LR2
Внуков Андрей 221-373
2025-02-28
library(caret)
Загрузка требуемого пакета: ggplot2
Загрузка требуемого пакета: lattice
> set.seed(123)
> x <- matrix(rnorm(50 * 5), ncol = 5)
> y <- factor(rep(c("A", "B"), 25))
> df <- data.frame(x, Class = y)
>
> # Построение графиков
> featurePlot(x = df[, 1:5], y = df$Class, plot = "box")
> # Сохранение графика
> jpeg("feature_plot.jpeg")
> featurePlot(x = df[, 1:5], y = df$Class, plot = "box")
> dev.off()
windows
2
> library(FSelector)
> data(iris)
> weights <- information.gain(Species ~ ., iris)
> print(weights)
attr_importance
Sepal.Length 0.4521286
Sepal.Width 0.2672750
Petal.Length 0.9402853
Petal.Width 0.9554360
> barplot(weights$attr_importance, names.arg = rownames(weights), las = 2, col = "lightblue", main = "Важность признаков (Information Gain)")
> weights <- gain.ratio(Species ~ ., iris)
> barplot(weights$attr_importance, names.arg = rownames(weights), las = 2, col = "lightblue", main = "Важность признаков (Gain Ratio)")
> #Задание 3
> library(arules)
Загрузка требуемого пакета: Matrix
Присоединяю пакет: ‘arules’
Следующие объекты скрыты от ‘package:base’:
abbreviate, write
> # Преобразование данных
> iris$Sepal.Length_interval <- discretize(iris$Sepal.Length, method = "interval", breaks = 3)
> iris$Sepal.Length_frequency <- discretize(iris$Sepal.Length, method = "frequency", breaks = 3)
> iris$Sepal.Length_cluster <- discretize(iris$Sepal.Length, method = "cluster", breaks = 3)
>
> # Вывод таблиц
> print(table(iris$Sepal.Length_interval))
[4.3,5.5) [5.5,6.7) [6.7,7.9]
52 70 28
> print(table(iris$Sepal.Length_frequency))
[4.3,5.4) [5.4,6.3) [6.3,7.9]
46 53 51
> print(table(iris$Sepal.Length_cluster))
[4.3,5.45) [5.45,6.46) [6.46,7.9]
52 63 35
> #Задание 4
> library(Boruta)
> library(mlbench)
>
> # Загрузка данных
> data("Ozone")
> ozone_data <- Ozone[, 4:6]
> ozone_data_clean <- na.omit(ozone_data)
>
> # Применение метода Boruta для выбора признаков
> boruta_result <- Boruta(V4 ~ ., data = ozone_data_clean, doTrace = 2)
run of importance source…
run of importance source…
run of importance source…
run of importance source…
run of importance source…
run of importance source…
run of importance source…
run of importance source…
After 8 iterations, +0.83 secs:
confirmed 2 attributes: V5, V6;
no more attributes left.
> # Отображение результатов
> print(boruta_result)
Boruta performed 8 iterations in 0.8322871 secs.
2 attributes confirmed important: V5, V6;
No attributes deemed unimportant.
> # Построение boxplot
> boxplot(ozone_data_clean[, c("V4", "V5", "V6")],
+ main = "Boxplot для набора данных Ozone (столбцы 4, 5, 6)",
+ col = c("lightblue", "lightgreen", "lightcoral"))
> save.image("C:\\Users\\user\\Downloads\\12")
