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

In hybridEnsemble: Build, Deploy and Evaluate Hybrid Ensembles

Summarize the performance of the cross-validated Hybrid Ensemble

Description

This function produces summary results per combination method.

Usage

## S3 method for class '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

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.

Author(s)

Michel Ballings, Dauwe Vercamer, Matthias Bogaert, and Dirk Van den Poel, Maintainer: [e-mail]

References

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

See Also

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

Examples

data(Credit)

## Not run: 
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) )


## End(Not run)

hybridEnsemble documentation built on April 1, 2023, 12:13 a.m.