ova_classification | R Documentation |
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 |
data frame with rows as samples, columns as features |
class |
true/reference class vector used for supervised learning |
algorithms |
character string of algorithm to use for supervised learning. See Algorithms section for possible options. |
rfe |
logical; if |
ova |
logical; if |
standardize |
logical; if |
sampling |
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. |
seed_samp |
random seed used for reproducibility in subsampling training sets for model generation |
trees |
number of trees to use in "rf" or boosting iterations (trees) in "adaboost" |
tune |
logical; if |
seed_alg |
random seed used for reproducibility when running algorithms with an intrinsic random element (random forests) |
list of binary classifier fits on each class
Dustin Johnson, Derek Chiu
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