neoML.all: Title glmnet/kknn/range/svm/xgboost training model

In yujijun/neoantigenML: Machine Learning Methods for Neoantigen Filtering

Title glmnet/kknn/range/svm/xgboost training model

Description

Title glmnet/kknn/range/svm/xgboost training model

Usage

neoML.all(
  Neodataset = MLtestData,
  taskid = "neoML.glmnet",
  target = "judge",
  tunningkey = "classif.glmnet.rbv2",
  evalsnum = 20,
  innerparall = 4,
  outerparall = 3,
  innercv = 4,
  outercv = 3,
  measures = "classif.auc"
)

Arguments

Neodataset	A data frame of feature selection dataset.
taskid	A character of task id.
target	A column name of prediction target in Neodataset
tunningkey	A character of mlr_tuning_spaces c(classif.glmnet.rbv2,classif.kknn.rbv2,classif.ranger.rbv2,classif.svm.rbv2,classif.xgboost.default)
evalsnum	the numbers of iterations evaluation
innerparall	The number of inner parallelizations,default is 4
outerparall	The number of outer parallelizations,default is 4
innercv	The number of inner sampling in Nested sampling methods, default is 4
outercv	The number of outer sampling in Nested sampling methods, default is 4
measures	A character for Performance Measures detail could be found in https://mlr3book.mlr-org.com/appendix.html?q=measure#list-measures

Value

A resampling model with model.store

Examples

neoML.glmnet(MLtestData)
The evaluation of Tunning Space

yujijun/neoantigenML documentation built on March 20, 2022, 11:59 p.m.