terda | R Documentation |
terbeam is a model search algorithm.
terda(
sparsity = 5,
nIterations = 5,
max.nb.features = 1000,
kBest = "NULL",
method = "glmnetRR",
kStep = "NULL",
vartype = "real",
gamma = 0.7,
nRR = 1,
lb = -1,
ub = 1,
language = "terinter",
scoreFormula = scoreRatio,
epsilon = "NULL",
nblambdas = 1000,
objective = "auc",
evalToFit = "auc_",
k_penalty = 0,
intercept = "NULL",
popSaveFile = "NULL",
final.pop.perc = 100,
alpha = 0.5,
plot = FALSE,
verbose = TRUE,
warnings = FALSE,
debug = FALSE,
print_ind_method = "short",
parallelize.folds = TRUE,
nCores = 4,
seed = "NULL",
experiment.id = "NULL",
experiment.description = "NULL",
experiment.save = "nothing"
)
language |
is the language that is used by the different algorithms bin, bininter, ter, terinter, ratio, (default:"terinter") |
sparsity: |
number of features in a given model. This is a vector with multiple lengths. |
nIterations: |
?? |
max.nb.features: |
create the glmnet object using only the top most significant features (default:1000) |
kBest: |
?? |
method: |
?? |
kStep: |
?? |
vartype: |
(??) |
gamma: |
?? |
nRR: |
(??) (default:FALSE) |
lb: |
?? |
ub: |
?? |
scoreFormula: |
a Function that contains the ratio Formula or other specific ones |
epsilon: |
a small value to be used with the ratio language (useCustomLanguage) (default: NULL). When null it is going to be calculated by the minimum value of X divided by 10. |
objective: |
this can be auc, cor or aic. Terga can also predict regression, other than class prediction. (default:auc) |
evalToFit: |
The model performance attribute to use as fitting score (default:"fit_"). Other choices are c("auc_","accuracy_","precision_","recall_","f_score_") |
k_penalty: |
Penalization of the fit by the k_sparsity (default: 0) |
intercept: |
(??) (default:NULL) |
popSaveFile: |
(??) |
final.pop.perc: |
?? |
plot: |
Plot different graphics (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 debug infotmation when activated (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"). |
parallelize.folds: |
parallelize folds when cross-validating (default:TRUE) |
nCores: |
the number of cores to execute the program. If nCores=1 than the program runs in a non parallel mode |
seed: |
the seed to be used for reproductibility. If seed=NULL than it is not taken into account (default:NULL). |
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" |
terda: terda classifier parameter function
an object containing a list of parameters for this classifier
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