hybridEnsemble (version 1.7.9)

summary.CVhybridEnsemble: Summarize the performance of the cross-validated Hybrid Ensemble

Description

This function produces summary results per combination method.

Usage

# S3 method for CVhybridEnsemble
summary(
  object,
  name = "",
  stat = "median",
  LateX = FALSE,
  toppart = FALSE,
  bottompart = FALSE,
  all = TRUE,
  ...
)

Arguments

object

An object of class CVhybridEnsemble

name

Name of the dataset. Default is blank.

stat

'median' or 'IQR' (inter quartile range) of the performance measure used in the CVhybridEnsemble object

LateX

TRUE or FALSE. If true LateX code is printed to the screen. Otherwise a data frame.

toppart

TRUE or FALSE. For the LateX table. Should the top part of the table be printed. Useful for concatenating multiple runs of the summary function (see examples).

bottompart

TRUE or FALSE. For the LateX table. Should the bottom part of the table be printed. Useful for concatenating multiple runs of the summary function (see examples). The all parameter will not have effect when setting this to TRUE.

all

TRUE or FALSE. Should the results of the predict.all be printed along with the main results? Works only when predict.all=TRUE in the call to CVhybridEnsemble.

...

Not used

Author

Michel Ballings, Dauwe Vercamer, Matthias Bogaert, and Dirk Van den Poel, Maintainer:

Details

In the output: 'RBGA' (Genetic Algorithm), 'DEOPT' (Differential Evolution), 'GENSA' (Generalized Simulated Annealing), 'MALSCHAINS' (Memetic Algorithm), 'PSOPTIM' (Particle Swarm), 'SOMA' (Self Organizing Migrating Algorithm), 'TABU' (Tabue Search), 'LHNNLS' (Lawson-Hanson Non-negative least squares), 'GINNLS' (Goldfarb-Idnani Non-negative least squares), 'NNloglik' (Non-negative binomial likelihood), 'MEAN' (Simple Mean), 'SB' (Single Best), 'AUTHORITY' (Authority Based method). SB names denote the single best for all cross-validation runs: RF= Random Forest, SV= Bagged Support Vector Machines, KF= Kernel Factory, AB=AdaBoost, LR=Bagged Logistic Regression, NN=Bagged Neural Networks, RoF= Rotation Forest, KN= K-Nearest Neighbors.

References

Ballings, M., Vercamer, D., Bogaert, M., Van den Poel, D.

See Also

hybridEnsemble, predict.hybridEnsemble, importance.hybridEnsemble, CVhybridEnsemble, plot.CVhybridEnsemble

Examples

data(Credit)

if (FALSE) {
CVhE <- CVhybridEnsemble(x=Credit[1:200,names(Credit) != 'Response'],
                    y=Credit$Response[1:200],
                    verbose=TRUE,
                    RF.ntree=50,
                    KF.rp=1,
                    AB.iter=50,
                    NN.size=5,
                    NN.decay=0,
                    SV.gamma = 2^-15,
                    SV.cost = 2^-5,
                    SV.degree=2,
                    SV.kernel='radial')

summary(object=CVhE,stat='median')
summary(object=CVhE,stat='IQR')

#LaTeX table
#This code example shows how toppart and bottompart can be convenient if you want 
#to concatenate multiple datasets (here six time the same dataset).
#Paste the output of this code in your LateX document:
cat(
 summary(object=CVhE ,name="Credit", LateX=TRUE, toppart=TRUE),
 summary(object=CVhE ,name="Credit", LateX=TRUE),
 summary(object=CVhE, name="Credit", LateX=TRUE),
 summary(object=CVhE ,name="Credit", LateX=TRUE),
 summary(object=CVhE ,name="Credit", LateX=TRUE),
 summary(object=CVhE ,name="Credit", LateX=TRUE, bottompart=TRUE) )

}