baseModel | R Documentation |
Prediction using different supervised machine learning models.
baseModel( trainData, testData, classifier = c("randForest", "SVM", "glmnet"), predMode = c("classification", "probability", "regression"), paramlist )
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. |
classifier |
Machine learning classifiers. Available options are c('randForest', 'SVM', 'glmnet'). |
predMode |
The prediction mode. Available options are c('classification', 'probability', 'regression'). 'probability' is currently only for 'randForest'. |
paramlist |
A set of model parameters defined in an R list object. See more details for each individual model. |
Based on a given machine learning, the predicted score/output will be estimated 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), 16) trainData = methylSub[trainIndex,] testData = methylSub[-trainIndex,] library(ranger) set.seed(123) predY <- baseModel(trainData, testData, classifier='randForest', predMode='classification', paramlist=list(ntree=300, nthreads=20)) print(table(predY)) testY <- testData[,1] accuracy <- classifiACC(dataY=testY, predY=predY) print(accuracy)
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