One-Vs-All training approach
ova_classification(
data,
class,
algorithms,
rfe = FALSE,
ova = FALSE,
standardize = FALSE,
sampling = c("none", "up", "down", "smote"),
seed_samp = NULL,
trees = 100,
tune = FALSE,
seed_alg = NULL
)data frame with rows as samples, columns as features
true/reference class vector used for supervised learning
character string of algorithm to use for supervised learning. See Algorithms section for possible options.
logical; if TRUE, run Recursive Feature Elimination as a feature
selection method for "lda", "rf", and "svm" algorithms.
logical; if TRUE, use the One-Vs-All approach for the knn
algorithm.
logical; if TRUE, the training sets are standardized on
features to have mean zero and unit variance. The test sets are
standardized using the vectors of centers and standard deviations used in
corresponding training sets.
the default is "none", in which no subsampling is performed. Other options include "up" (Up-sampling the minority class), "down" (Down-sampling the majority class), and "smote" (synthetic points for the minority class and down-sampling the majority class). Subsampling is only applicable to the training set.
random seed used for reproducibility in subsampling training sets for model generation
number of trees to use in "rf" or boosting iterations (trees) in "adaboost"
logical; if TRUE, algorithms with hyperparameters are tuned
random seed used for reproducibility when running algorithms with an intrinsic random element (random forests)
list of binary classifier fits on each class