| Type: | Package |
| Title: | Fit and Deploy Rotation Forest Models |
| Version: | 0.1.3 |
| Date: | 2017-04-16 |
| Imports: | rpart |
| Author: | Michel Ballings and Dirk Van den Poel |
| Maintainer: | Michel Ballings <michel.ballings@gmail.com> |
| Description: | Fit and deploy rotation forest models ("Rodriguez, J.J., Kuncheva, L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1619-1630 <doi:10.1109/TPAMI.2006.211>") for binary classification. Rotation forest is an ensemble method where each base classifier (tree) is fit on the principal components of the variables of random partitions of the feature set. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2017-04-16 15:53:48 UTC; mballin2 |
| Repository: | CRAN |
| Date/Publication: | 2017-04-16 16:49:11 UTC |
Predict method for rotationForest objects
Description
Prediction of new data using rotationForest.
Usage
## S3 method for class 'rotationForest'
predict(object, newdata, all = FALSE, ...)
Arguments
object |
An object of class |
newdata |
A data frame with the same predictors as in the training data. |
all |
Return the predictions per tree instead of the average. |
... |
Not used currently. |
Value
A vector containing the response scores.
Author(s)
Michel Ballings and Dirk Van den Poel, Maintainer: Michel.Ballings@GMail.com
References
Rodriguez, J.J., Kuncheva, L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1619-1630. doi:10.1109/TPAMI.2006.211
See Also
Examples
data(iris)
y <- as.factor(ifelse(iris$Species[1:100]=="setosa",0,1))
x <- iris[1:100,-5]
rF <- rotationForest(x,y)
predict(object=rF,newdata=x)
Binary classification with Rotation Forest (Rodriguez en Kuncheva, 2006)
Description
rotationForest implements an ensemble method where each base classifier (tree) is fit on the principal components of the variables of random partitions of the feature set.
Usage
rotationForest(x, y, K = round(ncol(x)/3, 0), L = 10, verbose = FALSE,
...)
Arguments
x |
A data frame of predictors (numeric, or integer). Categorical variables need to be transformed to indicator (dummy) variables. At minimum |
y |
A factor containing the response vector. Only {0,1} is allowed. |
K |
The number of variable subsets. The default is the value |
L |
The number of base classifiers (trees using the |
verbose |
Boolean. Should information about the subsets be printed? |
... |
Arguments to |
Value
An object of class rotationForest, which is a list with the following elements:
models |
A list of trees. |
loadings |
A list of loadings. |
columnnames |
Column names of x. |
Author(s)
Michel Ballings and Dirk Van den Poel, Maintainer: Michel.Ballings@GMail.com
References
Rodriguez, J.J., Kuncheva, L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1619-1630. doi:10.1109/TPAMI.2006.211
See Also
Examples
data(iris)
y <- as.factor(ifelse(iris$Species[1:100]=="setosa",0,1))
x <- iris[1:100,-5]
rF <- rotationForest(x,y)
predict(object=rF,newdata=x)
Display the NEWS file
Description
rotationForestNews shows the NEWS file of the rotationForest package.
Usage
rotationForestNews()
Author(s)
Michel Ballings and Dirk Van den Poel, Maintainer: Michel.Ballings@GMail.com
Examples
rotationForestNews()