144 lines
4.4 KiB
R
144 lines
4.4 KiB
R
v <- 10
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blad_i <- numeric(v)
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krok <- floor(nrow(iris) / v)
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permutacja <- sample(1:nrow(iris))
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temp <- 0
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for (i in 1:v) {
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if (i != v) {
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obs_temp <- permutacja[(temp + 1):(i * krok)]
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temp <- i * krok
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} else {
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obs_temp <- permutacja[(temp + 1):nrow(iris)]
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}
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model_lda_i <- lda(Species ~ ., data = iris[-obs_temp, ])
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pred_i <- stats::predict(model_lda_i, iris[obs_temp, ])$class
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blad_i[i] <- sum(pred_i != iris$Species[obs_temp])
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}
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sum(blad_i) / nrow(iris)
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library(MASS)
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v <- 10
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blad_i <- numeric(v)
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krok <- floor(nrow(iris) / v)
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permutacja <- sample(1:nrow(iris))
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temp <- 0
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for (i in 1:v) {
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if (i != v) {
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obs_temp <- permutacja[(temp + 1):(i * krok)]
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temp <- i * krok
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} else {
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obs_temp <- permutacja[(temp + 1):nrow(iris)]
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}
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model_lda_i <- lda(Species ~ ., data = iris[-obs_temp, ])
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pred_i <- stats::predict(model_lda_i, iris[obs_temp, ])$class
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blad_i[i] <- sum(pred_i != iris$Species[obs_temp])
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}
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sum(blad_i) / nrow(iris)
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# bootstrap
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n_boot <- 100
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temp_boot <- numeric(n_boot)
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set.seed(1234)
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for (i in 1:n_boot) {
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numery <- sample(1:nrow(iris), replace = TRUE)
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model_lda_i <- lda(Species ~ ., data = iris[numery, ])
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temp_boot[i] <- mean(stats::predict(model_lda_i, iris[-numery, ])$class != iris[-numery, ]$Species)
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}
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mean(temp_boot)
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# w pakiecie caret dla sprawdzenia
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library(caret)
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ctrl_boot <- trainControl(method = 'boot',
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number = 100,
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search = 'grid')
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ctrl_loo <- trainControl(method = 'LOOCV',
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search = 'grid')
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ctrl_10CV <- trainControl(method = "repeatedcv",
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number = 10,
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repeats = 10)
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train(Species ~ ., data = iris, method = 'lda', trControl = ctrl_boot)
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train(Species ~ ., data = iris, method = 'lda', trControl = ctrl_loo)
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train(Species ~ ., data = iris, method = 'lda', trControl = ctrl_10CV)
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library(caret)
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install.packages("caret")
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library(caret)
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ctrl_boot <- trainControl(method = 'boot',
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number = 100,
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search = 'grid')
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ctrl_loo <- trainControl(method = 'LOOCV',
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search = 'grid')
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ctrl_10CV <- trainControl(method = "repeatedcv",
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number = 10,
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repeats = 10)
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train(Species ~ ., data = iris, method = 'lda', trControl = ctrl_boot)
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train(Species ~ ., data = iris, method = 'lda', trControl = ctrl_loo)
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train(Species ~ ., data = iris, method = 'lda', trControl = ctrl_10CV)
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wina <- read.table('http://ls.home.amu.edu.pl/data_sets/wina.txt')
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head(wina)
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cat("...")
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wina$V14 <- as.factor(wina$V14)
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dim(wina)
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table(wina$V14)
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model_lda <- lda(V14 ~ V1 + V2 + V3, data = wina)
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model_lda$prior
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model_lda$means
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model_lda$scaling
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head(stats::predict(model_lda)$posterior)
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head(stats::predict(model_lda)$class)
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(conf_matrix <- table(stats::predict(model_lda)$class, wina$V14))
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(1 - sum(diag(conf_matrix)) / nrow(wina))
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(1 - sum(diag(conf_matrix)) / nrow(wina))
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pred_loo <- numeric(nrow(wina))
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for (i in 1:nrow(wina)) {
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model_lda_i <- lda(V14 ~ V1 + V2 + V3, data = wina[-i, ])
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pred_loo[i] <- stats::predict(model_lda_i, wina[i, ])$class
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}
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table(wina$V14, pred_loo)
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(1 - sum(diag(table(wina$V14, pred_loo))) / nrow(wina))
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# 10CV
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v <- 10
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blad_i <- numeric(v)
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krok <- floor(nrow(wina) / v)
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permutacja <- sample(1:nrow(wina))
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temp <- 0
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for (i in 1:v) {
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if (i != v) {
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obs_temp <- permutacja[(temp + 1):(i * krok)]
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temp <- i * krok
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} else {
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obs_temp <- permutacja[(temp + 1):nrow(wina)]
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}
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model_lda_i <- lda(V14 ~ V1 + V2 + V3, data = wina[-obs_temp, ])
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pred_i <- stats::predict(model_lda_i, wina[obs_temp, ])$class
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blad_i[i] <- sum(pred_i != wina$V14[obs_temp])
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}
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sum(blad_i) / nrow(wina)
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# bootstrap
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n_boot <- 100
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temp_boot <- numeric(n_boot)
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set.seed(1234)
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for (i in 1:n_boot) {
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numery <- sample(1:nrow(wina), replace = TRUE)
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model_lda_i <- lda(V14 ~ V1 + V2 + V3, data = wina[numery, ])
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temp_boot[i] <- mean(stats::predict(model_lda_i, wina[-numery, ])$class != wina[-numery, ]$V14)
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}
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mean(temp_boot)
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# w pakiecie caret dla sprawdzenia
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library(caret)
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ctrl_boot <- trainControl(method = 'boot',
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number = 100,
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search = 'grid')
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ctrl_loo <- trainControl(method = 'LOOCV',
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search = 'grid')
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ctrl_10CV <- trainControl(method = "repeatedcv",
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number = 10,
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repeats = 10)
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train(V14 ~ V1 + V2 + V3, data = wina, method = 'lda', trControl = ctrl_boot)
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train(V14 ~ V1 + V2 + V3, data = wina, method = 'lda', trControl = ctrl_loo)
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train(V14 ~ V1 + V2 + V3, data = wina, method = 'lda', trControl = ctrl_10CV)
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wybrane <- wina[c(20, 50, 150, 100), 1:3]
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rownames(wybrane) <- 1:4
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library(knitr)
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kable(wybrane, align = c('c', 'c', 'c'))
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new_data <- data.frame(V1 = c(13.64, 13.94, 13.08, 12.29),
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V2 = c(3.1, 1.73, 3.9, 3.17),
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V3 = c(2.56, 2.27, 2.36, 2.21))
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stats::predict(model_lda, new_data)
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