baseSVM {BioMM} | R Documentation |
Prediction by support vector machine (SVM) with two different settings: 'classification' and 'regression'.
baseSVM(trainData, testData, predMode = c("classification", "probability", "regression"), paramlist = list(tuneP = TRUE, kernel = "radial", gamma = 10^(-3:-1), cost = 10^(-2:2)))
trainData |
The input training dataset. The first column is the label or the output. For binary classes, 0 and 1 are used to indicate the class member. |
testData |
The input test dataset. The first column is the label or the output. For binary classes, 0 and 1 are used to indicate the class member. |
predMode |
The prediction mode. Available options are c('classification', 'probability', 'regression'). |
paramlist |
A set of model parameters defined in an R list object. The valid option: list(kernel, gamma, cost, tuneP).
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Hyperparameter tuning is recommended in many biological data mining applications. The best parameters can be determined via an internal cross validation.
The predicted output for the test data.
Junfang Chen
## Load data methylfile <- system.file('extdata', 'methylData.rds', package='BioMM') methylData <- readRDS(methylfile) dataY <- methylData[,1] ## select a subset of genome-wide methylation data at random methylSub <- data.frame(label=dataY, methylData[,c(2:2001)]) trainIndex <- sample(nrow(methylSub), 30) trainData = methylSub[trainIndex,] testData = methylSub[-trainIndex,] library(e1071) predY <- baseSVM(trainData, testData, predMode='classification', paramlist=list(tuneP=FALSE, kernel='radial', gamma=10^(-3:-1), cost=10^(-3:1))) testY <- testData[,1] accuracy <- classifiACC(dataY=testY, predY=predY) print(accuracy)