lilikoi.machine_learning | R Documentation |
This function for classification using 8 different machine learning algorithms and it plots the ROC curves and the AUC, SEN, and specificty
lilikoi.machine_learning( MLmatrix = PDSmatrix, measurementLabels = Label, significantPathways = selected_Pathways_Weka, trainportion = 0.8, cvnum = 10, dlround = 50, nrun = 10, Rpart = TRUE, LDA = TRUE, SVM = TRUE, RF = TRUE, GBM = TRUE, PAM = TRUE, LOG = TRUE, DL = TRUE )
MLmatrix |
selected pathway deregulation score or metabolites expression matrix |
measurementLabels |
measurement label for samples |
significantPathways |
selected pathway names |
trainportion |
train percentage of the total sample size |
cvnum |
number of folds |
dlround |
epoch number for the deep learning method |
nrun |
denotes the total number of runs of each method to get their averaged performance metrics |
Rpart |
TRUE if run Rpart method |
LDA |
TRUE if run LDA method |
SVM |
TRUE if run SVM method |
RF |
TRUE if run random forest method |
GBM |
TRUE if run GBM method |
PAM |
TRUE if run PAM method |
LOG |
TRUE if run LOG method |
DL |
TRUE if run deep learning method |
Evaluation results and plots of all 8 machine learning algorithms, along with variable importance plots.
dt = lilikoi.Loaddata(file=system.file("extdata","plasma_breast_cancer.csv", package = "lilikoi")) Metadata <- dt$Metadata # lilikoi.machine_learning(MLmatrix = Metadata, measurementLabels = Metadata$Label, # significantPathways = 0, # trainportion = 0.8, cvnum = 10, dlround=50,Rpart=TRUE, # LDA=FALSE,SVM=FALSE,RF=FALSE,GBM=FALSE,PAM=FALSE,LOG=FALSE,DL=FALSE)
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