lab2
Salov Dmitry
2025-01-18
Установка пакетов и подключение библиотек…
{r}install.packages("caret") install.packages("FSelector") install.packages("Boruta") install.packages("mlbench") install.packages("arules") library(caret) library(mlbench)
- Установить пакет CARET, выполнить команду names(getModelInfo()), ознакомиться со списком доступных методов выбора признаков. Выполните графический разведочный анализ данных с использование функции featurePlot() для набора данных из справочного файла пакета CARET:
x <- matrix(rnorm(50*5),ncol=5)
y <- factor(rep(c(“A”, “B”), 25))
Сохранить полученные графики в *.jpg файлы. Сделать выводы.
library(caret)## Загрузка требуемого пакета: ggplot2## Загрузка требуемого пакета: latticenames(getModelInfo())## [1] "ada" "AdaBag" "AdaBoost.M1"
## [4] "adaboost" "amdai" "ANFIS"
## [7] "avNNet" "awnb" "awtan"
## [10] "bag" "bagEarth" "bagEarthGCV"
## [13] "bagFDA" "bagFDAGCV" "bam"
## [16] "bartMachine" "bayesglm" "binda"
## [19] "blackboost" "blasso" "blassoAveraged"
## [22] "bridge" "brnn" "BstLm"
## [25] "bstSm" "bstTree" "C5.0"
## [28] "C5.0Cost" "C5.0Rules" "C5.0Tree"
## [31] "cforest" "chaid" "CSimca"
## [34] "ctree" "ctree2" "cubist"
## [37] "dda" "deepboost" "DENFIS"
## [40] "dnn" "dwdLinear" "dwdPoly"
## [43] "dwdRadial" "earth" "elm"
## [46] "enet" "evtree" "extraTrees"
## [49] "fda" "FH.GBML" "FIR.DM"
## [52] "foba" "FRBCS.CHI" "FRBCS.W"
## [55] "FS.HGD" "gam" "gamboost"
## [58] "gamLoess" "gamSpline" "gaussprLinear"
## [61] "gaussprPoly" "gaussprRadial" "gbm_h2o"
## [64] "gbm" "gcvEarth" "GFS.FR.MOGUL"
## [67] "GFS.LT.RS" "GFS.THRIFT" "glm.nb"
## [70] "glm" "glmboost" "glmnet_h2o"
## [73] "glmnet" "glmStepAIC" "gpls"
## [76] "hda" "hdda" "hdrda"
## [79] "HYFIS" "icr" "J48"
## [82] "JRip" "kernelpls" "kknn"
## [85] "knn" "krlsPoly" "krlsRadial"
## [88] "lars" "lars2" "lasso"
## [91] "lda" "lda2" "leapBackward"
## [94] "leapForward" "leapSeq" "Linda"
## [97] "lm" "lmStepAIC" "LMT"
## [100] "loclda" "logicBag" "LogitBoost"
## [103] "logreg" "lssvmLinear" "lssvmPoly"
## [106] "lssvmRadial" "lvq" "M5"
## [109] "M5Rules" "manb" "mda"
## [112] "Mlda" "mlp" "mlpKerasDecay"
## [115] "mlpKerasDecayCost" "mlpKerasDropout" "mlpKerasDropoutCost"
## [118] "mlpML" "mlpSGD" "mlpWeightDecay"
## [121] "mlpWeightDecayML" "monmlp" "msaenet"
## [124] "multinom" "mxnet" "mxnetAdam"
## [127] "naive_bayes" "nb" "nbDiscrete"
## [130] "nbSearch" "neuralnet" "nnet"
## [133] "nnls" "nodeHarvest" "null"
## [136] "OneR" "ordinalNet" "ordinalRF"
## [139] "ORFlog" "ORFpls" "ORFridge"
## [142] "ORFsvm" "ownn" "pam"
## [145] "parRF" "PART" "partDSA"
## [148] "pcaNNet" "pcr" "pda"
## [151] "pda2" "penalized" "PenalizedLDA"
## [154] "plr" "pls" "plsRglm"
## [157] "polr" "ppr" "pre"
## [160] "PRIM" "protoclass" "qda"
## [163] "QdaCov" "qrf" "qrnn"
## [166] "randomGLM" "ranger" "rbf"
## [169] "rbfDDA" "Rborist" "rda"
## [172] "regLogistic" "relaxo" "rf"
## [175] "rFerns" "RFlda" "rfRules"
## [178] "ridge" "rlda" "rlm"
## [181] "rmda" "rocc" "rotationForest"
## [184] "rotationForestCp" "rpart" "rpart1SE"
## [187] "rpart2" "rpartCost" "rpartScore"
## [190] "rqlasso" "rqnc" "RRF"
## [193] "RRFglobal" "rrlda" "RSimca"
## [196] "rvmLinear" "rvmPoly" "rvmRadial"
## [199] "SBC" "sda" "sdwd"
## [202] "simpls" "SLAVE" "slda"
## [205] "smda" "snn" "sparseLDA"
## [208] "spikeslab" "spls" "stepLDA"
## [211] "stepQDA" "superpc" "svmBoundrangeString"
## [214] "svmExpoString" "svmLinear" "svmLinear2"
## [217] "svmLinear3" "svmLinearWeights" "svmLinearWeights2"
## [220] "svmPoly" "svmRadial" "svmRadialCost"
## [223] "svmRadialSigma" "svmRadialWeights" "svmSpectrumString"
## [226] "tan" "tanSearch" "treebag"
## [229] "vbmpRadial" "vglmAdjCat" "vglmContRatio"
## [232] "vglmCumulative" "widekernelpls" "WM"
## [235] "wsrf" "xgbDART" "xgbLinear"
## [238] "xgbTree" "xyf"library(ggplot2)
x <- matrix(rnorm(50*5),ncol=5)
y <- factor(rep(c("A", "B"), 25))
featurePlot(x, y) 
Вывод: БОльшая часть значений находится в промежутке от -1 до 1
- С использование функций из пакета Fselector [2] определить важность признаков для решения задачи классификации. Использовать набор data(iris). Сделать выводы.
pairs(iris[,1:4], col=iris[,5], pch=19, cex=1.2, oma=c(4,4,6,12))
par(xpd=TRUE)
legend("topright", legend=as.vector(unique(iris$Species)), fill=c(1,2,3), title="Виды", cex=0.8)
Вывод: Sepal.Width является наимение значимым признаком, а Petal.Length наиболее значимым
- С использованием функции discretize() из пакета arules выполните преобразование непрерывной переменной в категориальную [3] различными методами: «interval» (равная ширина интервала), «frequency» (равная частота), «cluster» (кластеризация) и «fixed» (категории задают границы интервалов). Используйте набор данных iris. Сделайте выводы
library(arules)## Загрузка требуемого пакета: Matrix##
## Присоединяю пакет: 'arules'## Следующие объекты скрыты от 'package:base':
##
## abbreviate, writebreaks <- seq(from = 0, to = 10, by = 1)
percents <- discretize(iris[,1], method = "interval", breaks=10)
percents## [1] [5.02,5.38) [4.66,5.02) [4.66,5.02) [4.3,4.66) [4.66,5.02) [5.38,5.74)
## [7] [4.3,4.66) [4.66,5.02) [4.3,4.66) [4.66,5.02) [5.38,5.74) [4.66,5.02)
## [13] [4.66,5.02) [4.3,4.66) [5.74,6.1) [5.38,5.74) [5.38,5.74) [5.02,5.38)
## [19] [5.38,5.74) [5.02,5.38) [5.38,5.74) [5.02,5.38) [4.3,4.66) [5.02,5.38)
## [25] [4.66,5.02) [4.66,5.02) [4.66,5.02) [5.02,5.38) [5.02,5.38) [4.66,5.02)
## [31] [4.66,5.02) [5.38,5.74) [5.02,5.38) [5.38,5.74) [4.66,5.02) [4.66,5.02)
## [37] [5.38,5.74) [4.66,5.02) [4.3,4.66) [5.02,5.38) [4.66,5.02) [4.3,4.66)
## [43] [4.3,4.66) [4.66,5.02) [5.02,5.38) [4.66,5.02) [5.02,5.38) [4.3,4.66)
## [49] [5.02,5.38) [4.66,5.02) [6.82,7.18) [6.1,6.46) [6.82,7.18) [5.38,5.74)
## [55] [6.46,6.82) [5.38,5.74) [6.1,6.46) [4.66,5.02) [6.46,6.82) [5.02,5.38)
## [61] [4.66,5.02) [5.74,6.1) [5.74,6.1) [6.1,6.46) [5.38,5.74) [6.46,6.82)
## [67] [5.38,5.74) [5.74,6.1) [6.1,6.46) [5.38,5.74) [5.74,6.1) [6.1,6.46)
## [73] [6.1,6.46) [6.1,6.46) [6.1,6.46) [6.46,6.82) [6.46,6.82) [6.46,6.82)
## [79] [5.74,6.1) [5.38,5.74) [5.38,5.74) [5.38,5.74) [5.74,6.1) [5.74,6.1)
## [85] [5.38,5.74) [5.74,6.1) [6.46,6.82) [6.1,6.46) [5.38,5.74) [5.38,5.74)
## [91] [5.38,5.74) [6.1,6.46) [5.74,6.1) [4.66,5.02) [5.38,5.74) [5.38,5.74)
## [97] [5.38,5.74) [6.1,6.46) [5.02,5.38) [5.38,5.74) [6.1,6.46) [5.74,6.1)
## [103] [6.82,7.18) [6.1,6.46) [6.46,6.82) [7.54,7.9] [4.66,5.02) [7.18,7.54)
## [109] [6.46,6.82) [7.18,7.54) [6.46,6.82) [6.1,6.46) [6.46,6.82) [5.38,5.74)
## [115] [5.74,6.1) [6.1,6.46) [6.46,6.82) [7.54,7.9] [7.54,7.9] [5.74,6.1)
## [121] [6.82,7.18) [5.38,5.74) [7.54,7.9] [6.1,6.46) [6.46,6.82) [7.18,7.54)
## [127] [6.1,6.46) [6.1,6.46) [6.1,6.46) [7.18,7.54) [7.18,7.54) [7.54,7.9]
## [133] [6.1,6.46) [6.1,6.46) [6.1,6.46) [7.54,7.9] [6.1,6.46) [6.1,6.46)
## [139] [5.74,6.1) [6.82,7.18) [6.46,6.82) [6.82,7.18) [5.74,6.1) [6.46,6.82)
## [145] [6.46,6.82) [6.46,6.82) [6.1,6.46) [6.46,6.82) [6.1,6.46) [5.74,6.1)
## attr(,"discretized:breaks")
## [1] 4.30 4.66 5.02 5.38 5.74 6.10 6.46 6.82 7.18 7.54 7.90
## attr(,"discretized:method")
## [1] interval
## 10 Levels: [4.3,4.66) [4.66,5.02) [5.02,5.38) [5.38,5.74) ... [7.54,7.9]breaks <- seq(from = 0, to = 10, by = 1)
percents <- discretize(iris[,1], method = "cluster", breaks=10)
percents## [1] [4.81,5.12) [4.81,5.12) [4.3,4.81) [4.3,4.81) [4.81,5.12) [5.36,5.62)
## [7] [4.3,4.81) [4.81,5.12) [4.3,4.81) [4.81,5.12) [5.36,5.62) [4.3,4.81)
## [13] [4.3,4.81) [4.3,4.81) [5.62,5.88) [5.62,5.88) [5.36,5.62) [4.81,5.12)
## [19] [5.62,5.88) [4.81,5.12) [5.36,5.62) [4.81,5.12) [4.3,4.81) [4.81,5.12)
## [25] [4.3,4.81) [4.81,5.12) [4.81,5.12) [5.12,5.36) [5.12,5.36) [4.3,4.81)
## [31] [4.3,4.81) [5.36,5.62) [5.12,5.36) [5.36,5.62) [4.81,5.12) [4.81,5.12)
## [37] [5.36,5.62) [4.81,5.12) [4.3,4.81) [4.81,5.12) [4.81,5.12) [4.3,4.81)
## [43] [4.3,4.81) [4.81,5.12) [4.81,5.12) [4.3,4.81) [4.81,5.12) [4.3,4.81)
## [49] [5.12,5.36) [4.81,5.12) [6.86,7.37) [6.17,6.48) [6.86,7.37) [5.36,5.62)
## [55] [6.48,6.86) [5.62,5.88) [6.17,6.48) [4.81,5.12) [6.48,6.86) [5.12,5.36)
## [61] [4.81,5.12) [5.88,6.17) [5.88,6.17) [5.88,6.17) [5.36,5.62) [6.48,6.86)
## [67] [5.36,5.62) [5.62,5.88) [6.17,6.48) [5.36,5.62) [5.88,6.17) [5.88,6.17)
## [73] [6.17,6.48) [5.88,6.17) [6.17,6.48) [6.48,6.86) [6.48,6.86) [6.48,6.86)
## [79] [5.88,6.17) [5.62,5.88) [5.36,5.62) [5.36,5.62) [5.62,5.88) [5.88,6.17)
## [85] [5.36,5.62) [5.88,6.17) [6.48,6.86) [6.17,6.48) [5.36,5.62) [5.36,5.62)
## [91] [5.36,5.62) [5.88,6.17) [5.62,5.88) [4.81,5.12) [5.36,5.62) [5.62,5.88)
## [97] [5.62,5.88) [6.17,6.48) [4.81,5.12) [5.62,5.88) [6.17,6.48) [5.62,5.88)
## [103] [6.86,7.37) [6.17,6.48) [6.48,6.86) [7.37,7.9] [4.81,5.12) [6.86,7.37)
## [109] [6.48,6.86) [6.86,7.37) [6.48,6.86) [6.17,6.48) [6.48,6.86) [5.62,5.88)
## [115] [5.62,5.88) [6.17,6.48) [6.48,6.86) [7.37,7.9] [7.37,7.9] [5.88,6.17)
## [121] [6.86,7.37) [5.36,5.62) [7.37,7.9] [6.17,6.48) [6.48,6.86) [6.86,7.37)
## [127] [6.17,6.48) [5.88,6.17) [6.17,6.48) [6.86,7.37) [7.37,7.9] [7.37,7.9]
## [133] [6.17,6.48) [6.17,6.48) [5.88,6.17) [7.37,7.9] [6.17,6.48) [6.17,6.48)
## [139] [5.88,6.17) [6.86,7.37) [6.48,6.86) [6.86,7.37) [5.62,5.88) [6.48,6.86)
## [145] [6.48,6.86) [6.48,6.86) [6.17,6.48) [6.48,6.86) [6.17,6.48) [5.88,6.17)
## attr(,"discretized:breaks")
## [1] 4.300000 4.812250 5.116000 5.360000 5.623333 5.883333 6.167500 6.482500
## [9] 6.855000 7.365714 7.900000
## attr(,"discretized:method")
## [1] cluster
## 10 Levels: [4.3,4.81) [4.81,5.12) [5.12,5.36) [5.36,5.62) ... [7.37,7.9]breaks <- seq(from = 0, to = 10, by = 1)
percents <- discretize(iris[,1], method = "fixed", breaks=breaks)
percents## [1] [5,6) [4,5) [4,5) [4,5) [5,6) [5,6) [4,5) [5,6) [4,5) [4,5) [5,6) [4,5)
## [13] [4,5) [4,5) [5,6) [5,6) [5,6) [5,6) [5,6) [5,6) [5,6) [5,6) [4,5) [5,6)
## [25] [4,5) [5,6) [5,6) [5,6) [5,6) [4,5) [4,5) [5,6) [5,6) [5,6) [4,5) [5,6)
## [37] [5,6) [4,5) [4,5) [5,6) [5,6) [4,5) [4,5) [5,6) [5,6) [4,5) [5,6) [4,5)
## [49] [5,6) [5,6) [7,8) [6,7) [6,7) [5,6) [6,7) [5,6) [6,7) [4,5) [6,7) [5,6)
## [61] [5,6) [5,6) [6,7) [6,7) [5,6) [6,7) [5,6) [5,6) [6,7) [5,6) [5,6) [6,7)
## [73] [6,7) [6,7) [6,7) [6,7) [6,7) [6,7) [6,7) [5,6) [5,6) [5,6) [5,6) [6,7)
## [85] [5,6) [6,7) [6,7) [6,7) [5,6) [5,6) [5,6) [6,7) [5,6) [5,6) [5,6) [5,6)
## [97] [5,6) [6,7) [5,6) [5,6) [6,7) [5,6) [7,8) [6,7) [6,7) [7,8) [4,5) [7,8)
## [109] [6,7) [7,8) [6,7) [6,7) [6,7) [5,6) [5,6) [6,7) [6,7) [7,8) [7,8) [6,7)
## [121] [6,7) [5,6) [7,8) [6,7) [6,7) [7,8) [6,7) [6,7) [6,7) [7,8) [7,8) [7,8)
## [133] [6,7) [6,7) [6,7) [7,8) [6,7) [6,7) [6,7) [6,7) [6,7) [6,7) [5,6) [6,7)
## [145] [6,7) [6,7) [6,7) [6,7) [6,7) [5,6)
## attr(,"discretized:breaks")
## [1] 0 1 2 3 4 5 6 7 8 9 10
## attr(,"discretized:method")
## [1] fixed
## Levels: [0,1) [1,2) [2,3) [3,4) [4,5) [5,6) [6,7) [7,8) [8,9) [9,10]Вывод: В зависимости от выбранного метода, изменяются границы интервалов в категориальной переменной. Однако при этом разница довольно небольшая.
- Установите пакет Boruta и проведите выбор признаков для набора данных data(“Ozone”) [4, 5, 6]. Построить график boxplot, сделать выводы.
library(Boruta)
data("Ozone", package = "mlbench")
Ozone <- na.omit(Ozone)
boruta_result <- Boruta(V4 ~ ., data = Ozone, doTrace = 2)## 1. run of importance source...## 2. run of importance source...## 3. run of importance source...## 4. run of importance source...## 5. run of importance source...## 6. run of importance source...## 7. run of importance source...## 8. run of importance source...## 9. run of importance source...## 10. run of importance source...## 11. run of importance source...## After 11 iterations, +0.89 secs:## confirmed 9 attributes: V1, V10, V11, V12, V13 and 4 more;## rejected 1 attribute: V3;## still have 2 attributes left.## 12. run of importance source...## 13. run of importance source...## 14. run of importance source...## 15. run of importance source...## 16. run of importance source...## 17. run of importance source...## 18. run of importance source...## After 18 iterations, +1.4 secs:## rejected 1 attribute: V6;## still have 1 attribute left.## 19. run of importance source...## 20. run of importance source...## 21. run of importance source...## After 21 iterations, +1.6 secs:## rejected 1 attribute: V2;## no more attributes left.print(boruta_result)## Boruta performed 21 iterations in 1.637024 secs.
## 9 attributes confirmed important: V1, V10, V11, V12, V13 and 4 more;
## 3 attributes confirmed unimportant: V2, V3, V6;plot(boruta_result)
Вывод: Выбор признаков выявил, что признаки V2, V3, V6 являются малозначимыми, в отличие от 9-ти остальных признаков