Description Usage Arguments Value Author(s)
View source: R/tune_classifiers.R
Tune the parameters of the different classifiers for a given study case
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | TuneClassifiers(
studyCase,
dataType = "TMM_log2_PC",
tuneSVM = TRUE,
svmCost = 1,
svmDegree = 1:4,
svmGamma = 4^(-2:4),
svmCoef0 = 4^(-2:4),
tuneRandomForest = TRUE,
rfNtree = c(100, 200, 300, 500),
rfMtry = c(50, 100, 200, 300, 500),
tuneKNN = TRUE,
knnK = c(1, 2, 3, 4, 5, 7, 10, 15),
knnL = 0,
plotResults = FALSE
)
|
studyCase |
a studyCase object |
dataType="TMM_log2_PC" |
data type to use for the tuning. Must be one of the data types included in the 'datasetsForTest' attribute of the studyCase object |
tuneSVM=TRUE |
if TRUE, tune parameters for SVM |
svmCost=1 |
cost parameter values for SVM |
svmDegree=1:4 |
degree values for SVM polynomial kernel |
svmGamma=4^(-2:4) |
gamma values for SVM kernels (ignored for linear kernel) |
tuneRandomForest=TRUE |
if TRUE, tune parameters for Random Forest |
rfNtree=c(100, 200, 300, 500) |
values to test for RF numbers of trees |
rfMtry=c(50, 100, 200, 300, 500) |
values to test for RF mtry parameter |
tuneKNN=TRUE |
if TRUE, tune parameters for KNN |
knnK=c(1, 2, 3, 4, 5, 7, 10, 15) |
number of neighbours for KNN |
knnL=0 |
minimum vote for definite decision for KNN |
plotResults=FALSE |
if TRUE, run plot() on the tuned objects |
a list of parameters cloned from the studyCase, added with the optimal parameters identified here
Jacques van Helden
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