Задание 1 Разведочный анализ с featurePlot

library(caret)
set.seed(123)
x <- matrix(rnorm(50*5), ncol = 5)
y <- factor(rep(c("A", "B"), 25))
featurePlot(x, y, "box")

Задание 2 Важность признаков

library(FSelector)
data(iris, package = "datasets")
w <- information.gain(Species ~ ., iris)
w
##              attr_importance
## Sepal.Length       0.4521286
## Sepal.Width        0.2672750
## Petal.Length       0.9402853
## Petal.Width        0.9554360

Задание 3 Дискретизация

library(arules)
sl <- iris$Sepal.Length
table(arules::discretize(sl, method = "interval",  breaks = 3))
## 
## [4.3,5.5) [5.5,6.7) [6.7,7.9] 
##        52        70        28
table(arules::discretize(sl, method = "frequency", breaks = 3))
## 
## [4.3,5.4) [5.4,6.3) [6.3,7.9] 
##        46        53        51
table(arules::discretize(sl, method = "cluster",   breaks = 3))
## 
##  [4.3,5.45) [5.45,6.46)  [6.46,7.9] 
##          52          63          35
table(arules::discretize(sl, method = "fixed", breaks = c(4, 5.5, 7, 8)))
## 
## [4,5.5) [5.5,7)   [7,8] 
##      52      85      13

Задание 4. Boruta

library(Boruta); library(mlbench)
data("Ozone"); oz <- na.omit(Ozone)
set.seed(123)
b <- Boruta(V4 ~ ., data = oz, doTrace = 0)
plot(b, cex.axis = 0.7, las = 2)