Dimitriadou, and Andreas Weingessel

Examples

p <- mlbench.cuboids(7000)
plot(p)
## Not run: 
library(Rggobi)
g <- ggobi(p$x)
g$setColors(p$class)
g$setMode("2D Tour")

## End(Not run)

---

Benchmark Problem Friedman 1

Description

The regression problem Friedman 1 as described in Friedman (1991) and Breiman (1996). Inputs are 10 independent variables uniformly distributed on the interval $[0,1]$, only 5 out of these 10 are actually used. Outputs are created according to the formula

$y = 10 \sin(\pi x1 x2) + 20 (x3 - 0.5)^2 + 10 x4 + 5 x5 + e$

where e is N(0,sd).

Usage

mlbench.friedman1(n, sd=1)

Arguments

n	number of patterns to create
sd	Standard deviation of noise

Value

Returns a list with components

x	input values (independent variables)
y	output values (dependent variable)

References

Breiman L (1996). “Bagging Predictors.” Machine Learning, 24(2), 123–140. doi:10.1023/a:1018054314350.

Friedman JH (1991). “Multivariate Adaptive Regression Splines.” The Annals of Statistics, 19(1), 1–67. doi:10.1214/aos/1176347963.

---

Benchmark Problem Friedman 2

Description

The regression problem Friedman 2 as described in Friedman (1991) and Breiman (1996). Inputs are 4 independent variables uniformly distributed over the ranges

$0 \le x1 \le 100$

$40 \pi \le x2 \le 560 \pi$

$0 \le x3 \le 1$

$1 \le x4 \le 11$

The outputs are created according to the formula

$y = (x1^2 + (x2 x3 - (1/(x2 x4)))^2)^{0.5} + e$

where e is N(0,sd).

Usage

mlbench.friedman2(n, sd=125)

Arguments

n	number of patterns to create
sd	Standard deviation of noise. The default value of 125 gives a signal to noise ratio (i.e., the ratio of the standard deviations) of 3:1. Thus, the variance of the function itself (without noise) accounts for 90% of the total variance.

Value

Returns a list with components

x	input values (independent variables)
y	output values (dependent variable)

References

Breiman L (1996). “Bagging Predictors.” Machine Learning, 24(2), 123–140. doi:10.1023/a:1018054314350.

Friedman JH (1991). “Multivariate Adaptive Regression Splines.” The Annals of Statistics, 19(1), 1–67. doi:10.1214/aos/1176347963.

---

Benchmark Problem Friedman 3

Description

The regression problem Friedman 3 as described in Friedman (1991) and Breiman (1996). Inputs are 4 independent variables uniformly distributed over the ranges

$0 \le x1 \le 100$

$40 \pi \le x2 \le 560 \pi$

$0 \le x3 \le 1$

$1 \le x4 \le 11$

The outputs are created according to the formula

$y = \mbox{atan}((x2 x3 - (1/(x2 x4)))/x1) + e$

where e is N(0,sd).

Usage

mlbench.friedman3(n, sd=0.1)

Arguments

n	number of patterns to create
sd	Standard deviation of noise. The default value of 0.1 gives a signal to noise ratio (i.e., the ratio of the standard deviations) of 3:1. Thus, the variance of the function itself (without noise) accounts for 90% of the total variance.

Value

Returns a list with components

x	input values (independent variables)
y	output values (dependent variable)

References

Breiman L (1996). “Bagging Predictors.” Machine Learning, 24(2), 123–140. doi:10.1023/a:1018054314350.

Friedman JH (1991). “Multivariate Adaptive Regression Splines.” The Annals of Statistics, 19(1), 1–67. doi:10.1214/aos/1176347963.

---

Corners of Hypercube

Description

The created data are d-dimensional spherical Gaussians with standard deviation sd and means at the corners of a d-dimensional hypercube. The number of classes is $2^d$.

Usage

mlbench.hypercube(n=800, d=3, sides=rep(1,d), sd=0.1)
hypercube(d)

Arguments

n	number of patterns to create
d	dimensionality of hypercube, default is 3
sides	lengths of the sides of the hypercube, default is to create a unit hypercube
sd	standard deviation

Value

Returns an object of class "mlbench.hypercube" with components

x	input values
classes	factor of length n with target classes

Examples

p <- mlbench.hypercube()
plot(p)

library("lattice")
cloud(x.3~x.1+x.2, groups=classes, data=as.data.frame(p))

---

Peak Benchmark Problem

Description

Let $r=3u$ where u is uniform on [0,1]. Take x to be uniformly distributed on the d-dimensional sphere of radius r. Let $y=25exp(-.5r^2)$. This data set is not a classification problem but a regression problem where y is the dependent variable.

Usage

mlbench.peak(n, d=20)

Arguments

n	number of patterns to create
d	dimension of the problem

Value

Returns a list with components

x	input values (independent variables)
y	output values (dependent variable)

---

Ringnorm Benchmark Problem

Description

The inputs of the ringnorm problem are points from two Gaussian distributions. Class 1 is multivariate normal with mean 0 and covariance 4 times the identity matrix. Class 2 has unit covariance and mean $(a,a,\ldots,a)$, $a=d^{-0.5}$.

Usage

mlbench.ringnorm(n, d=20)

Arguments

n	number of patterns to create
d	dimension of the ringnorm problem

Value

Returns an object of class "mlbench.ringnorm" with components

x	input values
classes	factor vector of length n with target classes

References

Breiman, L. (1996). Bias, variance, and arcing classifiers. Tech. Rep. 460, Statistics Department, University of California, Berkeley, CA, USA.

Examples

p<-mlbench.ringnorm(1000, d=2)
plot(p)

---

Shapes in 2d

Description

A Gaussian, square, triangle and wave in 2 dimensions.

Usage

mlbench.shapes(n=500)

Arguments

n	number of patterns to create

Value

Returns an object of class "mlbench.shapes" with components

x	input values
classes	factor of length n with target classes

Examples

p<-mlbench.shapes()
plot(p)

---

Corners of d-dimensional Simplex

Description

The created data are d-dimensional spherical Gaussians with standard deviation sd and means at the corners of a d-dimensional simplex. The number of classes is d+1.

Usage

mlbench.simplex(n = 800, d = 3, sides = 1, sd = 0.1, center=TRUE)
simplex(d, sides, center=TRUE)

Arguments

n	number of patterns to create
d	dimensionality of simplex, default is 3
sides	lengths of the sides of the simplex, default is to create a unit simplex
sd	standard deviation
center	If TRUE, the origin is the center of gravity of the simplex. If FALSE, the origin is a corner of the simplex and all coordinates of the simplex are positive.

Value

Returns an object of class "mlbench.simplex" with components

x	input values
classes	factor of length n with target classes

Author(s)

Manuel Eugster and Sebastian Kaiser

Examples

p <- mlbench.simplex()
plot(p)

library("lattice")
cloud(x.3~x.1+x.2, groups=classes, data=as.data.frame(p))

---

The Smiley

Description

The smiley consists of 2 Gaussian eyes, a trapezoid nose and a parabula mouth (with vertical Gaussian noise).

Usage

mlbench.smiley(n=500, sd1 = 0.1, sd2 = 0.05)

Arguments

n	number of patterns to create
sd1	standard deviation for eyes
sd2	standard deviation for mouth

Value

Returns an object of class "mlbench.smiley" with components

x	input values
classes	factor vector of length n with target classes

Examples

p<-mlbench.smiley()
plot(p)

---

Two Spirals Benchmark Problem

Description

The inputs of the spirals problem are points on two entangled spirals. If sd>0, then Gaussian noise is added to each data point. mlbench.1spiral creates a single spiral.

Usage

mlbench.spirals(n, cycles=1, sd=0)
mlbench.1spiral(n, cycles=1, sd=0)

Arguments

n	number of patterns to create
cycles	the number of cycles each spiral makes
sd	standard deviation of data points around the spirals

Value

Returns an object of class "mlbench.spirals" with components

x	input values
classes	factor vector of length n with target classes

Examples

# 1 cycle each, no noise
p<-mlbench.spirals(300)
plot(p)
#
# 1.5 cycles each, with noise
p<-mlbench.spirals(300,1.5,0.05)
plot(p)

---

Threenorm Benchmark Problem

Description

The inputs of the threenorm problem are points from two Gaussian distributions with unit covariance matrix. Class 1 is drawn with equal probability from a unit multivariate normal with mean $(a,a,\ldots,a)$ and from a unit multivariate normal with mean $(-a,-a,\ldots,-a)$. Class 2 is drawn from a multivariate normal with mean at $(a,-a,a, \ldots,-a)$, $a=2/d^{0.5}$.

Usage

mlbench.threenorm(n, d=20)

Arguments

n	number of patterns to create
d	dimension of the threenorm problem

Value

Returns an object of class "mlbench.threenorm" with components

x	input values
classes	factor vector of length n with target classes

References

Breiman L (1996). “Bias, Variance, and Arcing Classifiers.” Technical Report 460, Statistics Department, University of California, Berkeley. https://www.stat.berkeley.edu/~breiman/arcall96.pdf.

Examples

p<-mlbench.threenorm(1000, d=2)
plot(p)

---

Twonorm Benchmark Problem

Description

The inputs of the twonorm problem are points from two Gaussian distributions with unit covariance matrix. Class 1 is multivariate normal with mean $(a,a,\ldots,a)$ and class 2 with mean $(-a,-a,\ldots,-a)$, $a=2/d^{0.5}$.

Usage

mlbench.twonorm(n, d=20)

Arguments

n	number of patterns to create
d	dimension of the twonorm problem

Value

Returns an object of class "mlbench.twonorm" with components

x	input values
classes	factor vector of length n with target classes

References

Breiman, L. (1996). Bias, variance, and arcing classifiers. Tech. Rep. 460, Statistics Department, University of California, Berkeley, CA, USA.

Examples

p<-mlbench.twonorm(1000, d=2)
plot(p)

---

Waveform Database Generator

Description

The generated data set consists of 21 attributes with continuous values and a variable showing the 3 classes (33% for each of 3 classes). Each class is generated from a combination of 2 of 3 "base" waves.

Usage

mlbench.waveform(n)

Arguments

n	number of patterns to create

Value

Returns an object of class "mlbench.waveform" with components

x	input values
classes	factor vector of length n with target classes

Source

The original C code for the waveform generator was taken from the UCI Repository of Machine Learning Databases (Blake and Merz 1998). The C code was modified to use R's random number generator by Friedrich Leisch, who also wrote the R interface.

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Breiman L (1996). “Bias, Variance, and Arcing Classifiers.” Technical Report 460, Statistics Department, University of California, Berkeley. https://www.stat.berkeley.edu/~breiman/arcall96.pdf.

Examples

p <- mlbench.waveform(100)
plot(p)

---

Continuous XOR Benchmark Problem

Description

The inputs of the XOR problem are uniformly distributed on the d-dimensional cube with corners $\{\pm 1\}$. Each pair of opposite corners form one class, hence the total number of classes is $2^(d-1)$

Usage

mlbench.xor(n, d=2)

Arguments

n	number of patterns to create
d	dimension of the XOR problem

Value

Returns an object of class "mlbench.xor" with components

x	input values
classes	factor vector of length n with target classes

Examples

# 2d example
p<-mlbench.xor(300,2)
plot(p)
#
# 3d example
p<-mlbench.xor(300,3)
plot(p)

---

Los Angeles ozone pollution data, 1976

Description

A data frame with 366 observations on 13 variables, each observation is one day

Usage

data("Ozone", package = "mlbench")

Format

1	Month: 1 = January, ..., 12 = December
2	Day of month
3	Day of week: 1 = Monday, ..., 7 = Sunday
4	Daily maximum one-hour-average ozone reading
5	500 millibar pressure height (m) measured at Vandenberg AFB
6	Wind speed (mph) at Los Angeles International Airport (LAX)
7	Humidity (%) at LAX
8	Temperature (degrees F) measured at Sandburg, CA
9	Temperature (degrees F) measured at El Monte, CA
10	Inversion base height (feet) at LAX
11	Pressure gradient (mm Hg) from LAX to Daggett, CA
12	Inversion base temperature (degrees F) at LAX
13	Visibility (miles) measured at LAX

Details

The problem is to predict the daily maximum one-hour-average ozone reading (V4).

Source

Data used in

Breiman L, Friedman JH (1985). “Estimating Optimal Transformations for Multiple Regression and Correlation.” Journal of the American Statistical Association, 80(391), 580–598. doi:10.1080/01621459.1985.10478157. https://doi.org/10.1080/01621459.1985.10478157, https://doi.org/10.1080/01621459.1985.10478157.

Examples

data("Ozone", package = "mlbench")
summary(Ozone)

---

Pima Indians Diabetes Database

Description

A data frame with 768 observations on 9 variables.

Usage

data("PimaIndiansDiabetes", package = "mlbench")
data("PimaIndiansDiabetes2", package = "mlbench")

Format

pregnant	Number of times pregnant
glucose	Plasma glucose concentration (glucose tolerance test)
pressure	Diastolic blood pressure (mm Hg)
triceps	Triceps skin fold thickness (mm)
insulin	2-Hour serum insulin (mu U/ml)
mass	Body mass index
pedigree	Diabetes pedigree function
age	Age (years)
diabetes	Class variable (test for diabetes)

Details

The data set PimaIndiansDiabetes2 contains a corrected version of the original data set. While the UCI repository index claims that there are no missing values, closer inspection of the data shows several physical impossibilities, e.g., blood pressure or body mass index of 0. In PimaIndiansDiabetes2, all zero values of glucose, pressure, triceps, insulin and mass have been set to NA, see also Wahba, Gu, Wang, and Chappell (1995) and Ripley (1996).

Source

• Original owners: National Institute of Diabetes and Digestive and Kidney Diseases

• Donor of database: Vincent Sigillito ([email protected])

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Friedrich Leisch in the late 1990s.

The data no longer seems to be available from the UC Irvine Machine Learning Repository (now at https://archive.ics.uci.edu/).

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Ripley BD (1996). Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge. doi:10.1017/CBO9780511812651.

Wahba G, Gu C, Wang Y, Chappell R (1995). “Chapter Soft Classification, a.k.a. Risk Estimation, via Penalized Log Likelihood and Smoothing Spline Analysis of Variance.” In Wolpert DH (ed.), The Mathematics of Generalization, 331–359. Addison-Wesley, Reading, MA.

Examples

data("PimaIndiansDiabetes", package = "mlbench")
summary(PimaIndiansDiabetes)

data("PimaIndiansDiabetes2", package = "mlbench")
summary(PimaIndiansDiabetes2)

---

Plot mlbench objects

Description

Plots the data of an mlbench object using different colors for each class. If the dimension of the input space is larger that 2, a scatter plot matrix is used.

Usage

## S3 method for class 'mlbench'
plot(x, xlab="", ylab="", ...)

Arguments

x	Object of class "mlbench".
xlab	Label for x-axis.
ylab	Label for y-axis.
...	Further plotting options.

Examples

# 6 normal classes
p <- mlbench.2dnormals(500,6)
plot(p)

# 4-dimensiona XOR
p <- mlbench.xor(500,4)
plot(p)

---

Landsat Multi-Spectral Scanner Image Data

Description

The database consists of the multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood. The aim is to predict this classification, given the multi-spectral values.

Usage

data("Satellite", package = "mlbench")

Format

A data frame with 36 inputs (x.1 ... x.36) and one target (classes).

Details

One frame of Landsat MSS imagery consists of four digital images of the same scene in different spectral bands. Two of these are in the visible region (corresponding approximately to green and red regions of the visible spectrum) and two are in the (near) infra-red. Each pixel is a 8-bit binary word, with 0 corresponding to black and 255 to white. The spatial resolution of a pixel is about 80m x 80m. Each image contains 2340 x 3380 such pixels.

The database is a (tiny) sub-area of a scene, consisting of 82 x 100 pixels. Each line of data corresponds to a 3x3 square neighbourhood of pixels completely contained within the 82x100 sub-area. Each line contains the pixel values in the four spectral bands (converted to ASCII) of each of the 9 pixels in the 3x3 neighbourhood and a number indicating the classification label of the central pixel.

The classes are

red soil

cotton crop

grey soil

damp grey soil

soil with vegetation stubble

very damp grey soil

The data is given in random order and certain lines of data have been removed so you cannot reconstruct the original image from this dataset.

In each line of data the four spectral values for the top-left pixel are given first followed by the four spectral values for the top-middle pixel and then those for the top-right pixel, and so on with the pixels read out in sequence left-to-right and top-to-bottom. Thus, the four spectral values for the central pixel are given by attributes 17,18,19 and 20. If you like you can use only these four attributes, while ignoring the others. This avoids the problem which arises when a 3x3 neighbourhood straddles a boundary.

Origin

The original Landsat data for this database was generated from data purchased from NASA by the Australian Centre for Remote Sensing, and used for research at: The Centre for Remote Sensing, University of New South Wales, Kensington, PO Box 1, NSW 2033, Australia.

The sample database was generated taking a small section (82 rows and 100 columns) from the original data. The binary values were converted to their present ASCII form by Ashwin Srinivasan. The classification for each pixel was performed on the basis of an actual site visit by Ms. Karen Hall, when working for Professor John A. Richards, at the Centre for Remote Sensing at the University of New South Wales, Australia. Conversion to 3x3 neighbourhoods and splitting into test and training sets was done by Alistair Sutherland.

History

The Landsat satellite data is one of the many sources of information available for a scene. The interpretation of a scene by integrating spatial data of diverse types and resolutions including multispectral and radar data, maps indicating topography, land use etc. is expected to assume significant importance with the onset of an era characterised by integrative approaches to remote sensing (for example, NASA's Earth Observing System commencing this decade). Existing statistical methods are ill-equipped for handling such diverse data types. Note that this is not true for Landsat MSS data considered in isolation (as in this sample database). This data satisfies the important requirements of being numerical and at a single resolution, and standard maximum-likelihood classification performs very well. Consequently, for this data, it should be interesting to compare the performance of other methods against the statistical approach.

Source

Ashwin Srinivasan, Department of Statistics and Data Modeling, University of Strathclyde, Glasgow, Scotland, UK, [email protected]

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Friedrich Leisch in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Statlog Landsat Satellite data set is available from doi:10.24432/C55887.

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Examples

data("Satellite", package = "mlbench")
summary(Satellite)

---

Servo Data

Description

This data set is 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. The variables that describe the data set and their values are the following:

[,1]	Motor	A,B,C,D,E
[,2]	Screw	A,B,C,D,E
[,3]	Pgain	3,4,5,6
[,4]	Vgain	1,2,3,4,5
[,5]	Class	0.13 to 7.10

Usage

data("Servo", package = "mlbench")

Format

A data frame with 167 observations on 5 variables, 4 nominal and 1 as the target class.

Source

• Creator: Karl Ulrich (MIT) in 1986

• Donor: Ross Quinlan

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Evgenia Dimitriadou in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Servo data set is available from doi:10.24432/C5Q30F.

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Quinlan JR (1993). “Combining Instance-Based and Model-Based Learning.” In Proceedings of the Tenth International Conference on International Conference on Machine Learning, series ICML'93, 236–243. ISBN 1558603077.

Quinlan R (1992). “Learning with Continuous Classes.” In Proceedings of the Australian Joint Conference on Artificial Intelligence, 343–348. World Scientific, Singapore.

Examples

data("Servo", package = "mlbench")
summary(Servo)

---

Shuttle Dataset (Statlog version)

Description

The shuttle dataset contains 9 attributes all of which are numerical with the first one being time. The last column is the class with the following 7 levels: Rad.Flow, Fpv.Close, Fpv.Open, High, Bypass, Bpv.Close, Bpv.Open.

Approximately 80% of the data belongs to class 1. Therefore the default accuracy is about 80%. The aim here is to obtain an accuracy of 99 - 99.9%.

Usage

data("Shuttle", package = "mlbench")

Format

A data frame with 58,000 observations on 9 numerical independent variables and 1 target class.

Source

• Source: Jason Catlett of Basser Department of Computer Science; University of Sydney; N.S.W.; Australia.

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Evgenia Dimitriadou in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Statlog Shuttle data set is available from doi:10.24432/C5WS31.

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Examples

data("Shuttle", package = "mlbench")
summary(Shuttle)

---

Sonar, Mines vs. Rocks

Description

This is the data set used by Gorman and Sejnowski (1988) in their study of the classification of sonar signals using a neural network. 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.

Each pattern is a set of 60 numbers in the range 0.0 to 1.0. Each number represents the energy within a particular frequency band, integrated over a certain period of time. The integration aperture for higher frequencies occur later in time, since these frequencies are transmitted later during the chirp.

The label associated with each record contains the letter "R" if the object is a rock and "M" if it is a mine (metal cylinder). The numbers in the labels are in increasing order of aspect angle, but they do not encode the angle directly.

Usage

data("Sonar", package = "mlbench")

Format

A data frame with 208 observations on 61 variables, all numerical and one (the Class) nominal.

Source

• Contribution: Terry Sejnowski, Salk Institute and University of California, San Deigo.

• Development: R. Paul Gorman, Allied-Signal Aerospace Technology Center.

• Maintainer: Scott E. Fahlman

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Evgenia Dimitriadou in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Connectionist Bench Sonar, Mines vs. Rocks data set is available from doi:10.24432/C5T01Q.

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Gorman RP, Sejnowski TJ (1988). “Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets.” Neural Networks, 1(1), 75–89. ISSN 0893-6080. doi:10.1016/0893-6080(88)90023-8.

Examples

data("Sonar", package = "mlbench")
summary(Sonar)

---

Soybean Database

Description

There are 19 classes, only the first 15 of which have been used in prior work. The folklore seems to be that the last four classes are unjustified by the data since they have so few examples. There are 35 categorical attributes, some nominal and some ordered. The value “dna” means does not apply. The values for attributes are encoded numerically, with the first value encoded as “0,” the second as “1,” and so forth.

Usage

data("Soybean", package = "mlbench")

Format

A data frame with 683 observations on 36 variables. There are 35 categorical attributes, all numerical and a nominal denoting the class.

[,1]	Class	the 19 classes
[,2]	date	apr(0),may(1),june(2),july(3),aug(4),sept(5),oct(6).
[,3]	plant.stand	normal(0),lt-normal(1).
[,4]	precip	lt-norm(0),norm(1),gt-norm(2).
[,5]	temp	lt-norm(0),norm(1),gt-norm(2).
[,6]	hail	yes(0),no(1).
[,7]	crop.hist	dif-lst-yr(0),s-l-y(1),s-l-2-y(2), s-l-7-y(3).
[,8]	area.dam	scatter(0),low-area(1),upper-ar(2),whole-field(3).
[,9]	sever	minor(0),pot-severe(1),severe(2).
[,10]	seed.tmt	none(0),fungicide(1),other(2).
[,11]	germ	90-100%(0),80-89%(1),lt-80%(2).
[,12]	plant.growth	norm(0),abnorm(1).
[,13]	leaves	norm(0),abnorm(1).
[,14]	leaf.halo	absent(0),yellow-halos(1),no-yellow-halos(2).
[,15]	leaf.marg	w-s-marg(0),no-w-s-marg(1),dna(2).
[,16]	leaf.size	lt-1/8(0),gt-1/8(1),dna(2).
[,17]	leaf.shread	absent(0),present(1).
[,18]	leaf.malf	absent(0),present(1).
[,19]	leaf.mild	absent(0),upper-surf(1),lower-surf(2).
[,20]	stem	norm(0),abnorm(1).
[,21]	lodging	yes(0),no(1).
[,22]	stem.cankers	absent(0),below-soil(1),above-s(2),ab-sec-nde(3).
[,23]	canker.lesion	dna(0),brown(1),dk-brown-blk(2),tan(3).
[,24]	fruiting.bodies	absent(0),present(1).
[,25]	ext.decay	absent(0),firm-and-dry(1),watery(2).
[,26]	mycelium	absent(0),present(1).
[,27]	int.discolor	none(0),brown(1),black(2).
[,28]	sclerotia	absent(0),present(1).
[,29]	fruit.pods	norm(0),diseased(1),few-present(2),dna(3).
[,30]	fruit.spots	absent(0),col(1),br-w/blk-speck(2),distort(3),dna(4).
[,31]	seed	norm(0),abnorm(1).
[,32]	mold.growth	absent(0),present(1).
[,33]	seed.discolor	absent(0),present(1).
[,34]	seed.size	norm(0),lt-norm(1).
[,35]	shriveling	absent(0),present(1).
[,36]	roots	norm(0),rotted(1),galls-cysts(2).

Source

• Source: Michalski and Chilausky (1980)

• Donor: Ming Tan & Jeff Schlimmer (Jeff.Schlimmer%cs.cmu.edu)

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Evgenia Dimitriadou in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Soybean (Large) data set is available from doi:10.24432/C5JG6Z.

References

Michalski RS, Chilausky RL (1980). “Learning by Being Told and Learning from Examples: An Experimental Comparison of the Two Methods for Knowledge Acquisition in the Context of Developing an Expert System for Soybean Disease Diagnosis.” International Journal of Policy Analysis and Information Systems, 4(2), 125–161.

Tan M, Eshelman L (1988). “Using Weighted Networks to Represent Classification Knowledge in Noisy Domains.” In Laird J (ed.), Machine Learning Proceedings 1988, 121-134. Morgan Kaufmann, San Francisco (CA). ISBN 978-0-934613-64-4. doi:10.1016/B978-0-934613-64-4.50018-9.
– IWN recorded a 97.1% classification accuracy
– 290 training and 340 test instances

Fisher DH, Schlimmer JC (1988). “Concept Simplification and Prediction Accuracy.” In Laird J (ed.), Machine Learning Proceedings 1988, 22–28. Morgan Kaufmann, San Francisco (CA). ISBN 978-0-934613-64-4. doi:10.1016/B978-0-934613-64-4.50007-4.
– Notes why this database is highly predictable

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Examples

data("Soybean", package = "mlbench")
summary(Soybean)

---

Vehicle Silhouettes

Description

The purpose is to classify a given silhouette as one of four types of vehicle, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles. The features were extracted from the silhouettes by the HIPS (Hierarchical Image Processing System) extension BINATTS, which extracts a combination of scale independent features utilising both classical moments based measures such as scaled variance, skewness and kurtosis about the major/minor axes and heuristic measures such as hollows, circularity, rectangularity and compactness.

Four "Corgie" model vehicles were used for the experiment: a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400. This particular combination of vehicles was chosen with the expectation that the bus, van and either one of the cars would be readily distinguishable, but it would be more difficult to distinguish between the cars.

Usage

data("Vehicle", package = "mlbench")

Format

A data frame with 846 observations on 19 variables, all numerical and one nominal defining the class of the objects.

[,1]	Comp	Compactness
[,2]	Circ	Circularity
[,3]	D.Circ	Distance Circularity
[,4]	Rad.Ra	Radius ratio
[,5]	Pr.Axis.Ra	pr.axis aspect ratio
[,6]	Max.L.Ra	max.length aspect ratio
[,7]	Scat.Ra	scatter ratio
[,8]	Elong	elongatedness
[,9]	Pr.Axis.Rect	pr.axis rectangularity
[,10]	Max.L.Rect	max.length rectangularity
[,11]	Sc.Var.Maxis	scaled variance along major axis
[,12]	Sc.Var.maxis	scaled variance along minor axis
[,13]	Ra.Gyr	scaled radius of gyration
[,14]	Skew.Maxis	skewness about major axis
[,15]	Skew.maxis	skewness about minor axis
[,16]	Kurt.maxis	kurtosis about minor axis
[,17]	Kurt.Maxis	kurtosis about major axis
[,18]	Holl.Ra	hollows ratio
[,19]	Class	type

Source

• Creator: Drs. Pete Mowforth and Barry Shepherd, Turing Institute, Glasgow, Scotland.

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Evgenia Dimitriadou in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Statlog Vehicle Silhouettes data set is available from doi:10.24432/C5HG6N.

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Siebert P (1987). “Vehicle Recognition Using Rule Based Methods.” Turing Institute Research Memorandum TIRM-87-018.

Examples

data("Vehicle", package = "mlbench")
summary(Vehicle)

---

Vowel Recognition (Deterding data)

Description

Speaker independent recognition of the eleven steady state vowels of British English using a specified training set of lpc derived log area ratios. The vowels are indexed by integers 0-10. For each utterance, there are ten floating-point input values, with array indices 0-9. The vowels are the following: hid, hId, hEd, hAd, hYd, had, hOd, hod, hUd, hud, hed.

Usage

data("Vowel", package = "mlbench")

Format

A data frame with 990 observations on 10 independent variables, one nominal and the other numerical, and 1 as the target class.

Source

• Creator: Tony Robinson

• Maintainer: Scott E. Fahlman, CMU

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Evgenia Dimitriadou in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Connectionist Bench Vowel Recognition Deterding Data data set is available from doi:10.24432/C58P4S.

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Deterding D (1990). Speaker Normalisation for Automatic Speech Recognition. Ph.D. thesis, University of Cambridge.

Niranjan M, Fallside F (1990). “Neural Networks and Radial Basis Functions in Classifying Static Speech Patterns.” Computer Speech & Language, 4(3), 275–289. ISSN 0885-2308. doi:10.1016/0885-2308(90)90009-U.

Renals S, Rohwer R (1989). “Phoneme Classification Experiments Using Radial Basis Functions.” In International 1989 Joint Conference on Neural Networks, volume 1, 461–467. doi:10.1109/IJCNN.1989.118620.

Examples

data("Vowel", package = "mlbench")
summary(Vowel)

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Zoo Data

Description

A simple dataset containing 17 (mostly logical) variables on 101 animals.

Usage

data("Zoo", package = "mlbench")

Format

A data frame with 17 columns: hair, feathers, eggs, milk, airborne, aquatic, predator, toothed, backbone, breathes, venomous, fins, legs, tail, domestic, catsize, type.

Most variables are logical and indicate whether the corresponding animal has the corresponsing characteristic or not. The only 2 exceptions are: legs takes values 0, 2, 4, 5, 6, and 8. type is a grouping of the animals into 7 groups, see the example section for the detailed list.

Details

Ask the original donor of the data why girl is an animal.

Source

The original data have been donated by Richard S. Forsyth to the UCI Repository of Machine Learning Databases (Blake and Merz 1998) and were converted to R format by Friedrich Leisch and Evgenia Dimitriadou in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Zoo data set is available from doi:10.24432/C5R59V.

References

Blake CL, Merz CJ (1998). “UCI Repository of Machine Learning Databases.” University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠http://www.ics.uci.edu/~mlearn/MLRepository.html⁠’.

Examples

data("Zoo", package = "mlbench")
summary(Zoo)

## see the annimals grouped by type
split(rownames(Zoo), Zoo$type)

## which animals have fins?
rownames(Zoo)[Zoo$fins]

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