ova_prediction: One-Vs-All prediction approach

In AlineTalhouk/splendid: SuPervised Learning ENsemble for Diagnostic IDentification

Description

One-Vs-All prediction approach

Usage

ova_prediction(fits, data, class, test.id = NULL, train.id = NULL,
  threshold = 0, standardize = FALSE, ...)

Arguments

fits	list of ova fits from ova_classification
data	data frame with rows as samples, columns as features
class	true/reference class vector used for supervised learning
test.id	integer vector of indices for test set. If NULL (default), all samples are used.
train.id	integer vector of indices for training set. If NULL (default), all samples are used.
threshold	a number between 0 and 1 indicating the lowest maximum class probability below which a sample will be unclassified.
standardize	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.
...	additional arguments to be passed to or from methods

Value

(tibble) predicted probabilities for each class

Author(s)

Dustin Johnson, Derek Chiu

AlineTalhouk/splendid documentation built on Aug. 30, 2018, 7:54 a.m.