sota.svm
sota.svm.Rdsota.svm is a wrapper that executes svm using the same framework as for the predomics package.
Usage
sota.svm(
sparsity = c(1:30),
objective = "auc",
max.nb.features = 1000,
intercept = 0,
language = "svm",
evalToFit = "auc_",
k_penalty = 0,
scaled = TRUE,
type = NULL,
kernel = "rbfdot",
kpar = "automatic",
C = c(1e-04, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000),
nu = 0.2,
epsilon.hp = 0.1,
prob.model = FALSE,
class.weights = NULL,
fit = TRUE,
cache = 40,
tol = 0.001,
shrinking = TRUE,
na.action = na.omit,
popSaveFile = "NULL",
seed = "NULL",
nCores = 4,
verbose = TRUE,
plot = FALSE,
warnings = FALSE,
debug = FALSE,
print_ind_method = "short",
experiment.id = NULL,
experiment.description = NULL,
experiment.save = "nothing"
)Arguments
- language
is the language that is used by the different algorithms bin, bininter, ter, terinter, ratio, (default:"sota")
- sparsity:
number of features in a given model. This is a vector with multiple lengths.
- objective:
prediction mode (default: auc)
- max.nb.features:
create the glmnet object using only the top most significant features (default:1000)
- intercept:
(Interceot for the a given model) (default:NULL)
- evalToFit:
Which model property will be used to select the best model among different k_sparsities (default: auc_)
- k_penalty:
Penalization of the fit by the k_sparsity (default: 0)
- scaled:
??
- type:
??
- kernel:
??
- kpar:
??
- C:
(??)
- nu:
??
- epsilon.hp:
(??) (for the SVM)
- prob.model:
??
- class.weights:
??
- fit:
??
- cache:
(??)
- tol:
??
- shrinking:
??
- na.action:
??
- popSaveFile:
(??)
- seed:
the seed to be used for reproductibility. If seed=NULL than it is not taken into account (default:NULL).
- nCores:
the number of CPUs to run the program in parallel
- plot:
Plot graphics indicating the evolution of the simulation (default:FALSE)
- verbose:
print out information on the progress of the algorithm (default:TRUE)
- warnings:
Print out warnings when runnig (default:FALSE).
- debug:
print out information on the progress of the algorithm (default:FALSE)
- print_ind_method:
One of c("short","graphical") indicates how to print a model and subsequently a population during the run (default:"short").
- experiment.id:
The id of the experiment that is to be used in the plots and comparitive analyses (default is the learner's name, when not specified)
- experiment.description:
A longer description of the experiment. This is important when many experiments are run and can also be printed in by the printExperiment function.
- experiment.save:
Data from an experiment can be saved with different levels of completness, with options to be selected from c("nothing", "minimal", "full"), default is "minimal"