Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----eval = FALSE, 'Install package'------------------------------------------
# install.packages("FSinR")
## ----message=FALSE, 'Load libraries'------------------------------------------
library(caret)
library(FSinR)
data(iris)
## ---- 'Generate Evaluator (S+W)'----------------------------------------------
evaluator <- wrapperEvaluator("knn")
## ---- 'Generate searcher (S+W)'-----------------------------------------------
searcher <- searchAlgorithm('sequentialForwardSelection')
## ---- 'Feature Selection (S+W)'-----------------------------------------------
results <- featureSelection(iris, 'Species', searcher, evaluator)
## ---- 'Results (S+W)'---------------------------------------------------------
results$bestFeatures
results$bestValue
## ----eval=FALSE, 'Generate wrapper (S+W) 2'-----------------------------------
# resamplingParams <- list(method = "cv", number = 10)
# fittingParams <- list(preProc = c("center", "scale"), metric="Accuracy", tuneGrid = expand.grid(k = c(1:20)))
#
# evaluator <- wrapperEvaluator("knn", resamplingParams, fittingParams)
## ----eval=FALSE, 'Search generator (W+S) 2'-----------------------------------
# searcher <- searchAlgorithm('tabu', list(tamTabuList = 4, iter = 5, intensification=2, iterIntensification=5, diversification=1, iterDiversification=5, verbose=FALSE) )
## ----eval=FALSE, 'Feature Selection (S+W) 2'----------------------------------
# results <- featureSelection(iris, 'Species', searcher, evaluator)
## ---- 'Generate Evaluator (S+F)'----------------------------------------------
evaluator <- filterEvaluator('MDLC')
## ---- 'Generate searcher (S+F)'-----------------------------------------------
searcher <- searchAlgorithm('sequentialForwardSelection')
## ---- 'Feature Selection (S+F)'-----------------------------------------------
results <- featureSelection(iris, 'Species', searcher, evaluator)
## ---- 'Results (S+F)'---------------------------------------------------------
results$bestFeatures
results$bestValue
## ---- 'Generate Evaluator (F/W)'----------------------------------------------
filter_evaluator <- filterEvaluator("IEConsistency")
wrapper_evaluator <- wrapperEvaluator("lvq")
## ---- 'Results (F/W)'---------------------------------------------------------
resultFilter <- filter_evaluator(iris, 'Species', c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"))
resultFilter
resultWrapper <- wrapper_evaluator(iris, 'Species', c("Petal.Length", "Petal.Width"))
resultWrapper
## ----'DFS'--------------------------------------------------------------------
library(caret)
library(FSinR)
data(mtcars)
evaluator <- filterEvaluator('determinationCoefficient')
directSearcher <- directSearchAlgorithm('selectKBest', list(k=3))
results <- directFeatureSelection(mtcars, 'mpg', directSearcher, evaluator)
results$bestFeatures
results$featuresSelected
results$valuePerFeature
## ----'HFS'--------------------------------------------------------------------
library(caret)
library(FSinR)
data(mtcars)
evaluator_1 <- filterEvaluator('determinationCoefficient')
evaluator_2 <- filterEvaluator('ReliefFeatureSetMeasure')
hybridSearcher <- hybridSearchAlgorithm('LCC')
results <- hybridFeatureSelection(mtcars, 'mpg', hybridSearcher, evaluator_1, evaluator_2)
results$bestFeatures
results$bestValue
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