ova_classification: One-Vs-All training approach

Description Usage Arguments Value Author(s)

Description

One-Vs-All training approach

Usage

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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)

Arguments

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 TRUE, run Recursive Feature Elimination as a feature selection method for "lda", "rf", and "svm" algorithms.

ova

logical; if TRUE, use the One-Vs-All approach for the knn algorithm.

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.

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 TRUE, algorithms with hyperparameters are tuned

seed_alg

random seed used for reproducibility when running algorithms with an intrinsic random element (random forests)

Value

list of binary classifier fits on each class

Author(s)

Dustin Johnson, Derek Chiu


AlineTalhouk/splendid documentation built on June 7, 2019, 5:23 p.m.