Credit approval (Frank and Asuncion, 2010)
Description
Credit contains credit card applications. The dataset has a good mix of continuous and categorical features.
Usage
data(Credit)Format
A data frame with 653 observations, 15 predictors and a binary criterion variable called Response
Details
All observations with missing values are deleted.
Source
Frank, A. and Asuncion, A. (2010). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
References
The original dataset can be downloaded at http://archive.ics.uci.edu/ml/datasets/Credit+Approval
Examples
data(Credit)
str(Credit)
table(Credit$Response)
Display the NEWS file
Description
kFNews shows the NEWS file of the kernelFactory package.
Usage
kFNews()
Value
None.
Author(s)
Authors: Michel Ballings and Dirk Van den Poel, Maintainer: Michel.Ballings@GMail.com
References
Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913.
See Also
kernelFactory , predict.kernelFactory
Examples
kFNews()
Binary classification with Kernel Factory
Description
kernelFactory implements an ensemble method for kernel machines (Ballings and Van den Poel, 2013).
Usage
kernelFactory(x = NULL, y = NULL, cp = 1, rp = round(log(nrow(x), 10)),
method = "burn", ntree = 500, filter = 0.01, popSize = rp * cp * 7,
iters = 80, mutationChance = 1/(rp * cp), elitism = max(1, round((rp *
cp) * 0.05)), oversample = TRUE)
Arguments
x
A data frame of predictors (numeric, integer or factor). Categorical variables need to be factors. Indicator values should not be too imbalanced because this might produce constants in the subsetting process.
y
A factor containing the response vector. Only {0,1} is allowed.
cp
The number of column partitions.
rp
The number of row partitions.
method
Can be one of the following: POLynomial kernel function (pol), LINear kernel function (lin), Radial Basis kernel Function rbf), random choice (random=pol, lin, rbf) (random), burn- in choice of best function (burn=pol, lin, rbf ) (burn). Use random or burn if you don't know in advance which kernel function is best.
ntree
Number of trees in the Random Forest base classifiers.
filter
either NULL (deactivate) or a percentage denoting the minimum class size of dummy predictors. This parameter is used to remove near constants. For example if nrow(xTRAIN)=100, and filter=0.01 then all dummy predictors with any class size equal to 1 will be removed. Set this higher (e.g., 0.05 or 0.10) in case of errors.
popSize
Population size of the genetic algorithm.
iters
Number of generations of the genetic algorithm.
mutationChance
Mutationchance of the genetic algorithm.
elitism
Elitism parameter of the genetic algorithm.
oversample
Oversample the smallest class. This helps avoid problems related to the subsetting procedure (e.g., if rp is too high).
Value
An object of class kernelFactory, which is a list with the following elements:
trn
Training data set.
trnlst
List of training partitions.
rbfstre
List of used kernel functions.
rbfmtrX
List of augmented kernel matrices.
rsltsKF
List of models.
cpr
Number of column partitions.
rpr
Number of row partitions.
cntr
Number of partitions.
wghts
Weights of the ensemble members.
nmDtrn
Vector indicating the numeric (and integer) features.
rngs
Ranges of numeric predictors.
constants
To exclude from newdata.
Author(s)
Authors: Michel Ballings and Dirk Van den Poel, Maintainer: Michel.Ballings@GMail.com
References
Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913.
See Also
Examples
#Credit Approval data available at UCI Machine Learning Repository
data(Credit)
#take subset (for the purpose of a quick example) and train and test
Credit <- Credit[1:100,]
train.ind <- sample(nrow(Credit),round(0.5*nrow(Credit)))
#Train Kernel Factory on training data
kFmodel <- kernelFactory(x=Credit[train.ind,names(Credit)!= "Response"],
y=Credit[train.ind,"Response"], method=random)
#Deploy Kernel Factory to predict response for test data
#predictedresponse <- predict(kFmodel, newdata=Credit[-train.ind,names(Credit)!= "Response"])
Predict method for kernelFactory objects
Description
Prediction of new data using kernelFactory.
Usage
## S3 method for class 'kernelFactory'
predict(object, newdata = NULL, predict.all = FALSE,
...)
Arguments
object
An object of class kernelFactory, as created by the function kernelFactory
newdata
A data frame with the same predictors as in the training data.
predict.all
TRUE or FALSE. If TRUE and rp and cp are 1 then the individual predictions of the random forest are returned. If TRUE and any of rp and cp or bigger than 1 then the predictions of all the members are returned.
...
Not used currently.
Value
A vector containing the response probabilities.
Author(s)
Authors: Michel Ballings and Dirk Van den Poel, Maintainer: Michel.Ballings@GMail.com
References
Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913.
See Also
Examples
#Credit Approval data available at UCI Machine Learning Repository
data(Credit)
#take subset (for the purpose of a quick example) and train and test
Credit <- Credit[1:100,]
train.ind <- sample(nrow(Credit),round(0.5*nrow(Credit)))
#Train Kernel Factory on training data
kFmodel <- kernelFactory(x=Credit[train.ind,names(Credit)!= "Response"],
y=Credit[train.ind,"Response"], method=random)
#Deploy Kernel Factory to predict response for test data
predictedresponse <- predict(kFmodel, newdata=Credit[-train.ind,names(Credit)!= "Response"])