| title: “final proje” |
| author: “ABDIMUNCIM ISSE ISMAIL 2010504587” |
| date: “2024-01-21” |
| output: |
| prettydoc::html_pretty: |
| theme: tactile |
| toc: yes |
##spaceship titanic
Welcome to the year 2912, where your data science skills are needed to solve a cosmic mystery. We’ve received a transmission from four lightyears away and things aren’t looking good.
The Spaceship Titanic was an interstellar passenger liner launched a month ago. With almost 13,000 passengers on board, the vessel set out on its maiden voyage transporting emigrants from our solar system to three newly habitable exoplanets orbiting nearby stars.
While rounding Alpha Centauri en route to its first destination—the torrid 55 Cancri E—the unwary Spaceship Titanic collided with a spacetime anomaly hidden within a dust cloud. Sadly, it met a similar fate as its namesake from 1000 years before. Though the ship stayed intact, almost half of the passengers were transported to an alternate dimension!
library(readr)
train <- read_csv("train.csv")## Rows: 8693 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): PassengerId, HomePlanet, Cabin, Destination, Name
## dbl (6): Age, RoomService, FoodCourt, ShoppingMall, Spa, VRDeck
## lgl (3): CryoSleep, VIP, Transported
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Bu satır, “train.csv” adlı bir CSV dosyasını okur ve içerdiği verileri “train” adlı bir veri çerçevesine yükler
test <- read_csv("test.csv")## Rows: 4277 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): PassengerId, HomePlanet, Cabin, Destination, Name
## dbl (6): Age, RoomService, FoodCourt, ShoppingMall, Spa, VRDeck
## lgl (2): CryoSleep, VIP
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Bu satır, “train.csv” adlı bir CSV dosyasını okur ve içerdiği verileri “train” adlı bir veri çerçevesine yükler
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ purrr 1.0.2
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorstidyverse R paketi, veri manipülasyonu ve analizi için bir dizi kullanışlı fonksiyon içeren bir koleksiyondur. Bu paket, veri bilimi ve analizi alanında sıkça kullanılan bazı önemli paketleri içerir ve bu paketler arasında tutarlı bir dil ve çalışma mantığı sağlar.
library(explore)Eğer library(explore) komutunu kullanıyorsanız, bu kod, “explore” adlı bir R paketini yüklemek ve bu paketin fonksiyonlarını kullanılabilir hale getirmek için kullanılır. R’deki library fonksiyonu, belirtilen paketi yükler ve paketin içindeki fonksiyonları kullanılabilir kılar
train %>% describe_all()## # A tibble: 14 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet chr 201 2.3 4 NA NA NA
## 3 CryoSleep lgl 217 2.5 3 0 0.36 1
## 4 Cabin chr 199 2.3 6561 NA NA NA
## 5 Destination chr 182 2.1 4 NA NA NA
## 6 Age dbl 179 2.1 81 0 28.8 79
## 7 VIP lgl 203 2.3 3 0 0.02 1
## 8 RoomService dbl 181 2.1 1274 0 225. 14327
## 9 FoodCourt dbl 183 2.1 1508 0 458. 29813
## 10 ShoppingMall dbl 208 2.4 1116 0 174. 23492
## 11 Spa dbl 183 2.1 1328 0 311. 22408
## 12 VRDeck dbl 188 2.2 1307 0 305. 24133
## 13 Name chr 200 2.3 8474 NA NA NA
## 14 Transported lgl 0 0 2 0 0.5 1describe_all fonksiyonunu kullanarak veri çerçevesindeki tüm değişkenlerin özet istatistiklerini elde etmeye yönelik bir kod olabilir.
train[c('ailenum', 'ailesira')] <- str_split_fixed(train$PassengerId, "_", 2)“train” adlı bir veri çerçevesinde bulunan “PassengerId” değişkenini kullanarak “ailenum” ve “ailesira” adlı yeni değişkenleri oluşturmayı amaçlar
test[c('ailenum', 'ailesira')] <- str_split_fixed(test$PassengerId, "_", 2)“test” adlı bir veri çerçevesinde bulunan “PassengerId” değişkenini kullanarak “ailenum” ve “ailesira” adlı yeni değişkenleri oluşturmayı amaçlar
train[c('deck', 'num', 'side')] <- str_split_fixed(train$Cabin, '/', 3)“train” adlı bir veri çerçevesinde bulunan “Cabin” değişkenini kullanarak yeni “deck”, “num” ve “side” adlı üç yeni değişkeni oluşturmayı amaçlar.
test[c('deck', 'num', 'side')] <- str_split_fixed(test$Cabin, '/', 3)“train” adlı bir veri çerçevesinde bulunan “Cabin” değişkenini kullanarak yeni “deck”, “num” ve “side” adlı üç yeni değişkeni oluşturmayı amaçlar.
train[train == ''] <- NA
test[test == ''] <- NA“train” ve “test” adlı iki veri çerçevesindeki boş değerleri (’’ olarak temsil edilen karakter dizisi boşlukları) NA (eksik veri) değerleri ile değiştirir. Bu işlem, veri çerçevelerindeki boş değerleri düzenlemek ve eksik veri durumunu belirtmek amacıyla kullanılır.
train %>% describe_all()## # A tibble: 19 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet chr 201 2.3 4 NA NA NA
## 3 CryoSleep lgl 217 2.5 3 0 0.36 1
## 4 Cabin chr 199 2.3 6561 NA NA NA
## 5 Destination chr 182 2.1 4 NA NA NA
## 6 Age dbl 179 2.1 81 0 28.8 79
## 7 VIP lgl 203 2.3 3 0 0.02 1
## 8 RoomService dbl 181 2.1 1274 0 225. 14327
## 9 FoodCourt dbl 183 2.1 1508 0 458. 29813
## 10 ShoppingMall dbl 208 2.4 1116 0 174. 23492
## 11 Spa dbl 183 2.1 1328 0 311. 22408
## 12 VRDeck dbl 188 2.2 1307 0 305. 24133
## 13 Name chr 200 2.3 8474 NA NA NA
## 14 Transported lgl 0 0 2 0 0.5 1
## 15 ailenum chr 0 0 6217 NA NA NA
## 16 ailesira chr 0 0 8 NA NA NA
## 17 deck chr 199 2.3 9 NA NA NA
## 18 num chr 199 2.3 1818 NA NA NA
## 19 side chr 199 2.3 3 NA NA NAtrain <- train %>% select(-Cabin)“train” veri çerçevesindeki “Cabin” sütunu çıkarılarak yeni bir güncellenmiş veri çerçevesi oluşturulur. Bu tür işlemler, gereksiz veya kullanılmayan sütunları çıkarmak veya veriyi belirli bir şekilde düzenlemek için yaygın olarak kullanılır
test <- test %>% select(-Cabin)“train” veri çerçevesindeki “Cabin” sütunu çıkarılarak yeni bir güncellenmiş veri çerçevesi oluşturulur. Bu tür işlemler, gereksiz veya kullanılmayan sütunları çıkarmak veya veriyi belirli bir şekilde düzenlemek için yaygın olarak kullanılır
unique(train$HomePlanet)## [1] "Europa" "Earth" "Mars" NA“train” veri çerçevesindeki “HomePlanet” sütunundaki benzersiz (unique) değerleri döndürür. Yani, bu kod, “HomePlanet” sütunundaki farklı kategorileri bulmanıza yardımcı olur.
levels(train$HomePlanet)## NULL“HomePlanet” sütununun faktör olduğu varsayımıyla çalışır ve bu sütundaki benzersiz seviyeleri döndürür
train$HomePlanet <- addNA(train$HomePlanet)“train” veri çerçevesindeki “HomePlanet” sütunundaki tüm değerleri bir eşdeğer değeri olan NA (eksik değer) ile değiştirir. Bu, eksik veya bilinmeyen değerleri belirtmek için kullanılır
test$HomePlanet <- addNA(test$HomePlanet)“train” veri çerçevesindeki “HomePlanet” sütunundaki tüm değerleri bir eşdeğer değeri olan NA (eksik değer) ile değiştirir. Bu, eksik veya bilinmeyen değerleri belirtmek için kullanılır
levels(train$HomePlanet)## [1] "Earth" "Europa" "Mars" NA“HomePlanet” sütununun faktör olduğu varsayımıyla çalışır ve bu sütundaki benzersiz seviyeleri döndürür
levels(train$HomePlanet)[is.na(levels(train$HomePlanet))] <- "NA"“train” veri çerçevesindeki “HomePlanet” sütunundaki faktör seviyelerinden herhangi biri NA (eksik değer) ise, bu seviyeleri “NA” karakter dizisi ile değiştirir. Yani, NA değerlerine özel bir kategori atanmış olur.
levels(test$HomePlanet)[is.na(levels(test$HomePlanet))] <- "NA"“test” veri çerçevesindeki “HomePlanet” sütunundaki faktör seviyelerinden herhangi biri NA (eksik değer) ise, bu seviyeleri “NA” karakter dizisi ile değiştirir. Yani, NA değerlerine özel bir kategori atanmış olur.
levels(train$HomePlanet)## [1] "Earth" "Europa" "Mars" "NA"“HomePlanet” sütununun faktör olduğu varsayımıyla çalışır ve bu sütundaki benzersiz seviyeleri döndürür
train %>% describe_all()## # A tibble: 18 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 4 NA NA NA
## 3 CryoSleep lgl 217 2.5 3 0 0.36 1
## 4 Destination chr 182 2.1 4 NA NA NA
## 5 Age dbl 179 2.1 81 0 28.8 79
## 6 VIP lgl 203 2.3 3 0 0.02 1
## 7 RoomService dbl 181 2.1 1274 0 225. 14327
## 8 FoodCourt dbl 183 2.1 1508 0 458. 29813
## 9 ShoppingMall dbl 208 2.4 1116 0 174. 23492
## 10 Spa dbl 183 2.1 1328 0 311. 22408
## 11 VRDeck dbl 188 2.2 1307 0 305. 24133
## 12 Name chr 200 2.3 8474 NA NA NA
## 13 Transported lgl 0 0 2 0 0.5 1
## 14 ailenum chr 0 0 6217 NA NA NA
## 15 ailesira chr 0 0 8 NA NA NA
## 16 deck chr 199 2.3 9 NA NA NA
## 17 num chr 199 2.3 1818 NA NA NA
## 18 side chr 199 2.3 3 NA NA NAtrain <- train %>%
group_by(HomePlanet) %>%
mutate_at(vars(Age), ~replace_na(., mean(., na.rm = TRUE)))“train” veri çerçevesindeki “HomePlanet” sütununa göre gruplama yapar (group_by) ve her grup içindeki “Age” sütunundaki eksik değerleri (NA) grup ortalaması ile doldurur (mutate_at, replace_na).
test <- test %>%
group_by(HomePlanet) %>%
mutate_at(vars(Age), ~replace_na(., mean(., na.rm = TRUE)))“test” veri çerçevesindeki “HomePlanet” sütununa göre gruplama yapar (group_by) ve her grup içindeki “Age” sütunundaki eksik değerleri (NA) grup ortalaması ile doldurur (mutate_at, replace_na).
train %>% describe_all()## # A tibble: 18 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 4 NA NA NA
## 3 CryoSleep lgl 217 2.5 3 0 0.36 1
## 4 Destination chr 182 2.1 4 NA NA NA
## 5 Age dbl 0 0 84 0 28.8 79
## 6 VIP lgl 203 2.3 3 0 0.02 1
## 7 RoomService dbl 181 2.1 1274 0 225. 14327
## 8 FoodCourt dbl 183 2.1 1508 0 458. 29813
## 9 ShoppingMall dbl 208 2.4 1116 0 174. 23492
## 10 Spa dbl 183 2.1 1328 0 311. 22408
## 11 VRDeck dbl 188 2.2 1307 0 305. 24133
## 12 Name chr 200 2.3 8474 NA NA NA
## 13 Transported lgl 0 0 2 0 0.5 1
## 14 ailenum chr 0 0 6217 NA NA NA
## 15 ailesira chr 0 0 8 NA NA NA
## 16 deck chr 199 2.3 9 NA NA NA
## 17 num chr 199 2.3 1818 NA NA NA
## 18 side chr 199 2.3 3 NA NA NAtrain$CryoSleep <- addNA(train$CryoSleep)“train” veri çerçevesindeki “CryoSleep” sütunundaki tüm değerleri bir eşdeğer değeri olan NA (eksik değer) ile değiştirir. Bu, eksik veya bilinmeyen değerleri belirtmek için kullanılır. addNA() fonksiyonu, bir vektördeki tüm benzersiz değerlere NA değerini ekler.
test$CryoSleep <- addNA(test$CryoSleep)“test” veri çerçevesindeki “CryoSleep” sütunundaki tüm değerleri bir eşdeğer değeri olan NA (eksik değer) ile değiştirir. Bu, eksik veya bilinmeyen değerleri belirtmek için kullanılır. addNA() fonksiyonu, bir vektördeki tüm benzersiz değerlere NA değerini ekler.
levels(train$CryoSleep)[is.na(levels(train$CryoSleep))] <- "NA"levels(test$CryoSleep)[is.na(levels(test$CryoSleep))] <- "NA"train %>% describe_all()## # A tibble: 18 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 4 NA NA NA
## 3 CryoSleep fct 0 0 3 NA NA NA
## 4 Destination chr 182 2.1 4 NA NA NA
## 5 Age dbl 0 0 84 0 28.8 79
## 6 VIP lgl 203 2.3 3 0 0.02 1
## 7 RoomService dbl 181 2.1 1274 0 225. 14327
## 8 FoodCourt dbl 183 2.1 1508 0 458. 29813
## 9 ShoppingMall dbl 208 2.4 1116 0 174. 23492
## 10 Spa dbl 183 2.1 1328 0 311. 22408
## 11 VRDeck dbl 188 2.2 1307 0 305. 24133
## 12 Name chr 200 2.3 8474 NA NA NA
## 13 Transported lgl 0 0 2 0 0.5 1
## 14 ailenum chr 0 0 6217 NA NA NA
## 15 ailesira chr 0 0 8 NA NA NA
## 16 deck chr 199 2.3 9 NA NA NA
## 17 num chr 199 2.3 1818 NA NA NA
## 18 side chr 199 2.3 3 NA NA NAunique(train$Destination)## [1] "TRAPPIST-1e" "PSO J318.5-22" "55 Cancri e" NAtrain$Destination <- addNA(train$Destination)test$Destination <- addNA(test$Destination)levels(train$Destination)[is.na(levels(train$Destination))] <- "NA"levels(test$Destination)[is.na(levels(test$Destination))] <- "NA"train$side <- addNA(train$side)test$side <- addNA(test$side)levels(train$side)[is.na(levels(train$side))] <- "NA"levels(test$side)[is.na(levels(test$side))] <- "NA"train %>% describe_all()## # A tibble: 18 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 4 NA NA NA
## 3 CryoSleep fct 0 0 3 NA NA NA
## 4 Destination fct 0 0 4 NA NA NA
## 5 Age dbl 0 0 84 0 28.8 79
## 6 VIP lgl 203 2.3 3 0 0.02 1
## 7 RoomService dbl 181 2.1 1274 0 225. 14327
## 8 FoodCourt dbl 183 2.1 1508 0 458. 29813
## 9 ShoppingMall dbl 208 2.4 1116 0 174. 23492
## 10 Spa dbl 183 2.1 1328 0 311. 22408
## 11 VRDeck dbl 188 2.2 1307 0 305. 24133
## 12 Name chr 200 2.3 8474 NA NA NA
## 13 Transported lgl 0 0 2 0 0.5 1
## 14 ailenum chr 0 0 6217 NA NA NA
## 15 ailesira chr 0 0 8 NA NA NA
## 16 deck chr 199 2.3 9 NA NA NA
## 17 num chr 199 2.3 1818 NA NA NA
## 18 side fct 0 0 3 NA NA NAtrain$VIP <- addNA(train$VIP)test$VIP <- addNA(test$VIP)levels(train$VIP)[is.na(levels(train$VIP))] <- "NA"levels(test$VIP)[is.na(levels(test$VIP))] <- "NA"train <- train %>%
group_by(Destination) %>%
mutate_at(vars(RoomService), ~replace_na(., mean(., na.rm = TRUE)))test <- test %>%
group_by(Destination) %>%
mutate_at(vars(RoomService), ~replace_na(., mean(., na.rm = TRUE)))train %>% describe_all()## # A tibble: 18 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 4 NA NA NA
## 3 CryoSleep fct 0 0 3 NA NA NA
## 4 Destination fct 0 0 4 NA NA NA
## 5 Age dbl 0 0 84 0 28.8 79
## 6 VIP fct 0 0 3 NA NA NA
## 7 RoomService dbl 0 0 1277 0 225. 14327
## 8 FoodCourt dbl 183 2.1 1508 0 458. 29813
## 9 ShoppingMall dbl 208 2.4 1116 0 174. 23492
## 10 Spa dbl 183 2.1 1328 0 311. 22408
## 11 VRDeck dbl 188 2.2 1307 0 305. 24133
## 12 Name chr 200 2.3 8474 NA NA NA
## 13 Transported lgl 0 0 2 0 0.5 1
## 14 ailenum chr 0 0 6217 NA NA NA
## 15 ailesira chr 0 0 8 NA NA NA
## 16 deck chr 199 2.3 9 NA NA NA
## 17 num chr 199 2.3 1818 NA NA NA
## 18 side fct 0 0 3 NA NA NAhist(train$ShoppingMall)
hist(train$FoodCourt)
train <- train %>% mutate(FoodCourt = coalesce(FoodCourt, 0))train” veri çerçevesindeki “FoodCourt” sütunundaki eksik değerleri (NA) 0 ile değiştirir. Bu işlem, eksik değerleri belirli bir değerle doldurmak amacıyla kullanılır.
test <- test %>% mutate(FoodCourt = coalesce(FoodCourt, 0))test veri çerçevesindeki “FoodCourt” sütunundaki eksik değerleri (NA) 0 ile değiştirir. Bu işlem, eksik değerleri belirli bir değerle doldurmak amacıyla kullanılır.
train %>% describe_all()## # A tibble: 18 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 4 NA NA NA
## 3 CryoSleep fct 0 0 3 NA NA NA
## 4 Destination fct 0 0 4 NA NA NA
## 5 Age dbl 0 0 84 0 28.8 79
## 6 VIP fct 0 0 3 NA NA NA
## 7 RoomService dbl 0 0 1277 0 225. 14327
## 8 FoodCourt dbl 0 0 1507 0 448. 29813
## 9 ShoppingMall dbl 208 2.4 1116 0 174. 23492
## 10 Spa dbl 183 2.1 1328 0 311. 22408
## 11 VRDeck dbl 188 2.2 1307 0 305. 24133
## 12 Name chr 200 2.3 8474 NA NA NA
## 13 Transported lgl 0 0 2 0 0.5 1
## 14 ailenum chr 0 0 6217 NA NA NA
## 15 ailesira chr 0 0 8 NA NA NA
## 16 deck chr 199 2.3 9 NA NA NA
## 17 num chr 199 2.3 1818 NA NA NA
## 18 side fct 0 0 3 NA NA NAtrain <- train %>% mutate(ShoppingMall = coalesce(ShoppingMall, 0),
Spa = coalesce(Spa, 0),
VRDeck = coalesce(VRDeck, 0))“train” veri çerçevesindeki “ShoppingMall”, “Spa” ve “VRDeck” sütunlarındaki eksik değerleri (NA) sırasıyla 0 ile değiştirir. Bu işlem, belirli sütunlardaki eksik değerleri belirli bir değerle doldurmak amacıyla kullanılır.
test <- test %>% mutate(ShoppingMall = coalesce(ShoppingMall, 0),
Spa = coalesce(Spa, 0),
VRDeck = coalesce(VRDeck, 0))“test” veri çerçevesindeki “ShoppingMall”, “Spa” ve “VRDeck” sütunlarındaki eksik değerleri (NA) sırasıyla 0 ile değiştirir. Bu işlem, belirli sütunlardaki eksik değerleri belirli bir değerle doldurmak amacıyla kullanılır.
train %>% describe_all()## # A tibble: 18 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 4 NA NA NA
## 3 CryoSleep fct 0 0 3 NA NA NA
## 4 Destination fct 0 0 4 NA NA NA
## 5 Age dbl 0 0 84 0 28.8 79
## 6 VIP fct 0 0 3 NA NA NA
## 7 RoomService dbl 0 0 1277 0 225. 14327
## 8 FoodCourt dbl 0 0 1507 0 448. 29813
## 9 ShoppingMall dbl 0 0 1115 0 170. 23492
## 10 Spa dbl 0 0 1327 0 305. 22408
## 11 VRDeck dbl 0 0 1306 0 298. 24133
## 12 Name chr 200 2.3 8474 NA NA NA
## 13 Transported lgl 0 0 2 0 0.5 1
## 14 ailenum chr 0 0 6217 NA NA NA
## 15 ailesira chr 0 0 8 NA NA NA
## 16 deck chr 199 2.3 9 NA NA NA
## 17 num chr 199 2.3 1818 NA NA NA
## 18 side fct 0 0 3 NA NA NAtrain <- train %>% select(-Name)“train” veri çerçevesinden “Name” sütununu çıkararak güncelleme yapar. Bu işlem, dplyr paketinin %>% (pipe) operatörü ve select fonksiyonunu kullanır.
test <- test %>% select(-Name)“test” veri çerçevesinden “Name” sütununu çıkararak güncelleme yapar. Bu işlem, dplyr paketinin %>% (pipe) operatörü ve select fonksiyonunu kullanır.
train %>% describe_all()## # A tibble: 17 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 4 NA NA NA
## 3 CryoSleep fct 0 0 3 NA NA NA
## 4 Destination fct 0 0 4 NA NA NA
## 5 Age dbl 0 0 84 0 28.8 79
## 6 VIP fct 0 0 3 NA NA NA
## 7 RoomService dbl 0 0 1277 0 225. 14327
## 8 FoodCourt dbl 0 0 1507 0 448. 29813
## 9 ShoppingMall dbl 0 0 1115 0 170. 23492
## 10 Spa dbl 0 0 1327 0 305. 22408
## 11 VRDeck dbl 0 0 1306 0 298. 24133
## 12 Transported lgl 0 0 2 0 0.5 1
## 13 ailenum chr 0 0 6217 NA NA NA
## 14 ailesira chr 0 0 8 NA NA NA
## 15 deck chr 199 2.3 9 NA NA NA
## 16 num chr 199 2.3 1818 NA NA NA
## 17 side fct 0 0 3 NA NA NAtest$deck <- addNA(test$deck)“test” veri çerçevesindeki “deck” sütunundaki tüm değerleri bir eşdeğer değeri olan NA (eksik değer) ile değiştirir. addNA() fonksiyonu, bir vektördeki tüm benzersiz değerlere NA değerini ekler.
levels(train$deck)[is.na(levels(train$deck))] <- "NA"“train” veri çerçevesindeki “deck” sütunundaki faktör seviyelerinden herhangi biri NA (eksik değer) ise, bu seviyeleri “NA” karakter dizisi ile değiştirir. Yani, NA değerlerine özel bir kategori atanmış olur.
levels(test$deck)[is.na(levels(test$deck))] <- "NA"“test” veri çerçevesindeki “deck” sütunundaki faktör seviyelerinden herhangi biri NA (eksik değer) ise, bu seviyeleri “NA” karakter dizisi ile değiştirir. Yani, NA değerlerine özel bir kategori atanmış olur.
train %>% describe_all()## # A tibble: 17 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 4 NA NA NA
## 3 CryoSleep fct 0 0 3 NA NA NA
## 4 Destination fct 0 0 4 NA NA NA
## 5 Age dbl 0 0 84 0 28.8 79
## 6 VIP fct 0 0 3 NA NA NA
## 7 RoomService dbl 0 0 1277 0 225. 14327
## 8 FoodCourt dbl 0 0 1507 0 448. 29813
## 9 ShoppingMall dbl 0 0 1115 0 170. 23492
## 10 Spa dbl 0 0 1327 0 305. 22408
## 11 VRDeck dbl 0 0 1306 0 298. 24133
## 12 Transported lgl 0 0 2 0 0.5 1
## 13 ailenum chr 0 0 6217 NA NA NA
## 14 ailesira chr 0 0 8 NA NA NA
## 15 deck chr 199 2.3 9 NA NA NA
## 16 num chr 199 2.3 1818 NA NA NA
## 17 side fct 0 0 3 NA NA NAtest %>% describe_all()## # A tibble: 16 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 4277 NA NA NA
## 2 HomePlanet fct 0 0 4 NA NA NA
## 3 CryoSleep fct 0 0 3 NA NA NA
## 4 Destination fct 0 0 4 NA NA NA
## 5 Age dbl 0 0 83 0 28.7 79
## 6 VIP fct 0 0 3 NA NA NA
## 7 RoomService dbl 0 0 846 0 219. 11567
## 8 FoodCourt dbl 0 0 902 0 429. 25273
## 9 ShoppingMall dbl 0 0 715 0 173. 8292
## 10 Spa dbl 0 0 833 0 296. 19844
## 11 VRDeck dbl 0 0 796 0 305. 22272
## 12 ailenum chr 0 0 3063 NA NA NA
## 13 ailesira chr 0 0 8 NA NA NA
## 14 deck fct 0 0 9 NA NA NA
## 15 num chr 100 2.3 1506 NA NA NA
## 16 side fct 0 0 3 NA NA NAtrain$aile <- ifelse(duplicated(train$ailenum) | duplicated(train$ailenum,
fromLast = TRUE), 1, 0)“train” veri çerçevesindeki “deck” sütunundaki faktör seviyelerinden herhangi biri NA (eksik değer) ise, bu seviyeleri “NA” karakter dizisi ile değiştirir. Yani, NA değerlerine özel bir kategori atanmış olur.
test$aile <- ifelse(duplicated(test$ailenum) | duplicated(test$ailenum,
fromLast = TRUE), 1, 0)“test” veri çerçevesindeki “deck” sütunundaki faktör seviyelerinden herhangi biri NA (eksik değer) ise, bu seviyeleri “NA” karakter dizisi ile değiştirir. Yani, NA değerlerine özel bir kategori atanmış olur.
head(train[, c("PassengerId", "ailenum", "ailesira", "aile")], 20)## # A tibble: 20 × 4
## PassengerId ailenum ailesira aile
## <chr> <chr> <chr> <dbl>
## 1 0001_01 0001 01 0
## 2 0002_01 0002 01 0
## 3 0003_01 0003 01 1
## 4 0003_02 0003 02 1
## 5 0004_01 0004 01 0
## 6 0005_01 0005 01 0
## 7 0006_01 0006 01 1
## 8 0006_02 0006 02 1
## 9 0007_01 0007 01 0
## 10 0008_01 0008 01 1
## 11 0008_02 0008 02 1
## 12 0008_03 0008 03 1
## 13 0009_01 0009 01 0
## 14 0010_01 0010 01 0
## 15 0011_01 0011 01 0
## 16 0012_01 0012 01 0
## 17 0014_01 0014 01 0
## 18 0015_01 0015 01 0
## 19 0016_01 0016 01 0
## 20 0017_01 0017 01 1test <- test %>% select(-c(ailenum, ailesira, num))“test” veri çerçevesinden “ailenum”, “ailesira” ve “num” sütunlarını çıkararak güncelleme yapar. Bu işlem, dplyr paketinin %>% (pipe) operatörü ve select fonksiyonunu kullanır.
train %>% describe_all()## # A tibble: 18 × 8
## variable type na na_pct unique min mean max
## <chr> <chr> <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 PassengerId chr 0 0 8693 NA NA NA
## 2 HomePlanet fct 0 0 4 NA NA NA
## 3 CryoSleep fct 0 0 3 NA NA NA
## 4 Destination fct 0 0 4 NA NA NA
## 5 Age dbl 0 0 84 0 28.8 79
## 6 VIP fct 0 0 3 NA NA NA
## 7 RoomService dbl 0 0 1277 0 225. 14327
## 8 FoodCourt dbl 0 0 1507 0 448. 29813
## 9 ShoppingMall dbl 0 0 1115 0 170. 23492
## 10 Spa dbl 0 0 1327 0 305. 22408
## 11 VRDeck dbl 0 0 1306 0 298. 24133
## 12 Transported lgl 0 0 2 0 0.5 1
## 13 ailenum chr 0 0 6217 NA NA NA
## 14 ailesira chr 0 0 8 NA NA NA
## 15 deck chr 199 2.3 9 NA NA NA
## 16 num chr 199 2.3 1818 NA NA NA
## 17 side fct 0 0 3 NA NA NA
## 18 aile dbl 0 0 2 0 0.45 1 base::is.data.frame(data)## [1] FALSEtrain_set <- train[2:15]“train” veri çerçevesinden sadece 2. sütundan 15. sütuna kadar olan sütunları içeren bir yeni veri çerçevesi olan “train_set”i oluşturur.
test_set <- test[2:14]“test” veri çerçevesinden sadece 2. sütundan 15. sütuna kadar olan sütunları içeren bir yeni veri çerçevesi olan “train_set”i oluşturur.
##Naive Bayes
library(e1071)
nb_modelim<- naiveBayes(Transported~ ., data= train_set )
preds<-predict(nb_modelim, newdata= test_set[-13], type = "raw") %>% data.frame()“e1071” paketini kullanarak bir Naive Bayes sınıflandırma modeli oluşturur. Oluşturulan modeli kullanarak “test_set” veri çerçevesindeki gözlemleri tahmin eder.
y_pred = ifelse(preds$TRUE. > 0.5, 1, 0)“preds” veri çerçevesindeki “TRUE.” sütunundaki olasılık değerlerini 0.5’ten büyükse 1, küçükse 0 olarak sınıflandırarak “y_pred” adlı yeni bir vektör oluşturur
(510+1016)/(510+1016+78+569)## [1] 0.7022549nb_son = naiveBayes(Transported ~ ., data = train_set)Oluşturulan bu model, daha sonra test veri seti üzerinde tahminler yapmak veya modelin performansını değerlendirmek amacıyla kullanılabilir.
preds <- predict(nb_son, newdata = test_set, type = "raw") %>%
data.frame()daha önce oluşturulan nb_son Naive Bayes modelini kullanarak “test_set” veri çerçevesindeki gözlemleri tahmin eder ve tahmin edilen olasılık değerlerini içeren bir veri çerçevesi olan “preds”i oluşturu
y_pred = ifelse(preds$TRUE. > 0.5, TRUE, FALSE)“preds” veri çerçevesindeki “TRUE.” sütunundaki olasılık değerlerini 0.5’ten büyükse TRUE, küçükse FALSE olarak sınıflandırarak “y_pred” adlı yeni bir vektör oluşturur.
Transported <- as.character(y_pred)
PassengerId <- test$PassengerIddaha önce oluşturulan “y_pred” vektöründeki sınıflandırılmış sonuçları karakter dizisine (as.character) dönüştürür ve bu değerleri “Transported” adlı yeni bir vektöre atar. Aynı zamanda, “test” veri çerçevesindeki “PassengerId” sütununu “PassengerId” adlı başka bir vektöre atar.
Transported <-as.vector(Transported)“Transported” adlı vektörü as.vector fonksiyonu kullanarak bir vektör olarak günceller. as.vector fonksiyonu, belirli bir veri yapısını vektöre dönüştürmek için kullanılır.
sample_submission <- cbind(PassengerId, Transported)“PassengerId” ve “Transported” vektörlerini birleştirerek yeni bir veri çerçevesi olan “sample_submission”ı oluşturur. cbind fonksiyonu, sütunları birleştirmek için kullanılır ve bu durumda “PassengerId” ve “Transported” sütunları “sample_submission” veri çerçevesini oluşturur.
sample_submission <- as.data.frame(sample_submission)“sample_submission” adlı nesnenin tipini veri çerçevesine dönüştürür. Yani, “sample_submission” nesnesinin içeriği aynı kalmakla birlikte, artık bir veri çerçevesi olarak ele alınacaktır.
sample_submission$Transported <- str_to_title(sample_submission$Transported)Bu tür bir dönüşüm genellikle metin verilerinin düzenlenmesi veya görsel sunumu için kullanılır. Özellikle kategorik değişkenlerdeki etiketlerin standart bir formatta olması, analiz ve raporlama süreçlerini kolaylaştırabilir
write.csv(sample_submission, "sub_nb.csv", row.names = FALSE,
quote = FALSE)sample_submission” veri çerçevesini bir CSV dosyasına (“sub_nb.csv”) yazma işlemini gerçekleştirir
svm
library(e1071)
fit_svm <- svm(Transported ~ ., data = train_set,
type = "C-classification",
kernel = "linear")“e1071” paketini kullanarak bir destek vektör makinesi (SVM) sınıflandırma modeli oluşturur. svm fonksiyonu, belirtilen sınıflandırma değişkeni ile diğer bağımsız değişkenler arasındaki ilişkiyi öğrenen bir SVM modeli oluşturur.
y_pred = ifelse(preds$.== TRUE, 1, 0)“preds” veri çerçevesindeki her bir gözlem için “TRUE.” sütunundaki değeri kontrol eder ve bu değer “TRUE” ise 1, değilse 0 olarak sınıflandırır.
(574+661)/(574+661+127+203)## [1] 0.7891374svm_son = svm(Transported ~ ., data = train_set,
type = "C-classification",
kernel = "linear")“train_set” veri çerçevesindeki “Transported” sınıflandırma değişkenini diğer tüm değişkenlere bağlı olarak kullanarak bir destek vektör makinesi (SVM) sınıflandırma modeli olan “svm_son”u oluşturur. Oluşturulan bu model, “C-classification” tipinde bir sınıflandırma modelidir ve doğrusal bir çekirdek (kernel) kullanır (kernel = “linear”).
y_pred = preds$.“preds” veri çerçevesindeki “TRUE.” sütunundaki tahmin edilen değerleri “y_pred” adlı bir vektöre atar. Ancak, dikkat edilmesi gereken bir konu var: “TRUE.” sütunu muhtemelen mantıksal (logical) değerleri içerir (TRUE veya FALSE)
Transported <- as.character(y_pred)
PassengerId <- test$PassengerIddaha önce oluşturulan “y_pred” vektöründeki sınıflandırılmış sonuçları karakter dizisine (as.character) dönüştürür ve bu değerleri “Transported” adlı yeni bir vektöre atar. Aynı zamanda, “test” veri çerçevesindeki “PassengerId” sütununu “PassengerId” adlı başka bir vektöre atar.
Transported <-as.vector(Transported)“Transported” adlı vektörü as.vector fonksiyonu kullanarak bir vektör olarak günceller. as.vector fonksiyonu, belirli bir veri yapısını vektöre dönüştürmek için kullanılır
sample_submission <- cbind(PassengerId, Transported)“PassengerId” ve “Transported” vektörlerini birleştirerek yeni bir veri çerçevesi olan “sample_submission”ı oluşturur. cbind fonksiyonu, sütunları birleştirmek için kullanılır ve bu durumda “PassengerId” ve “Transported” sütunları “sample_submission” veri çerçevesini oluşturur.
sample_submission <- as.data.frame(sample_submission)“sample_submission” adlı nesnenin tipini veri çerçevesine dönüştürür. Yani, “sample_submission” nesnesinin içeriği aynı kalmakla birlikte, artık bir veri çerçevesi olarak ele alınacaktır
library(stringr)stringr kütüphanesi, R programlama dilinde metin işleme ve manipülasyon işlemleri için kullanılan bir kütüphanedir.
write.csv(sample_submission, "sub_svm.csv", row.names = FALSE,
quote = FALSE)Decision Trees
library(rpart)
library(rpart.plot)
library(randomForest)## randomForest 4.7-1.1## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:ggplot2':
##
## marginüç farklı R kütüphanesini yükler:
rpart: Karar ağacı modelleri oluşturmak için kullanılan “rpart” paketini yükler. rpart.plot: “rpart” paketindeki karar ağacı modellerini görselleştirmek için kullanılan “rpart.plot” paketini yükler. randomForest: R’deki rastgele orman modelleri oluşturmak ve yönetmek için kullanılan “randomForest” paketini yükler. Bu kütüphaneler, makine öğrenimi ve istatistiksel analizle ilgili birçok işlevselliği içerir. Örneğin, “rpart” ile karar ağacı modelleri oluşturabilir ve “rpart.plot” ile bu modelleri görselleştirebilirsiniz. “randomForest” paketi ise rastgele orman modelleri oluşturmak için kullanılır ve genellikle sınıflandırma ve regresyon problemlerinde kullanılır.
library(caret)## Zorunlu paket yükleniyor: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftcaret kütüphanesi, sınıflandırma, regresyon, kümeleme ve diğer makine öğrenimi görevlerini kolaylaştıran ve standartlaştıran bir R kütüphanesidir. Bu kütüphane, model eğitimi, hiperparametre ayarı, performans değerlendirmesi ve diğer birçok işlemi gerçekleştirmek için kullanılır.
fit_tree <- rpart::rpart(Transported ~ ., data = train_set)
summary(fit_tree)## Call:
## rpart::rpart(formula = Transported ~ ., data = train_set)
## n= 8693
##
## CP nsplit rel error xerror xstd
## 1 0.62521454 0 1.0000000 1.000254 0.0001741426
## 2 0.08751388 1 0.3747855 1.184750 0.0167475694
## 3 0.04828533 2 0.2872716 1.275394 0.0183074680
## 4 0.02910718 3 0.2389862 1.258982 0.0185621258
## 5 0.02455280 4 0.2098791 1.271194 0.0187608931
## 6 0.01310940 5 0.1853263 1.305570 0.0193755534
## 7 0.01000000 6 0.1722169 1.306817 0.0194079183
##
## Variable importance
## ailenum CryoSleep RoomService Spa VRDeck ShoppingMall
## 54 12 9 8 8 6
## ailesira FoodCourt Age
## 2 1 1
##
## Node number 1: 8693 observations, complexity param=0.6252145
## mean=0.5036236, MSE=0.2499869
## left son=2 (3780 obs) right son=3 (4913 obs)
## Primary splits:
## ailenum splits as 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, improve=0.6252145, (0 missing)
## CryoSleep splits as LRL, improve=0.2117218, (0 missing)
## RoomService < 0.5 to the right, improve=0.1204234, (0 missing)
## Spa < 0.5 to the right, improve=0.1183706, (0 missing)
## VRDeck < 0.5 to the right, improve=0.1103404, (0 missing)
## Surrogate splits:
## CryoSleep splits as LRR, agree=0.650, adj=0.196, (0 split)
## RoomService < 0.5 to the right, agree=0.649, adj=0.192, (0 split)
## Spa < 0.5 to the right, agree=0.634, adj=0.158, (0 split)
## VRDeck < 0.5 to the right, agree=0.628, adj=0.146, (0 split)
## ShoppingMall < 0.5 to the right, agree=0.624, adj=0.134, (0 split)
##
## Node number 2: 3780 observations, complexity param=0.0245528
## mean=0.05291005, MSE=0.05011058
## left son=4 (3173 obs) right son=5 (607 obs)
## Primary splits:
## ailenum splits as L-L------L-LLL--RL-LLLL--LLLL-L--LLL--LLL-L-L-LLLL---LL---L----L---L-LLR----L----LL-LL---L---L-L-L-LLL----LL---LLL-L--L-LLR--LL-L----L-L--LLLL-RL-LL-LLLLL-L-LLL--LLL-LL---LLL--L-L--L----L-L------LL-LLL-LL-L--LLLLL--L-LL---LLL-L--L--L-LL-L--L-LLL-L----LL-LLLL-LL--LL--LLLLLL-LL--------L----L-LLLLL-------LR-L--LL-LL--L-L-LL-LLLL----LLR-------LL--L-L---LL-LLL---LLLL---L----L-LL-L------LL-LL--L----LLLLLLL--L---LL-LL--------L--LL-LL--L---L-----L-L-LL-L-LL-LLLLLL-L-L-LL--R-----L-----L-L--LLLL---LLLLL-R-LL---L-LL-L--RL-L-LLL--L-LLR-LL-L-L-L---L---LLL-----L------LLLLLLL---L--LL-L-LL-LLLL-LLLL--L-L---LLL-L---LLLLL--L--L-----L---LLL-L--LLL--L--LLL--LL-LL---R-L-LLL-LL-L-LL--L--L-L-LL---LRLL--LLL-L--L--LLL---LLL---LLL---LL-LLLLLL-LL-L---L-L-LRLL----L--L-L-L-L------RL--RLL--------L-LLL----L-L-LRLL-LL--L-LLLL-RL----LLLL----LL----L----LL-LL-LLL-LLL-LLLLL--LLL-L--LLL-L-L-L---L--LR--L--R-LL--LLL-LLLL--RLL--L--L--LLLLL--LL-L--L-L--RL-L-L-L-LLL-LLLLL-LLLLL-----LL-LRLLLL----L--R-L-LL-L-LLL-L-LL--LL-LLL-LL-RLL-L-----L---LLL--R-L-L-L-L-LLLLLLLL---LLL-L-L-L----L-----LL-L-L--LL-L-----L-RL--LLL-L-LL-LL--L-LL-LLRL--LRLLLLLLLL-RLRLL-LLL----LLR-LLLLL--LLL-L-L---L-L--LLL--RL--LLLLL---L-L---L--LL--LLLLL--LL-LL--LLLL--L-L-L--LL--LL--LL-LLLLL----L-L--LRL--L-LLLLL--LLL-LLL--LL-LLLLLLLL--LL-LLL---LL-LLLLLLLLLL-L---RLL---L-LL---L-LL--L--L-LL-L-LLL---LLL--L---LL-LL--L---L--LL-R-L-LLL----L-LL-LL-L---L-LR-LL---LLLLLLLL--LLL-L-LLL-LLLL-LLL------LLL-L-LLLLL----L-L-L----LLLLLLLLL-LL---L-LL-LLLLL-L----L-LLLLLLRL---L-LLLL-----LL-L-L---L-LL-LL-LLLL-LL-LLLRL-L--L-LLLL--LLLL-L--LL--LL-LRLL--L-----LLLLL--L-LLRL-LLL-LLLLL-LL-------LLL--L---LLL-LL--L-LLL--LL-LLL---LLLLLLLL-LL-LLLLLL-L-LLL-LL-LR-L--L-L--LL------L---L-LLL-LL-LR-L-----LRL------LLLLLL--RLLL----LL-R-L--LL-LLL-LL-L-L--LLLLLL-LLLLL--LLL-L-L--LLLLL--L-LL-LL-L-LLLLLLL--LL------LL-RL---LLL----L-L--LLLL----RLL-L-LRL--LRLLLLL-LL----LLLR---LL--L-LLR-LL---L-L------L-L-LLL-LL--L-LLL-----L--L----L-LL-LLL-LLL-LRLL-L--LLL-LLLLL---L---LLLL--LLLR--LL--LLLLLLLL--LL--LL--LLL-LLL-L-L----L-LLLL-LLLLL---LL--LLLLLLLLLL-L-LLLLLL---L-L----LL--L-L-L--L-L----L--LL-LLLLL-----LL--L--LLL----LL-LRLL----L---LLL--LL--L-RLLLLLL-L-LLLL--LRLLLL--L---LRRL-LLLLLLL---LLLLL----LR-L---L------L-L-L---LLL-LL-LLL-L----LLL-L---R-L-LL--L-LL--RL-LL--LL-LR-L-----L-L-L--RL-L--LL--LLL--LLL---LL----LLL--L-LL-LL----L-LLL---L---LLL---LL--LL--L-L--LLL-------L---LL--LL---LL-L-L---L--L-L--LL--LL-L-L--LLL----LL---L------L--L--L--L-LL-L-L-L---L-LLLL-L-----L-L-L---L--LL--LL-LL---L--LR-------L----L--L-L---L-LL-L-------L-L-L--L-----LLLLL-L-----L-LLLL-L-L-LLLL-LL--LLLLL--LLL-LLL-L-----R-L-----L-LL-----R-L--RL----L--LL----L--LL--LL--LLLLLLLLL-LL-LRL-----LL-L-----L-LL-LLL-R-L-L--L-----LLLLRL-LLLL--R---L-L-LLL-L-L--LL-LLL---L-L---LLLLLLL-----LL-LL-L-LL-L--L---L-L---L--L-LLLL-LLL----L-LLL----LL--L-LLL--L--LLL---LL----L-LL-L---LLL--RL-L-LLL--L--L--L--L-L-LR-RLL----L--L--L--L-L----LL-------LL-L--LL--L--L-L---L---L-L-L-L-L-L-R----L---LL--L----L---LLLL-L--LLL----L-LL--L-L-L-LLL--L--LL-------L----L-LLL--LLL--LLL-L--LLL----LRL-L-LL---L--LLLL--LL----LLL--L---L--LLLL--L--LLLL---L-LLL---LLL---R---L-LLLL-LL----LL---L----RLL-L--LL-----LL--L--LLLLR--LL--LL-LL------L----LL-LL-L-LLL----L--L--L----LLLLLL--LLL-----L----L-R----LLL---L----LL-LLL---L---L--LL-L--L-RL------L-LLLL--LL---LLLL-L-L-L--L--L--LL--LLLLLLLL--L-----L------LL---L--R-LL----L--LLL-----LLL---LLLL----L-L-L-L--L---LL--L--LLLL-L-L-L---LL---L--L-L---L-L-LLLLL-L--RLL--------L---L-LL-----LRL---------LL--LL-----LLL--LL------LLLRL-----LLL-L-R----L-LL-LLL-L---L--L-L----L---L------LL----LL--L-LLL---L-L---LL----------L---L------L-L---L-L-L-L-LL-L-LL----L--LL--LLLL-LL---L--L-LLLLLLL--RL-L-L--LLRL--LL-LR-LL-----LLLLLLL-RRL--LL--L--L-LL-L---LLL----L-L-LL-----LL-L-L--RLL----L-L---L-L--R-L--LR-L-L--LL----L--LL-L-LLL-L--L--L----------R-LL-LL---RL---LL-LLLL---LL-LL----LLL-LL-------------L--------LL---LL-LL--L---LL-LLLLL-LL--L-L----L-LLL--L-LL-L-L-L-L-L-LLL--------L--L------LLLL--L-L---LL-LL---L-----LL---L-L---L-L-LL------LLL-L----LL--L-L-LRR--LL--L---L--LL-LLLL---L--L--L-L--L--------LL----L---LLL-----LL-L--LL-L---L-------L--LLLL-L-L-RL--LL-RL-LL------L--L----L-L-LL-L--L----LLLL------LL---LL---L-LL-----LLL-LLL---LL--RR-L--LL-----L-RLLLR-----LLLLL-LL--L--L--LL--L----LL----LL--L-L---L--LL----L----L---L-LLLL-L------L-R---LLL-L--L-L------L--RL--L--L--L--RLL---LL-L-R----LLLLL---L--RL-LRL-----L----LL-L-L-LL-LL--LL-L----LLL---LLL---LL--LL-LR-LLLL-----L-LL-L--LL-LLL-L---L-LLL-LL-L-LL----L------L---LLL---------L-LL-L--LLL---L---LLL----L-L-----L--L-L----R--L-LLL-----LLR-----L--R-L-----LL----L----LL--L----LR-LL--L-L-L---L----LL-L-L--L-LLL--L-RL----L---L-L-LLL----L---LL---L---LL----L----L-LR--LL-L--L-L--L-L------L----L-L--L-LLLLLLLL-LL---L-L-RL--L-R-LLLLL-LL---L-L---L-LL----L----LL-LLL--LLL-L--L-LLL--L-LLRLL--LLL-L-L-L-L-L--L---R-L---LLL-LL-LLL-L-RLL-LLRLL--L-L----LL-L--L--L-L-L-L-R----L--LLL-L-LL--L------L-LLLL-LLLL-L--LL--L-LL---L--L-L-LL---L-L---LLL---LL---LLLLL-L-LL-L-LLLLLLLLLL--LLLL----L---LLLL-L--LLL--LLLLL---LL-L-LL-LL--L--LLLL--L--LLLLLLLL----LLL------L--L--LL----LL---LL--L-L-LLLLLL-L-LLL-------L--LLLLL---LL-L--LLLLL--LL-------L----LL-L------L-LL-LL--LLLL--------L---L-----L-LLL-LLL--LLL--L-LL---LL-LLLL-L--LL-L-L--L-L--L-LL-L--LL--LL---L-LL--LL---LL-L-L--LL-LLRLLL-LL-LLL-LLL--L-LLL--LL-LL-LLL---L-LL-LRLL-L---LL--R--LLLL--L-L------L-R-RL-R--L-RL-L-LLLL--R-LLL----LL-LLL--LLL----L-L----LL-L--L-L----RL-----LL-LL-L--LLL--L-LL-L--L-LLLL--LL-L--LL---LLLL---LLL--L-L----LLL-LLL-L-LR--RLL-RL-L--LL------LL-LLL-LLLLLL-RL--LL-L-L-LL-LL---LL---L-LL--L-L---LL----LLLLLL-LL-L-L-LLL-L-L------L-----LLLLL---LL--L-L---L-LLL-L-L--LLLLL-L-L--LL-L-LL--L--L-R---L---LLL-LLRLL--LLLL------LLLL-L---L-LL-L-L-LL-LLL----L-L-LL-L-L-L-LLLLL-LLL-L--L---L---LR-L-LL-L-L-L-LLLL-LL-LLLL-LL-----LL-LLL---L-LLLL-LLL----LLL--L-L-LL------LLLLLL-LLLL---LL-LLL----LR---L-LLRL--L-L-L-LL--LL-R--LLL-LLL--LLR--LLLRLLL-L--LLLLLLLL--LL----RL-L--LLL----L--L--LL-L-L-L-LLLLL-L-L--L-L-L-----LLL-L--L-L--L-LL-L-----L-----L-L--L-LRLL-L----L--LL-L-LLLL-----L---LR--L--LLL-R-LL--L--LLLLLLLLL---LLL-LLL---LL--LLR-LLL-----LLLLL-L-L-----L-L-LLLLLL-LR--LL-L-L-LRL---LLL-LL-LLL-L--LLLL--LLL--LLLLL--LLLL-L------L--LLL--LLL---L-L-LL--LL--LL-----L---L-----L-LLL----L-LLL---L-----L-L-L-------LL--, improve=0.28168690, (0 missing)
## ailesira splits as LLRRRRRR, improve=0.11212740, (0 missing)
## Age < 12.5 to the right, improve=0.11123430, (0 missing)
## CryoSleep splits as LRL, improve=0.07443996, (0 missing)
## RoomService < 0.5 to the right, improve=0.03282292, (0 missing)
## Surrogate splits:
## ailesira splits as LLRRRRRR, agree=0.903, adj=0.397, (0 split)
## Age < 12.5 to the right, agree=0.869, adj=0.183, (0 split)
##
## Node number 3: 4913 observations, complexity param=0.08751388
## mean=0.8503969, MSE=0.127222
## left son=6 (1676 obs) right son=7 (3237 obs)
## Primary splits:
## ailenum splits as -R-RRRRRR-R---RL--R----RR----R-LL---RR---R-L-R----RRR--LRR-RRLL-LLR-L---LRRR-RRLR--R--RLR-LRL-R-R-R---RLRL--RRR---L-RR-R---LR--R-RRRR-R-RL----R--L--R-----R-R---RL---R--RRL---RR-R-RR-RRRR-L-RRRRRR--R---R--R-RR-----RL-R--RRR---R-RR-RR-R--R-LR-R---R-LRRR--R----R--RR--RR------R--LRLRRRRR-LRRL-R-----RRLRRRR--R-RR--L--RL-R-L--R----RRLR---RRRRRRR--RR-R-RRL--L---RRL----RRR-RRLR-L--L-RRLRRL--R--LR-RRLR-------RL-LRL--R--RRRLLRRR-RR--L--RR-RRR-RLRRR-L-R--R-L--R------R-R-L--RR-LRRLR-RRRRR-L-LR----RRR-----R-R--LRL-R--L-LR--L-R---RR-R---R--R-R-R-RRL-LLR---RRLRR-RRLRRR-------RRR-RR--R-R--R----R----LL-R-LRR---R-RLR-----LR-LL-RLRRL-LRR---R-RR---RR-RR---RR--R--RLR-R-L---L--R-R--LR-LR-R-R--RRR----LR---R-RR-RR---RRR---RRR---RRR--R------R--L-LRR-R-R----LLRR-RR-R-R-L-RRRRRR--RL---RRLLLRRL-R---RRRR-R-R----R--RR-R----L--RRRR----RRRR--LRRR-RRRR--R--R---R---R-----RR---R-RL---R-L-R-RLR-LR--RL-RR-L--LR---R----RL---LR-RR-LR-----RL--R-RL-R-RR--R-R-R-R---L-----R-----RRLRL--R------RRRR-LL-L-R--R-R---R-R--RR--L---L--R---L-RRRRR-RRR---RR-R-R-R-R-R--------RLR---R-R-R-RRLR-RRRRR--L-R-RR--L-RLRRL-R--RR---R-R--R--RL-L--R----RR----------R-----R---RRLL---R-----LR---R-L-RRR-R-LR---RR--RR-----LRL-R-RRR-RR--RR-----RR--L--RR----LR-R-R-RR--RR--RR--L-----RRLR-R-LR---RR-R-----RR---R---LR--R--------LL--R---RRR--R----------L-LRR---RRR-R--RRR-L--RR-LR-R--R-R---RLR---RL-RLR--R--RR-RRR-RR--L-R-R---RLRL-L--R--L-RRL-R--R--RRR--------RR---R-L---R----L---RRRRRR---R-R-----RRRR-R-L-RRRR---------R--RLR-R--R-----R-LRRR-R--------RRR-R----RRRRR--R-R-RRR-R--R--R----R--R-----R-LL-L----RR----R-RR--LL--R----LL-RLRRR-----RR-R----R---R-----R--LRLRRRR---RR-RRR---R--RR-R---RL--R---RRR--------R--R------R-R---R--L--R-RL-L-RR--RRRRRR-RLL-R---L--L--R-RRRRR---LRLRRR------RL----RLLL--R-R-RR--R---L--L-R-RR------R-----RR---R-L-RR-----RR-R--R--R-R-------RR--RRRLRR--R--RRL---RLLR-L-RR----RRRR---R-R---RR-------R--LRRR----RRL--LL-R---R--RRR-R-RRLRRR-L-R---R--RR-R---RRRRR-LR-RRRR-R--R---R---L----R-RL---R-----RRR-RLR----RR----RR--RR--------RR--RR--RR---R---R-R-LRLR-R----R-----RRL--LR----------L-R------LRR-R-RLRR--RR-R-R-RL-R-RRLR-RR--R-----LRRRR--RR-RR---RRRR--R----RRRR-RRR---RR--LR-R-------R-R----RR------RR-RRR----R-------RRR-----RRLR--R-RLR-RLRRRL-R-R-LRR---R--R---R-RRRR---R-RLR-R-R--RR-L--RL--R--RR--L--R-RRLRR-R-R-RR--R-RL--RR---RR---RRR--RLLL---RR-R--R--RRRR-R---RRL-LLL---LLR--LR--RL-L-RR---LRRLRRR-RRL--LR--RRR--R-L-RRL-RR-R-RR--RR--R-R-RR---LRRR--RRR-RRLRRR-LR-RL-RR-R--R-R-L-RRR-R----R-RRRRR-R-R-LLR-RR--RL--L--LLR-LR--RLRRRRL-RLRL-RL-R-LRR-R--R-RRLRRRR-R-R-RR-RRRRR-----R-RRRRR-R----R-L-R----R--RR-----RR---R---R-RRRRR-R-RLRRR-L--LRRRR-R-RR--RRLR-RL--RRLR-LL--RR--RR---------R--R---RLRRL--R-RRRRR-R--R---R-R-R-LL-RLRRR------R----RR-RRR-R-R---R-L-RR--R---LRR-L-RRR-------RRRRR--R--R-R--R-RR-RRR-R-RRL-RR-R----R---RRRR-R---RRRR--RR-L---LL-RR---RRR--RRLR-L--R-RRR---RR--R-R---RR-RR-RR-RR-R-R--R---RRRL-RR-RR-RR-R-RRRR--RRRLLRL--R-RR--LR-RR-R-RRR-RRR-R-R-L-R-R-R-RRRR-RRR--RL-RRRR-RLR----R-LR---RRRR-R--RR-R-R-R---RR-RR--LRRRLRR-RRRR-R---RL---RL---L-LR---RRRR---R-L--RRR-RL----RR--RRRR---RR-RRR-RR----RR-RR----LRR-R---RRR---RRR-RRR-L----R--RRRR--RRR-RRRR---R-RR--RRRRR--RR-RR-----LR--RR--R--RLRRRL-LRLR--L--L-R---RRRL-RL-RR-RRRR------RR---RRRRR-RLRR-L-RRRR---RRL-RRRR--R---RRR-RRR-RR--R-RR-R--RLRRRR-R----RR--RRR----R-R-R-RR-RR-RR--RR--------LR-RRRRR-RRRRRL--RRR-RR-R--RRRR-RR---RLLRL---LRR----RRRR-R-R-L-RR-RRL--RR-RR----R-R-L-RLR--RRL-LR-R-LRR-R-R-----R-RL---RRRRRRRR-RRR-R--RLLRR---RRRLRRRLR--RR--RLRRR---RL--RRRRRR-----RRRLR---R-L-LLRR-R--R---R-RRR-RR-R-RRRR-RRR-RRRRRR--RRRR--RR-R---RRR-R-RRR--RRRRRRLLRR-RRR-RLRRRR-R-RRR-R-L-R-R--R-R--RRRR-RR--RR----R--RLR-RR-R-------RR--L-R-RL----RR--R--R--RRRRR-------R---RR--RR-RR-R--R-RLL---RRRR-R-R--RRLLR--R-R-RL---LLRR-R-RRR-R-RR-R-RR--R-L-RR--LRRR-RL--R-R---R-RR-RR-LLRRRRRRRR-R--R--RRR--RRL--R----LLR--R--RRRL---R--RRRLRRRRRRRRR-RRRRRRLL--RRR--R--LR-RRR--R-----R--RR-R-RRRR-R---RR-R--R-R-R-R-R-R---LRLRRLRR-RR-RRRRRR----RR-R-RRR--R--RRR-RRLRL--RRR-R-RRR-L-L--RRRRRR---R-RRRR--RR-L-R---RL--RR-RRR-RL--R----LLR-RL-LR-R-RR-LRLLLRRR--RRRR-RRR---RLRRR--R-RR--R-RRL-RRLLLLR-LR----R-R-R--RR--R--R--RRRRRR-RR-RLRR-L-R--R-RR-LRRR----RLRRRR--RRR--RRR-R--RRRLR---R---RRR--RR--R-RR--RRRRR-R-----RRRLR-----L--RR-RR-RR--RR-RRRR--RRLL--RR-R-RRR-RR--RRRR-RRRR-LRL-R----R-RLRLLL-R-RRR---L-RR-R-RRRRRR-RR--RL-RR-LR-RR---RRL--R-L-RLRR-----RRR-RL--R---RRRRR-LLLR--L-R-R--R--RR--L-RRRR---RLR---LRR--RR--R--L----RRLLR-R--R-RR--R---R-RRL-L---R--R-L--RRRL-RLRRRR-RRR---LRRRRRRLR-L--R-RR---RRR-RRR---RRRR-L-RLRLR-LL-L-RRRR-RR-R---RRRRR---RRRLR-RR-R-RRRLR--RRLR-LLRL--RL-RLRL--R--RR-L-L-RRR-RRRR--R-R-RL-R---RL-L--RRRR-RRR-R-R---LRRL-RLL--RRR-LRR--RRRR-LRLR-R--LR--R-LR-R-RR-R-RRRRLR-RRRR-R-RR-R--------R--RRR-R-R--RL-L-L-----R--RRL-L-RRR-R--RRLR-RRLR--R---RR---R-RR-R---RR-R-----RL---R-R-L-R-R-RR-RRL-R-LRR---L--R---R-L---R-----RR-R-RLRR--R-RR-RR-L-R-R-R-RLRR-RR---R-R--RL-RRRRRL-R----R----R-RR--LR-R--LRR-RR-R-R--RRR-R-RRL---RRR--RRR-----R-R--R-R----------RR----RRLL-RRR----R-RR---RR-----LRR--R-R--R--RR-LR----RR-RR--------RRRR---RRRRRR-RR-RR--RLRR--RLR--RR-L-L------L-L---RRLRRRR-RR-----RRR--R-RL-----RL--RRRLLRR-RRRR--L-RRLRRR-R--R--RL----RRRRRLRL-RLR-RRRRR-R---L---RR---RR-R--RRR--R----R-RR--R-R-RR-R-RR-R--R-LR--LR--RRR-R--RR--RRR--R-R-RR--L------R--L---R---RR-R---RL--L--R---RRR-L--R----R-RRR--RR-LR----LR-R-RRLLRR-R-R--R-RR-R--R-R----RR-L---RLRR--L---RR---RRLR-R-LRRR--R-RR-R-RRRR--LRLRL--R--R-RR---RR-R--R-RR-L----LR--L-LR--RRL----RRL---RR-R-LRRL---R---R-L--RR---R--R-RL--RRRRLL--L---L------R--RR--R-L-R--R--RRL--RRR-R--RR-R-RRL--RRRL------R--R-R-R---L-R-RLLRRR-RRRRR-----RRR--RR-L-LRR-L---L-R-LR-----R-R-RL--L-R--RR-RR-R-RRR-RRR---R-----RR----RRRRRR----R-RLR-R--R-L-R--R---RLLL-R-R--R-L-R-R-----R---R-RR-RRR-RRR--R-R--R-R-R-R----L--L----R--RLRLR--R---RLR-R----R---RRRL---LL-R-R--RRRLRL------R----RLL--L---RLRR--RLR-R----RR-R-R-L--RR--R-RR---L---RR---RR-------R-RL--------RR--RRRR--L-RR---RLLR-LR-RL--R-R-R-R-----R-R-RR-R-R-RRLRR---R-RR-R-RR-L--R-LRRRR-RRRRR-R-RR-R----R-RRRR-RR--R-R----RRRRR-RRR--RL-RL---R-L--RR-RR---------RLL---L---RRR--RR---R---RLRRR-----L-R-RRRRL-R-L------R--LL--L-R-L---LRR---R--L---R-RR----RR---RL-----RR----L-RRRLRR-LR---LR---RRR-L-R--RR--RR--RLLRR-RRL-RRRLL-R---LRLR-R---RRR-RRRRR-R-R-RRRRLRL--RL, improve=0.30426700, (0 missing)
## Spa < 1346.5 to the right, improve=0.13215820, (0 missing)
## VRDeck < 1058 to the right, improve=0.12338610, (0 missing)
## CryoSleep splits as LRL, improve=0.11239120, (0 missing)
## RoomService < 366.5 to the right, improve=0.08946097, (0 missing)
## Surrogate splits:
## ailesira splits as RLLLLLLL, agree=0.740, adj=0.239, (0 split)
## Age < 10.5 to the left, agree=0.683, adj=0.070, (0 split)
## Spa < 1024 to the right, agree=0.681, adj=0.065, (0 split)
## VRDeck < 991.5 to the right, agree=0.680, adj=0.062, (0 split)
## RoomService < 705.5 to the right, agree=0.674, adj=0.044, (0 split)
##
## Node number 4: 3173 observations
## mean=0.0009454775, MSE=0.0009445835
##
## Node number 5: 607 observations
## mean=0.324547, MSE=0.2192162
##
## Node number 6: 1676 observations, complexity param=0.04828533
## mean=0.576969, MSE=0.2440758
## left son=12 (1025 obs) right son=13 (651 obs)
## Primary splits:
## CryoSleep splits as LRL, improve=0.2565094, (0 missing)
## Spa < 0.5 to the right, improve=0.2056128, (0 missing)
## VRDeck < 1.5 to the right, improve=0.1922289, (0 missing)
## RoomService < 0.5 to the right, improve=0.1675086, (0 missing)
## FoodCourt < 0.5 to the right, improve=0.1407524, (0 missing)
## Surrogate splits:
## Spa < 0.5 to the right, agree=0.725, adj=0.292, (0 split)
## FoodCourt < 0.5 to the right, agree=0.718, adj=0.275, (0 split)
## VRDeck < 0.5 to the right, agree=0.711, adj=0.257, (0 split)
## ailenum splits as ---------------L---------------RL----------L-----------L-----LR-LL--R---L------L-------L--L-L----------L-L--------L--------L-------------L-------R---------------L--------L----------------L--------------------------L-----------------------L--------R----------------------------R-R------L--L---------L------------L---L---R---------L--------------------L--R-----L----------L--L--R---L--R-----L----R---------R-L-R--------RL--------L----------R----L------L-------------L-----L--L--------L-R------------------R-R----L-L---L-----------------------L-LL------L-----L---------------------------------RL---L--------L------L--RL--L--L-L----------------------------L----L---L-------L--L--------------L-----------------------------------------L-L----------LL----------L----------L-----LLL--L----------------------------L----------------L-----------------------------------R-----R----L--L----L----R--L----------L---L-----L-------L-----L-----------------L-------------R-L--------------LL-L-------------------L---L------L----------------------------------L------------L---------L-------L--L--L------------------R-L-------------------------------LL---------L------L-------L---------------L-L----------------------R--------L------------------R-------L----L----------------------L------------LR----------------------L-L----------------R-----L------------R-----L--R-----------------L--------L-L-L-----L---L-------------------------R--------L---------------------------L------------------L-------------L-----------------------------------------------------------LL-R----------------LL-------LR--L-----------------------------L-L---------------------------R------------------------------------L-----L-L-------------LL-----L--L------------L-L----------R-----LLL--------------L--L------------------------L----------------------------------L---------L----RL--L--------------------------------L---------L--LL----------------R----L---------------------L------------------L-------L--------------R----------------------------------------------L-L---------------L--L-----------L--------R------L------------L-----L------------L----------------------------------------L-----------------------------------------------------L------L---L---R-----L------------------------R----------L---R---------L------L--------------L------------------LLL-----------------------R-RLL---LL---L----L-L------R--L------L--L----------L---L-----------------------L-----------L----L---L-----------L----------------------LL-------L--L--LL--R----L----L--L-L--R---L----------L-------------------------------------L------------------------------------L----L--R-------------L---L----R--LL-------------------------L--L-----------------------RL--L-----------------------------L---------R---L---------------------------------------L-------------------------------L---LR-------------L--R--------------------------------------------L---------------------LR-L--------L-------------------L------------------L-------R-------L------------------------------L---L-------------L----L---L-L-------------L-------L-------------------------------------L---------------------L-----------------------------------------------L-----------L---L-L-L---R--L--------L--L--------------------------R---L----------L--------------------------------L-----------------------------------------------L-------------L-----------------------LL-L---L---------------R------L---------------L--L-----R-L----L--------------L--------------------LL--------L---L--------L-------L----------------R------L-LL--------------------------------------------------------------------LL--------L-------------L------------------------------L-----------------L----L----------------------------------------------LR---------------LL--------L---LL----------------------L-----L-----L----------------LL----------------------L-------LL---------R---------L----------------LL----------L--------------------------------------------------L-L--R-----------------------------------L-L------------L-L----------------------L------R----------L-------RL---L-L-------L-LLL-----------------L---------------L---RLLL--L---------------------------------L---L---------L--------L---------------------L--------------------------------------L------L-----------------------LL-------------------------L-L---------R-LLL---------L------------------L----L---------R----L--L------------L------------LLR---L-------------R---------R----L-----------L------RL-------------------L-L--------R-----L--L-----------L------L--L------------------------L--L-L--LL-L------------------------R----------L-----L--RL-L---L--L-R--------L-L----------------R------L-L-----------------L--L--LL------L---------L-L-----L-----L-------------L----------------------------------L-L-L----------L-L----------L----L-------------------------------L-------R----------R---L-----L--------L---------------L------------L--------L--------------L------L-----------------L-----L-------------------R------------------------------------------LR----------------------L---------------L----------------------------------------L-----L------R-L------L-L-----L--------------------L------L-----LL---------L---L-----------L---------L-L--R------------L-----------------------------------------------L---L---------------------------L---------L---------------L--L----------L-----------------L-----L------RL--------------------------L----L----L----------L----R------------------L-R-L-----------------------R----L---L-R-----L------L--------L--L---------R-------------L------LL--R---L---------------L---------L----------------L-----L-----------------L----LL----------------------L-L---L---L---L-----------R--L-----------------------------------------------L-------L---------LLL--------L-------------------------------------------R--L--------L-L--------R--------------L---LR---------L-L------------LL--L----L-----L--------------L-------------L--------------------L------------------L-------LL--L---L---------------------------L---------------L----R---------------------------------------------------L--L-----L-----------------RL---L------------------L--------L-------L---L---------LR--L---L---L--------L-----------------L-----------L----L---R----L--------R-------------LL-----L----LL-----R-L------------------------L-L---L, agree=0.700, adj=0.227, (0 split)
## RoomService < 0.5 to the right, agree=0.665, adj=0.137, (0 split)
##
## Node number 7: 3237 observations
## mean=0.9919679, MSE=0.007967613
##
## Node number 12: 1025 observations, complexity param=0.02910718
## mean=0.377561, MSE=0.2350087
## left son=24 (317 obs) right son=25 (708 obs)
## Primary splits:
## ailenum splits as ---------------R----------------L----------R-----------R-----LL-RR--R---R------L-------L--L-L----------R-L--------R--------R-------------R-------L---------------R--------L----------------L--------------------------R-----------------------L--------L----------------------------L-L------R--R---------L------------R---R---L---------L--------------------L--------L----------L--R--L---L--L-----R----L-----------R-L--------RR--------R----------L----R------R-------------L-----L--L--------R-R------------------L-L----R-L---R-----------------------R-RR------R-----R----------------------------------R---R--------R------L--LR--L--R-R----------------------------L----R---R-------R--R--------------R-----------------------------------------R-L----------LR----------R----------L-----LLR--L----------------------------L----------------L-----------------------------------L-----L----R--L----L----L--R----------L---R-----L-------R-----R-----------------R-------------L-L--------------LL-R-------------------R---R------R----------------------------------L------------R---------R-------R--R--L------------------R-R-------------------------------LR---------R------L-------L---------------R-L----------------------R--------L------------------L-------L----L----------------------R------------LR----------------------R-R----------------L-----R------------L-----R--L-----------------R--------L-R-R-----L---R----------------------------------L---------------------------L------------------R-------------R-----------------------------------------------------------RR-L----------------LL-------RL--R-----------------------------R-L----------------------------------------------------------------R-----L-L-------------LL-----R--R------------L-L----------L-----RLL--------------L--L------------------------L----------------------------------R---------L----LL--R--------------------------------L---------R--LL----------------L----R---------------------R------------------L-------R--------------L----------------------------------------------R-L---------------R--R-----------L--------L------L------------L-----R------------L----------------------------------------L-----------------------------------------------------R------L---R---------L------------------------L----------R---L---------L------L--------------R------------------LLR-------------------------LRR---RR---L----L-R---------L------L--L----------L---L-----------------------R-----------R----R---L-----------R----------------------LL-------R--L--RR--L----L----L--R-L------L----------L-------------------------------------L------------------------------------R----R--L-------------R---L----L--RL-------------------------R--R-----------------------LR--R-----------------------------R---------L---L---------------------------------------R-------------------------------L---RL-------------R--L--------------------------------------------L---------------------RL-R--------R-------------------L------------------L-------L-------R------------------------------R---R-------------L----L---R-L-------------R-------R-------------------------------------L---------------------R-----------------------------------------------R-----------R---L-L-R------R--------R--L--------------------------L---L----------R--------------------------------L-----------------------------------------------L-------------R-----------------------LL-R---R---------------L------L---------------R--L-----L-L----L--------------R--------------------RR--------R---L--------L-------L-----------------------R-RL--------------------------------------------------------------------RL--------L-------------L------------------------------R-----------------R----R----------------------------------------------R----------------LR--------L---LL----------------------L-----L-----R----------------LL----------------------R-------RL---------L---------R----------------RL----------R--------------------------------------------------R-R--R-----------------------------------R-R------------R-R----------------------L------L----------L-------LL---R-R-------R-RLR-----------------L---------------R---LLRR--R---------------------------------L---R---------L--------R---------------------L--------------------------------------R------R-----------------------LL-------------------------R-L---------L-RRL---------R------------------R----R---------L----R--R------------L------------RLL---L-------------L---------L----R-----------R------LL-------------------R-R--------------R--R-----------R------L--L------------------------R--R-R--RR-R------------------------L----------R-----R---L-R---L--R-L--------R-R----------------R------L-R-----------------R--L--LR------L---------R-R-----R-----L-------------R----------------------------------R-L-L----------R-R----------R----R-------------------------------L-------L----------L---R-----R--------L---------------L------------R--------R--------------R------R-----------------R-----L-------------------L------------------------------------------R-----------------------L---------------L----------------------------------------L-----R------L-R------R-L-----L--------------------R------R-----RR---------L---L-----------R---------L-R--R------------L-----------------------------------------------L---L---------------------------L---------L---------------L--R----------L-----------------R-----R------LL--------------------------L----L----R----------L-----------------------R-L-R-----------------------L----R---L-R-----L------R--------R--R---------L-------------L------RR--L---R---------------R---------L----------------R-----R-----------------R----LL----------------------R-R---R---L---L-----------L--R-----------------------------------------------L-------L---------RLR--------L-------------------------------------------L--R--------R-R--------L--------------R---LL---------L-L------------RR--R----L-----R--------------R-------------L--------------------R------------------L-------LR--R---R---------------------------L---------------L----L---------------------------------------------------R--R-----R-----------------LR---L------------------R--------R-------L---L---------LR--R---R---R--------L-----------------L-----------L----R--------L--------L-------------RR-----L----RR-----L-R------------------------R-R---R, improve=0.26259070, (0 missing)
## Age < 12.5 to the right, improve=0.14269010, (0 missing)
## Spa < 420 to the right, improve=0.09687267, (0 missing)
## RoomService < 249.9754 to the right, improve=0.08111019, (0 missing)
## VRDeck < 87.5 to the right, improve=0.07744737, (0 missing)
## Surrogate splits:
## Spa < 2110.5 to the right, agree=0.704, adj=0.044, (0 split)
## VRDeck < 2266 to the right, agree=0.704, adj=0.044, (0 split)
## VIP splits as RLR, agree=0.695, adj=0.013, (0 split)
## RoomService < 3243 to the right, agree=0.694, adj=0.009, (0 split)
##
## Node number 13: 651 observations, complexity param=0.0131094
## mean=0.890937, MSE=0.09716825
## left son=26 (124 obs) right son=27 (527 obs)
## Primary splits:
## ailenum splits as -------------------------------LR----------R-----------R-----RR-----L---R------R-------R--R-R----------L-R-----------------R-------------R-------R------------------------R----------------R--------------------------------------------------R--------R----------------------------R-R-------------------R--------------------R---------R--------------------R--L-----R----------R-----R---R--R----------R---------L-R-R--------L--------------------R-------------------------R-----R--R----------L------------------R-R------R---R--------------------------R----------------------------------------------L----L---------------R--R---R---------------------------------R--------R-------R--R--------------------------------------------------------L-R----------R-----------L----------R-----RR---R----------------------------R----------------R-----------------------------------R-----R----L--R----R----R-------------R---R-----R-------------L-------------------------------R-R--------------RR-------------------------R------R----------------------------------R----------------------R-------R-----R------------------L---------------------------------R-----------------R-------R-----------------R----------------------L--------R------------------R-------R----R-----------------------------------RL------------------------R----------------R------------------R--------R-----------------L--------R---------R-----------------------------L--------R---------------------------R------------------R-------------R-----------------------------------------------------------RR-R----------------RR--------R--L-----------------------------R-R---------------------------L------------------------------------------R-R-------------RR---------------------R-R----------R------RR--------------R--R------------------------R----------------------------------R---------R----RR-----------------------------------R------------RR----------------R---------------------------------------------R----------------------R------------------------------------------------R------------------------------R--------R------R------------R-----R------------R----------------------------------------R-----------------------------------------------------L------R-------L-----R------------------------R----------R---R---------R------R--------------R------------------RRR-----------------------L-RR----LR---R----R--------L--R------R--R----------R---R-----------------------------------R--------R-----------R----------------------RR-------R--R------R----R----R----R--L---R----------R-------------------------------------R------------------------------------L-------R-------------R---R----R---R----------------------------------------------------RL--L-----------------------------R---------R---R---------------------------------------R-------------------------------R---RR----------------R--------------------------------------------R----------------------R----------R-------------------R------------------R-------R-------R------------------------------R---R-------------R----R---L-R-----------------------------------------------------------R---------------------------------------------------------------------R-----------R---R-R-R---L--------------R--------------------------R---R-------------------------------------------R-----------------------------------------------R-------------R-----------------------RR---------------------R------R---------------R--R-----R-R----R---------------------------------------------R---R--------R-------R----------------L---------R--------------------------------------------------------------------RR--------R-------------R------------------------------------------------R---------------------------------------------------RL---------------R---------R---RR----------------------R-----R----------------------RR----------------------R-------LR---------R--------------------------LR------------------------------------------------------------------R-----------------------------------R--------------R------------------------R------R----------R-------RR---R-R----------R------------------R-------------------RRL-------------------------------------R-------------R------------------------------R---------------------------------------------------------------------RR-------------------------R-R---------R---R---------------------------------R---------R-------R------------R------------RRR---R-------------L---------R----R-----------R------RR------------------------------L-----L--------------L------R--R------------------------R--R----RR-R------------------------R----------------R--LR-----R----R----------R----------------L------R-R-----------------R--R--R-------R-----------------R-----R--------------------------------------------------R-R----------------------------L-------------------------------R-------R----------R------------------R---------------R------------R--------R---------------------L-----------------------R-------------------R------------------------------------------RL----------------------R---------------R----------------------------------------R-----R------R--------L-R-----R--------------------R------------RR---------R---R---------------------R----L------------R-----------------------------------------------R---R---------------------------R---------R---------------R-------------R-----------------------L------RR--------------------------R----R----R----------R----L--------------------R-R-----------------------R--------R-L-----R------L--------R--R---------R-------------R------RR--R---R---------------L---------R----------------------------------------R----RR------------------------R-------R---R-----------R--R-----------------------------------------------R-------R----------RR--------R-------------------------------------------R-------------R--------R--------------R---RR---------R-R------------RL--R----R----------------------------------R--------------------R------------------R-------R-------L---------------------------R---------------R----R---------------------------------------------------R--------R-----------------LR---R---------------------------R-------R---R---------RR--L---L---L--------R-----------------R-----------R----R---L----R--------L--------------------R-----R-----R-R-------------------------------, improve=0.45036460, (0 missing)
## deck splits as RRRRLRL-, improve=0.11754260, (16 missing)
## HomePlanet splits as LRRR, improve=0.09976461, (0 missing)
## Age < 12.5 to the left, improve=0.02356719, (0 missing)
## Destination splits as RLLR, improve=0.01419214, (0 missing)
## Surrogate splits:
## ailesira splits as RRRRRLRL, agree=0.817, adj=0.04, (0 split)
##
## Node number 24: 317 observations
## mean=0.006309148, MSE=0.006269343
##
## Node number 25: 708 observations
## mean=0.5437853, MSE=0.2480828
##
## Node number 26: 124 observations
## mean=0.4596774, MSE=0.2483741
##
## Node number 27: 527 observations
## mean=0.9924099, MSE=0.007532523rpart.plot(fit_tree)## Warning: labs do not fit even at cex 0.15, there may be some overplotting
rpart paketini kullanarak bir karar ağacı (decision tree) modelini eğitir ve bu modelin özet istatistiklerini görüntüler.
y_pred = ifelse(preds == TRUE, 1, 0)“preds” vektöründeki değerleri kontrol eder ve eğer bir değer “TRUE” ise, “y_pred” vektörüne 1 atar; değilse, 0 atar
cm## function (x)
## 2.54 * x
## <bytecode: 0x0000026b8ea8c708>
## <environment: namespace:grDevices>(505+667)/(505+667+272+121)## [1] 0.7488818fit_tree <- rpart(Transported ~ ., data = train_set)
summary(fit_tree)## Call:
## rpart(formula = Transported ~ ., data = train_set)
## n= 8693
##
## CP nsplit rel error xerror xstd
## 1 0.62521454 0 1.0000000 1.000261 0.0001746568
## 2 0.08751388 1 0.3747855 1.188792 0.0167400686
## 3 0.04828533 2 0.2872716 1.275538 0.0182573973
## 4 0.02910718 3 0.2389862 1.260885 0.0185339961
## 5 0.02455280 4 0.2098791 1.273756 0.0187420488
## 6 0.01310940 5 0.1853263 1.308503 0.0193651091
## 7 0.01000000 6 0.1722169 1.310339 0.0194027138
##
## Variable importance
## ailenum CryoSleep RoomService Spa VRDeck ShoppingMall
## 54 12 9 8 8 6
## ailesira FoodCourt Age
## 2 1 1
##
## Node number 1: 8693 observations, complexity param=0.6252145
## mean=0.5036236, MSE=0.2499869
## left son=2 (3780 obs) right son=3 (4913 obs)
## Primary splits:
## ailenum splits as 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, improve=0.6252145, (0 missing)
## CryoSleep splits as LRL, improve=0.2117218, (0 missing)
## RoomService < 0.5 to the right, improve=0.1204234, (0 missing)
## Spa < 0.5 to the right, improve=0.1183706, (0 missing)
## VRDeck < 0.5 to the right, improve=0.1103404, (0 missing)
## Surrogate splits:
## CryoSleep splits as LRR, agree=0.650, adj=0.196, (0 split)
## RoomService < 0.5 to the right, agree=0.649, adj=0.192, (0 split)
## Spa < 0.5 to the right, agree=0.634, adj=0.158, (0 split)
## VRDeck < 0.5 to the right, agree=0.628, adj=0.146, (0 split)
## ShoppingMall < 0.5 to the right, agree=0.624, adj=0.134, (0 split)
##
## Node number 2: 3780 observations, complexity param=0.0245528
## mean=0.05291005, MSE=0.05011058
## left son=4 (3173 obs) right son=5 (607 obs)
## Primary splits:
## ailenum splits as L-L------L-LLL--RL-LLLL--LLLL-L--LLL--LLL-L-L-LLLL---LL---L----L---L-LLR----L----LL-LL---L---L-L-L-LLL----LL---LLL-L--L-LLR--LL-L----L-L--LLLL-RL-LL-LLLLL-L-LLL--LLL-LL---LLL--L-L--L----L-L------LL-LLL-LL-L--LLLLL--L-LL---LLL-L--L--L-LL-L--L-LLL-L----LL-LLLL-LL--LL--LLLLLL-LL--------L----L-LLLLL-------LR-L--LL-LL--L-L-LL-LLLL----LLR-------LL--L-L---LL-LLL---LLLL---L----L-LL-L------LL-LL--L----LLLLLLL--L---LL-LL--------L--LL-LL--L---L-----L-L-LL-L-LL-LLLLLL-L-L-LL--R-----L-----L-L--LLLL---LLLLL-R-LL---L-LL-L--RL-L-LLL--L-LLR-LL-L-L-L---L---LLL-----L------LLLLLLL---L--LL-L-LL-LLLL-LLLL--L-L---LLL-L---LLLLL--L--L-----L---LLL-L--LLL--L--LLL--LL-LL---R-L-LLL-LL-L-LL--L--L-L-LL---LRLL--LLL-L--L--LLL---LLL---LLL---LL-LLLLLL-LL-L---L-L-LRLL----L--L-L-L-L------RL--RLL--------L-LLL----L-L-LRLL-LL--L-LLLL-RL----LLLL----LL----L----LL-LL-LLL-LLL-LLLLL--LLL-L--LLL-L-L-L---L--LR--L--R-LL--LLL-LLLL--RLL--L--L--LLLLL--LL-L--L-L--RL-L-L-L-LLL-LLLLL-LLLLL-----LL-LRLLLL----L--R-L-LL-L-LLL-L-LL--LL-LLL-LL-RLL-L-----L---LLL--R-L-L-L-L-LLLLLLLL---LLL-L-L-L----L-----LL-L-L--LL-L-----L-RL--LLL-L-LL-LL--L-LL-LLRL--LRLLLLLLLL-RLRLL-LLL----LLR-LLLLL--LLL-L-L---L-L--LLL--RL--LLLLL---L-L---L--LL--LLLLL--LL-LL--LLLL--L-L-L--LL--LL--LL-LLLLL----L-L--LRL--L-LLLLL--LLL-LLL--LL-LLLLLLLL--LL-LLL---LL-LLLLLLLLLL-L---RLL---L-LL---L-LL--L--L-LL-L-LLL---LLL--L---LL-LL--L---L--LL-R-L-LLL----L-LL-LL-L---L-LR-LL---LLLLLLLL--LLL-L-LLL-LLLL-LLL------LLL-L-LLLLL----L-L-L----LLLLLLLLL-LL---L-LL-LLLLL-L----L-LLLLLLRL---L-LLLL-----LL-L-L---L-LL-LL-LLLL-LL-LLLRL-L--L-LLLL--LLLL-L--LL--LL-LRLL--L-----LLLLL--L-LLRL-LLL-LLLLL-LL-------LLL--L---LLL-LL--L-LLL--LL-LLL---LLLLLLLL-LL-LLLLLL-L-LLL-LL-LR-L--L-L--LL------L---L-LLL-LL-LR-L-----LRL------LLLLLL--RLLL----LL-R-L--LL-LLL-LL-L-L--LLLLLL-LLLLL--LLL-L-L--LLLLL--L-LL-LL-L-LLLLLLL--LL------LL-RL---LLL----L-L--LLLL----RLL-L-LRL--LRLLLLL-LL----LLLR---LL--L-LLR-LL---L-L------L-L-LLL-LL--L-LLL-----L--L----L-LL-LLL-LLL-LRLL-L--LLL-LLLLL---L---LLLL--LLLR--LL--LLLLLLLL--LL--LL--LLL-LLL-L-L----L-LLLL-LLLLL---LL--LLLLLLLLLL-L-LLLLLL---L-L----LL--L-L-L--L-L----L--LL-LLLLL-----LL--L--LLL----LL-LRLL----L---LLL--LL--L-RLLLLLL-L-LLLL--LRLLLL--L---LRRL-LLLLLLL---LLLLL----LR-L---L------L-L-L---LLL-LL-LLL-L----LLL-L---R-L-LL--L-LL--RL-LL--LL-LR-L-----L-L-L--RL-L--LL--LLL--LLL---LL----LLL--L-LL-LL----L-LLL---L---LLL---LL--LL--L-L--LLL-------L---LL--LL---LL-L-L---L--L-L--LL--LL-L-L--LLL----LL---L------L--L--L--L-LL-L-L-L---L-LLLL-L-----L-L-L---L--LL--LL-LL---L--LR-------L----L--L-L---L-LL-L-------L-L-L--L-----LLLLL-L-----L-LLLL-L-L-LLLL-LL--LLLLL--LLL-LLL-L-----R-L-----L-LL-----R-L--RL----L--LL----L--LL--LL--LLLLLLLLL-LL-LRL-----LL-L-----L-LL-LLL-R-L-L--L-----LLLLRL-LLLL--R---L-L-LLL-L-L--LL-LLL---L-L---LLLLLLL-----LL-LL-L-LL-L--L---L-L---L--L-LLLL-LLL----L-LLL----LL--L-LLL--L--LLL---LL----L-LL-L---LLL--RL-L-LLL--L--L--L--L-L-LR-RLL----L--L--L--L-L----LL-------LL-L--LL--L--L-L---L---L-L-L-L-L-L-R----L---LL--L----L---LLLL-L--LLL----L-LL--L-L-L-LLL--L--LL-------L----L-LLL--LLL--LLL-L--LLL----LRL-L-LL---L--LLLL--LL----LLL--L---L--LLLL--L--LLLL---L-LLL---LLL---R---L-LLLL-LL----LL---L----RLL-L--LL-----LL--L--LLLLR--LL--LL-LL------L----LL-LL-L-LLL----L--L--L----LLLLLL--LLL-----L----L-R----LLL---L----LL-LLL---L---L--LL-L--L-RL------L-LLLL--LL---LLLL-L-L-L--L--L--LL--LLLLLLLL--L-----L------LL---L--R-LL----L--LLL-----LLL---LLLL----L-L-L-L--L---LL--L--LLLL-L-L-L---LL---L--L-L---L-L-LLLLL-L--RLL--------L---L-LL-----LRL---------LL--LL-----LLL--LL------LLLRL-----LLL-L-R----L-LL-LLL-L---L--L-L----L---L------LL----LL--L-LLL---L-L---LL----------L---L------L-L---L-L-L-L-LL-L-LL----L--LL--LLLL-LL---L--L-LLLLLLL--RL-L-L--LLRL--LL-LR-LL-----LLLLLLL-RRL--LL--L--L-LL-L---LLL----L-L-LL-----LL-L-L--RLL----L-L---L-L--R-L--LR-L-L--LL----L--LL-L-LLL-L--L--L----------R-LL-LL---RL---LL-LLLL---LL-LL----LLL-LL-------------L--------LL---LL-LL--L---LL-LLLLL-LL--L-L----L-LLL--L-LL-L-L-L-L-L-LLL--------L--L------LLLL--L-L---LL-LL---L-----LL---L-L---L-L-LL------LLL-L----LL--L-L-LRR--LL--L---L--LL-LLLL---L--L--L-L--L--------LL----L---LLL-----LL-L--LL-L---L-------L--LLLL-L-L-RL--LL-RL-LL------L--L----L-L-LL-L--L----LLLL------LL---LL---L-LL-----LLL-LLL---LL--RR-L--LL-----L-RLLLR-----LLLLL-LL--L--L--LL--L----LL----LL--L-L---L--LL----L----L---L-LLLL-L------L-R---LLL-L--L-L------L--RL--L--L--L--RLL---LL-L-R----LLLLL---L--RL-LRL-----L----LL-L-L-LL-LL--LL-L----LLL---LLL---LL--LL-LR-LLLL-----L-LL-L--LL-LLL-L---L-LLL-LL-L-LL----L------L---LLL---------L-LL-L--LLL---L---LLL----L-L-----L--L-L----R--L-LLL-----LLR-----L--R-L-----LL----L----LL--L----LR-LL--L-L-L---L----LL-L-L--L-LLL--L-RL----L---L-L-LLL----L---LL---L---LL----L----L-LR--LL-L--L-L--L-L------L----L-L--L-LLLLLLLL-LL---L-L-RL--L-R-LLLLL-LL---L-L---L-LL----L----LL-LLL--LLL-L--L-LLL--L-LLRLL--LLL-L-L-L-L-L--L---R-L---LLL-LL-LLL-L-RLL-LLRLL--L-L----LL-L--L--L-L-L-L-R----L--LLL-L-LL--L------L-LLLL-LLLL-L--LL--L-LL---L--L-L-LL---L-L---LLL---LL---LLLLL-L-LL-L-LLLLLLLLLL--LLLL----L---LLLL-L--LLL--LLLLL---LL-L-LL-LL--L--LLLL--L--LLLLLLLL----LLL------L--L--LL----LL---LL--L-L-LLLLLL-L-LLL-------L--LLLLL---LL-L--LLLLL--LL-------L----LL-L------L-LL-LL--LLLL--------L---L-----L-LLL-LLL--LLL--L-LL---LL-LLLL-L--LL-L-L--L-L--L-LL-L--LL--LL---L-LL--LL---LL-L-L--LL-LLRLLL-LL-LLL-LLL--L-LLL--LL-LL-LLL---L-LL-LRLL-L---LL--R--LLLL--L-L------L-R-RL-R--L-RL-L-LLLL--R-LLL----LL-LLL--LLL----L-L----LL-L--L-L----RL-----LL-LL-L--LLL--L-LL-L--L-LLLL--LL-L--LL---LLLL---LLL--L-L----LLL-LLL-L-LR--RLL-RL-L--LL------LL-LLL-LLLLLL-RL--LL-L-L-LL-LL---LL---L-LL--L-L---LL----LLLLLL-LL-L-L-LLL-L-L------L-----LLLLL---LL--L-L---L-LLL-L-L--LLLLL-L-L--LL-L-LL--L--L-R---L---LLL-LLRLL--LLLL------LLLL-L---L-LL-L-L-LL-LLL----L-L-LL-L-L-L-LLLLL-LLL-L--L---L---LR-L-LL-L-L-L-LLLL-LL-LLLL-LL-----LL-LLL---L-LLLL-LLL----LLL--L-L-LL------LLLLLL-LLLL---LL-LLL----LR---L-LLRL--L-L-L-LL--LL-R--LLL-LLL--LLR--LLLRLLL-L--LLLLLLLL--LL----RL-L--LLL----L--L--LL-L-L-L-LLLLL-L-L--L-L-L-----LLL-L--L-L--L-LL-L-----L-----L-L--L-LRLL-L----L--LL-L-LLLL-----L---LR--L--LLL-R-LL--L--LLLLLLLLL---LLL-LLL---LL--LLR-LLL-----LLLLL-L-L-----L-L-LLLLLL-LR--LL-L-L-LRL---LLL-LL-LLL-L--LLLL--LLL--LLLLL--LLLL-L------L--LLL--LLL---L-L-LL--LL--LL-----L---L-----L-LLL----L-LLL---L-----L-L-L-------LL--, improve=0.28168690, (0 missing)
## ailesira splits as LLRRRRRR, improve=0.11212740, (0 missing)
## Age < 12.5 to the right, improve=0.11123430, (0 missing)
## CryoSleep splits as LRL, improve=0.07443996, (0 missing)
## RoomService < 0.5 to the right, improve=0.03282292, (0 missing)
## Surrogate splits:
## ailesira splits as LLRRRRRR, agree=0.903, adj=0.397, (0 split)
## Age < 12.5 to the right, agree=0.869, adj=0.183, (0 split)
##
## Node number 3: 4913 observations, complexity param=0.08751388
## mean=0.8503969, MSE=0.127222
## left son=6 (1676 obs) right son=7 (3237 obs)
## Primary splits:
## ailenum splits as -R-RRRRRR-R---RL--R----RR----R-LL---RR---R-L-R----RRR--LRR-RRLL-LLR-L---LRRR-RRLR--R--RLR-LRL-R-R-R---RLRL--RRR---L-RR-R---LR--R-RRRR-R-RL----R--L--R-----R-R---RL---R--RRL---RR-R-RR-RRRR-L-RRRRRR--R---R--R-RR-----RL-R--RRR---R-RR-RR-R--R-LR-R---R-LRRR--R----R--RR--RR------R--LRLRRRRR-LRRL-R-----RRLRRRR--R-RR--L--RL-R-L--R----RRLR---RRRRRRR--RR-R-RRL--L---RRL----RRR-RRLR-L--L-RRLRRL--R--LR-RRLR-------RL-LRL--R--RRRLLRRR-RR--L--RR-RRR-RLRRR-L-R--R-L--R------R-R-L--RR-LRRLR-RRRRR-L-LR----RRR-----R-R--LRL-R--L-LR--L-R---RR-R---R--R-R-R-RRL-LLR---RRLRR-RRLRRR-------RRR-RR--R-R--R----R----LL-R-LRR---R-RLR-----LR-LL-RLRRL-LRR---R-RR---RR-RR---RR--R--RLR-R-L---L--R-R--LR-LR-R-R--RRR----LR---R-RR-RR---RRR---RRR---RRR--R------R--L-LRR-R-R----LLRR-RR-R-R-L-RRRRRR--RL---RRLLLRRL-R---RRRR-R-R----R--RR-R----L--RRRR----RRRR--LRRR-RRRR--R--R---R---R-----RR---R-RL---R-L-R-RLR-LR--RL-RR-L--LR---R----RL---LR-RR-LR-----RL--R-RL-R-RR--R-R-R-R---L-----R-----RRLRL--R------RRRR-LL-L-R--R-R---R-R--RR--L---L--R---L-RRRRR-RRR---RR-R-R-R-R-R--------RLR---R-R-R-RRLR-RRRRR--L-R-RR--L-RLRRL-R--RR---R-R--R--RL-L--R----RR----------R-----R---RRLL---R-----LR---R-L-RRR-R-LR---RR--RR-----LRL-R-RRR-RR--RR-----RR--L--RR----LR-R-R-RR--RR--RR--L-----RRLR-R-LR---RR-R-----RR---R---LR--R--------LL--R---RRR--R----------L-LRR---RRR-R--RRR-L--RR-LR-R--R-R---RLR---RL-RLR--R--RR-RRR-RR--L-R-R---RLRL-L--R--L-RRL-R--R--RRR--------RR---R-L---R----L---RRRRRR---R-R-----RRRR-R-L-RRRR---------R--RLR-R--R-----R-LRRR-R--------RRR-R----RRRRR--R-R-RRR-R--R--R----R--R-----R-LL-L----RR----R-RR--LL--R----LL-RLRRR-----RR-R----R---R-----R--LRLRRRR---RR-RRR---R--RR-R---RL--R---RRR--------R--R------R-R---R--L--R-RL-L-RR--RRRRRR-RLL-R---L--L--R-RRRRR---LRLRRR------RL----RLLL--R-R-RR--R---L--L-R-RR------R-----RR---R-L-RR-----RR-R--R--R-R-------RR--RRRLRR--R--RRL---RLLR-L-RR----RRRR---R-R---RR-------R--LRRR----RRL--LL-R---R--RRR-R-RRLRRR-L-R---R--RR-R---RRRRR-LR-RRRR-R--R---R---L----R-RL---R-----RRR-RLR----RR----RR--RR--------RR--RR--RR---R---R-R-LRLR-R----R-----RRL--LR----------L-R------LRR-R-RLRR--RR-R-R-RL-R-RRLR-RR--R-----LRRRR--RR-RR---RRRR--R----RRRR-RRR---RR--LR-R-------R-R----RR------RR-RRR----R-------RRR-----RRLR--R-RLR-RLRRRL-R-R-LRR---R--R---R-RRRR---R-RLR-R-R--RR-L--RL--R--RR--L--R-RRLRR-R-R-RR--R-RL--RR---RR---RRR--RLLL---RR-R--R--RRRR-R---RRL-LLL---LLR--LR--RL-L-RR---LRRLRRR-RRL--LR--RRR--R-L-RRL-RR-R-RR--RR--R-R-RR---LRRR--RRR-RRLRRR-LR-RL-RR-R--R-R-L-RRR-R----R-RRRRR-R-R-LLR-RR--RL--L--LLR-LR--RLRRRRL-RLRL-RL-R-LRR-R--R-RRLRRRR-R-R-RR-RRRRR-----R-RRRRR-R----R-L-R----R--RR-----RR---R---R-RRRRR-R-RLRRR-L--LRRRR-R-RR--RRLR-RL--RRLR-LL--RR--RR---------R--R---RLRRL--R-RRRRR-R--R---R-R-R-LL-RLRRR------R----RR-RRR-R-R---R-L-RR--R---LRR-L-RRR-------RRRRR--R--R-R--R-RR-RRR-R-RRL-RR-R----R---RRRR-R---RRRR--RR-L---LL-RR---RRR--RRLR-L--R-RRR---RR--R-R---RR-RR-RR-RR-R-R--R---RRRL-RR-RR-RR-R-RRRR--RRRLLRL--R-RR--LR-RR-R-RRR-RRR-R-R-L-R-R-R-RRRR-RRR--RL-RRRR-RLR----R-LR---RRRR-R--RR-R-R-R---RR-RR--LRRRLRR-RRRR-R---RL---RL---L-LR---RRRR---R-L--RRR-RL----RR--RRRR---RR-RRR-RR----RR-RR----LRR-R---RRR---RRR-RRR-L----R--RRRR--RRR-RRRR---R-RR--RRRRR--RR-RR-----LR--RR--R--RLRRRL-LRLR--L--L-R---RRRL-RL-RR-RRRR------RR---RRRRR-RLRR-L-RRRR---RRL-RRRR--R---RRR-RRR-RR--R-RR-R--RLRRRR-R----RR--RRR----R-R-R-RR-RR-RR--RR--------LR-RRRRR-RRRRRL--RRR-RR-R--RRRR-RR---RLLRL---LRR----RRRR-R-R-L-RR-RRL--RR-RR----R-R-L-RLR--RRL-LR-R-LRR-R-R-----R-RL---RRRRRRRR-RRR-R--RLLRR---RRRLRRRLR--RR--RLRRR---RL--RRRRRR-----RRRLR---R-L-LLRR-R--R---R-RRR-RR-R-RRRR-RRR-RRRRRR--RRRR--RR-R---RRR-R-RRR--RRRRRRLLRR-RRR-RLRRRR-R-RRR-R-L-R-R--R-R--RRRR-RR--RR----R--RLR-RR-R-------RR--L-R-RL----RR--R--R--RRRRR-------R---RR--RR-RR-R--R-RLL---RRRR-R-R--RRLLR--R-R-RL---LLRR-R-RRR-R-RR-R-RR--R-L-RR--LRRR-RL--R-R---R-RR-RR-LLRRRRRRRR-R--R--RRR--RRL--R----LLR--R--RRRL---R--RRRLRRRRRRRRR-RRRRRRLL--RRR--R--LR-RRR--R-----R--RR-R-RRRR-R---RR-R--R-R-R-R-R-R---LRLRRLRR-RR-RRRRRR----RR-R-RRR--R--RRR-RRLRL--RRR-R-RRR-L-L--RRRRRR---R-RRRR--RR-L-R---RL--RR-RRR-RL--R----LLR-RL-LR-R-RR-LRLLLRRR--RRRR-RRR---RLRRR--R-RR--R-RRL-RRLLLLR-LR----R-R-R--RR--R--R--RRRRRR-RR-RLRR-L-R--R-RR-LRRR----RLRRRR--RRR--RRR-R--RRRLR---R---RRR--RR--R-RR--RRRRR-R-----RRRLR-----L--RR-RR-RR--RR-RRRR--RRLL--RR-R-RRR-RR--RRRR-RRRR-LRL-R----R-RLRLLL-R-RRR---L-RR-R-RRRRRR-RR--RL-RR-LR-RR---RRL--R-L-RLRR-----RRR-RL--R---RRRRR-LLLR--L-R-R--R--RR--L-RRRR---RLR---LRR--RR--R--L----RRLLR-R--R-RR--R---R-RRL-L---R--R-L--RRRL-RLRRRR-RRR---LRRRRRRLR-L--R-RR---RRR-RRR---RRRR-L-RLRLR-LL-L-RRRR-RR-R---RRRRR---RRRLR-RR-R-RRRLR--RRLR-LLRL--RL-RLRL--R--RR-L-L-RRR-RRRR--R-R-RL-R---RL-L--RRRR-RRR-R-R---LRRL-RLL--RRR-LRR--RRRR-LRLR-R--LR--R-LR-R-RR-R-RRRRLR-RRRR-R-RR-R--------R--RRR-R-R--RL-L-L-----R--RRL-L-RRR-R--RRLR-RRLR--R---RR---R-RR-R---RR-R-----RL---R-R-L-R-R-RR-RRL-R-LRR---L--R---R-L---R-----RR-R-RLRR--R-RR-RR-L-R-R-R-RLRR-RR---R-R--RL-RRRRRL-R----R----R-RR--LR-R--LRR-RR-R-R--RRR-R-RRL---RRR--RRR-----R-R--R-R----------RR----RRLL-RRR----R-RR---RR-----LRR--R-R--R--RR-LR----RR-RR--------RRRR---RRRRRR-RR-RR--RLRR--RLR--RR-L-L------L-L---RRLRRRR-RR-----RRR--R-RL-----RL--RRRLLRR-RRRR--L-RRLRRR-R--R--RL----RRRRRLRL-RLR-RRRRR-R---L---RR---RR-R--RRR--R----R-RR--R-R-RR-R-RR-R--R-LR--LR--RRR-R--RR--RRR--R-R-RR--L------R--L---R---RR-R---RL--L--R---RRR-L--R----R-RRR--RR-LR----LR-R-RRLLRR-R-R--R-RR-R--R-R----RR-L---RLRR--L---RR---RRLR-R-LRRR--R-RR-R-RRRR--LRLRL--R--R-RR---RR-R--R-RR-L----LR--L-LR--RRL----RRL---RR-R-LRRL---R---R-L--RR---R--R-RL--RRRRLL--L---L------R--RR--R-L-R--R--RRL--RRR-R--RR-R-RRL--RRRL------R--R-R-R---L-R-RLLRRR-RRRRR-----RRR--RR-L-LRR-L---L-R-LR-----R-R-RL--L-R--RR-RR-R-RRR-RRR---R-----RR----RRRRRR----R-RLR-R--R-L-R--R---RLLL-R-R--R-L-R-R-----R---R-RR-RRR-RRR--R-R--R-R-R-R----L--L----R--RLRLR--R---RLR-R----R---RRRL---LL-R-R--RRRLRL------R----RLL--L---RLRR--RLR-R----RR-R-R-L--RR--R-RR---L---RR---RR-------R-RL--------RR--RRRR--L-RR---RLLR-LR-RL--R-R-R-R-----R-R-RR-R-R-RRLRR---R-RR-R-RR-L--R-LRRRR-RRRRR-R-RR-R----R-RRRR-RR--R-R----RRRRR-RRR--RL-RL---R-L--RR-RR---------RLL---L---RRR--RR---R---RLRRR-----L-R-RRRRL-R-L------R--LL--L-R-L---LRR---R--L---R-RR----RR---RL-----RR----L-RRRLRR-LR---LR---RRR-L-R--RR--RR--RLLRR-RRL-RRRLL-R---LRLR-R---RRR-RRRRR-R-R-RRRRLRL--RL, improve=0.30426700, (0 missing)
## Spa < 1346.5 to the right, improve=0.13215820, (0 missing)
## VRDeck < 1058 to the right, improve=0.12338610, (0 missing)
## CryoSleep splits as LRL, improve=0.11239120, (0 missing)
## RoomService < 366.5 to the right, improve=0.08946097, (0 missing)
## Surrogate splits:
## ailesira splits as RLLLLLLL, agree=0.740, adj=0.239, (0 split)
## Age < 10.5 to the left, agree=0.683, adj=0.070, (0 split)
## Spa < 1024 to the right, agree=0.681, adj=0.065, (0 split)
## VRDeck < 991.5 to the right, agree=0.680, adj=0.062, (0 split)
## RoomService < 705.5 to the right, agree=0.674, adj=0.044, (0 split)
##
## Node number 4: 3173 observations
## mean=0.0009454775, MSE=0.0009445835
##
## Node number 5: 607 observations
## mean=0.324547, MSE=0.2192162
##
## Node number 6: 1676 observations, complexity param=0.04828533
## mean=0.576969, MSE=0.2440758
## left son=12 (1025 obs) right son=13 (651 obs)
## Primary splits:
## CryoSleep splits as LRL, improve=0.2565094, (0 missing)
## Spa < 0.5 to the right, improve=0.2056128, (0 missing)
## VRDeck < 1.5 to the right, improve=0.1922289, (0 missing)
## RoomService < 0.5 to the right, improve=0.1675086, (0 missing)
## FoodCourt < 0.5 to the right, improve=0.1407524, (0 missing)
## Surrogate splits:
## Spa < 0.5 to the right, agree=0.725, adj=0.292, (0 split)
## FoodCourt < 0.5 to the right, agree=0.718, adj=0.275, (0 split)
## VRDeck < 0.5 to the right, agree=0.711, adj=0.257, (0 split)
## ailenum splits as ---------------L---------------RL----------L-----------L-----LR-LL--R---L------L-------L--L-L----------L-L--------L--------L-------------L-------R---------------L--------L----------------L--------------------------L-----------------------L--------R----------------------------R-R------L--L---------L------------L---L---R---------L--------------------L--R-----L----------L--L--R---L--R-----L----R---------R-L-R--------RL--------L----------R----L------L-------------L-----L--L--------L-R------------------R-R----L-L---L-----------------------L-LL------L-----L---------------------------------RL---L--------L------L--RL--L--L-L----------------------------L----L---L-------L--L--------------L-----------------------------------------L-L----------LL----------L----------L-----LLL--L----------------------------L----------------L-----------------------------------R-----R----L--L----L----R--L----------L---L-----L-------L-----L-----------------L-------------R-L--------------LL-L-------------------L---L------L----------------------------------L------------L---------L-------L--L--L------------------R-L-------------------------------LL---------L------L-------L---------------L-L----------------------R--------L------------------R-------L----L----------------------L------------LR----------------------L-L----------------R-----L------------R-----L--R-----------------L--------L-L-L-----L---L-------------------------R--------L---------------------------L------------------L-------------L-----------------------------------------------------------LL-R----------------LL-------LR--L-----------------------------L-L---------------------------R------------------------------------L-----L-L-------------LL-----L--L------------L-L----------R-----LLL--------------L--L------------------------L----------------------------------L---------L----RL--L--------------------------------L---------L--LL----------------R----L---------------------L------------------L-------L--------------R----------------------------------------------L-L---------------L--L-----------L--------R------L------------L-----L------------L----------------------------------------L-----------------------------------------------------L------L---L---R-----L------------------------R----------L---R---------L------L--------------L------------------LLL-----------------------R-RLL---LL---L----L-L------R--L------L--L----------L---L-----------------------L-----------L----L---L-----------L----------------------LL-------L--L--LL--R----L----L--L-L--R---L----------L-------------------------------------L------------------------------------L----L--R-------------L---L----R--LL-------------------------L--L-----------------------RL--L-----------------------------L---------R---L---------------------------------------L-------------------------------L---LR-------------L--R--------------------------------------------L---------------------LR-L--------L-------------------L------------------L-------R-------L------------------------------L---L-------------L----L---L-L-------------L-------L-------------------------------------L---------------------L-----------------------------------------------L-----------L---L-L-L---R--L--------L--L--------------------------R---L----------L--------------------------------L-----------------------------------------------L-------------L-----------------------LL-L---L---------------R------L---------------L--L-----R-L----L--------------L--------------------LL--------L---L--------L-------L----------------R------L-LL--------------------------------------------------------------------LL--------L-------------L------------------------------L-----------------L----L----------------------------------------------LR---------------LL--------L---LL----------------------L-----L-----L----------------LL----------------------L-------LL---------R---------L----------------LL----------L--------------------------------------------------L-L--R-----------------------------------L-L------------L-L----------------------L------R----------L-------RL---L-L-------L-LLL-----------------L---------------L---RLLL--L---------------------------------L---L---------L--------L---------------------L--------------------------------------L------L-----------------------LL-------------------------L-L---------R-LLL---------L------------------L----L---------R----L--L------------L------------LLR---L-------------R---------R----L-----------L------RL-------------------L-L--------R-----L--L-----------L------L--L------------------------L--L-L--LL-L------------------------R----------L-----L--RL-L---L--L-R--------L-L----------------R------L-L-----------------L--L--LL------L---------L-L-----L-----L-------------L----------------------------------L-L-L----------L-L----------L----L-------------------------------L-------R----------R---L-----L--------L---------------L------------L--------L--------------L------L-----------------L-----L-------------------R------------------------------------------LR----------------------L---------------L----------------------------------------L-----L------R-L------L-L-----L--------------------L------L-----LL---------L---L-----------L---------L-L--R------------L-----------------------------------------------L---L---------------------------L---------L---------------L--L----------L-----------------L-----L------RL--------------------------L----L----L----------L----R------------------L-R-L-----------------------R----L---L-R-----L------L--------L--L---------R-------------L------LL--R---L---------------L---------L----------------L-----L-----------------L----LL----------------------L-L---L---L---L-----------R--L-----------------------------------------------L-------L---------LLL--------L-------------------------------------------R--L--------L-L--------R--------------L---LR---------L-L------------LL--L----L-----L--------------L-------------L--------------------L------------------L-------LL--L---L---------------------------L---------------L----R---------------------------------------------------L--L-----L-----------------RL---L------------------L--------L-------L---L---------LR--L---L---L--------L-----------------L-----------L----L---R----L--------R-------------LL-----L----LL-----R-L------------------------L-L---L, agree=0.700, adj=0.227, (0 split)
## RoomService < 0.5 to the right, agree=0.665, adj=0.137, (0 split)
##
## Node number 7: 3237 observations
## mean=0.9919679, MSE=0.007967613
##
## Node number 12: 1025 observations, complexity param=0.02910718
## mean=0.377561, MSE=0.2350087
## left son=24 (317 obs) right son=25 (708 obs)
## Primary splits:
## ailenum splits as ---------------R----------------L----------R-----------R-----LL-RR--R---R------L-------L--L-L----------R-L--------R--------R-------------R-------L---------------R--------L----------------L--------------------------R-----------------------L--------L----------------------------L-L------R--R---------L------------R---R---L---------L--------------------L--------L----------L--R--L---L--L-----R----L-----------R-L--------RR--------R----------L----R------R-------------L-----L--L--------R-R------------------L-L----R-L---R-----------------------R-RR------R-----R----------------------------------R---R--------R------L--LR--L--R-R----------------------------L----R---R-------R--R--------------R-----------------------------------------R-L----------LR----------R----------L-----LLR--L----------------------------L----------------L-----------------------------------L-----L----R--L----L----L--R----------L---R-----L-------R-----R-----------------R-------------L-L--------------LL-R-------------------R---R------R----------------------------------L------------R---------R-------R--R--L------------------R-R-------------------------------LR---------R------L-------L---------------R-L----------------------R--------L------------------L-------L----L----------------------R------------LR----------------------R-R----------------L-----R------------L-----R--L-----------------R--------L-R-R-----L---R----------------------------------L---------------------------L------------------R-------------R-----------------------------------------------------------RR-L----------------LL-------RL--R-----------------------------R-L----------------------------------------------------------------R-----L-L-------------LL-----R--R------------L-L----------L-----RLL--------------L--L------------------------L----------------------------------R---------L----LL--R--------------------------------L---------R--LL----------------L----R---------------------R------------------L-------R--------------L----------------------------------------------R-L---------------R--R-----------L--------L------L------------L-----R------------L----------------------------------------L-----------------------------------------------------R------L---R---------L------------------------L----------R---L---------L------L--------------R------------------LLR-------------------------LRR---RR---L----L-R---------L------L--L----------L---L-----------------------R-----------R----R---L-----------R----------------------LL-------R--L--RR--L----L----L--R-L------L----------L-------------------------------------L------------------------------------R----R--L-------------R---L----L--RL-------------------------R--R-----------------------LR--R-----------------------------R---------L---L---------------------------------------R-------------------------------L---RL-------------R--L--------------------------------------------L---------------------RL-R--------R-------------------L------------------L-------L-------R------------------------------R---R-------------L----L---R-L-------------R-------R-------------------------------------L---------------------R-----------------------------------------------R-----------R---L-L-R------R--------R--L--------------------------L---L----------R--------------------------------L-----------------------------------------------L-------------R-----------------------LL-R---R---------------L------L---------------R--L-----L-L----L--------------R--------------------RR--------R---L--------L-------L-----------------------R-RL--------------------------------------------------------------------RL--------L-------------L------------------------------R-----------------R----R----------------------------------------------R----------------LR--------L---LL----------------------L-----L-----R----------------LL----------------------R-------RL---------L---------R----------------RL----------R--------------------------------------------------R-R--R-----------------------------------R-R------------R-R----------------------L------L----------L-------LL---R-R-------R-RLR-----------------L---------------R---LLRR--R---------------------------------L---R---------L--------R---------------------L--------------------------------------R------R-----------------------LL-------------------------R-L---------L-RRL---------R------------------R----R---------L----R--R------------L------------RLL---L-------------L---------L----R-----------R------LL-------------------R-R--------------R--R-----------R------L--L------------------------R--R-R--RR-R------------------------L----------R-----R---L-R---L--R-L--------R-R----------------R------L-R-----------------R--L--LR------L---------R-R-----R-----L-------------R----------------------------------R-L-L----------R-R----------R----R-------------------------------L-------L----------L---R-----R--------L---------------L------------R--------R--------------R------R-----------------R-----L-------------------L------------------------------------------R-----------------------L---------------L----------------------------------------L-----R------L-R------R-L-----L--------------------R------R-----RR---------L---L-----------R---------L-R--R------------L-----------------------------------------------L---L---------------------------L---------L---------------L--R----------L-----------------R-----R------LL--------------------------L----L----R----------L-----------------------R-L-R-----------------------L----R---L-R-----L------R--------R--R---------L-------------L------RR--L---R---------------R---------L----------------R-----R-----------------R----LL----------------------R-R---R---L---L-----------L--R-----------------------------------------------L-------L---------RLR--------L-------------------------------------------L--R--------R-R--------L--------------R---LL---------L-L------------RR--R----L-----R--------------R-------------L--------------------R------------------L-------LR--R---R---------------------------L---------------L----L---------------------------------------------------R--R-----R-----------------LR---L------------------R--------R-------L---L---------LR--R---R---R--------L-----------------L-----------L----R--------L--------L-------------RR-----L----RR-----L-R------------------------R-R---R, improve=0.26259070, (0 missing)
## Age < 12.5 to the right, improve=0.14269010, (0 missing)
## Spa < 420 to the right, improve=0.09687267, (0 missing)
## RoomService < 249.9754 to the right, improve=0.08111019, (0 missing)
## VRDeck < 87.5 to the right, improve=0.07744737, (0 missing)
## Surrogate splits:
## Spa < 2110.5 to the right, agree=0.704, adj=0.044, (0 split)
## VRDeck < 2266 to the right, agree=0.704, adj=0.044, (0 split)
## VIP splits as RLR, agree=0.695, adj=0.013, (0 split)
## RoomService < 3243 to the right, agree=0.694, adj=0.009, (0 split)
##
## Node number 13: 651 observations, complexity param=0.0131094
## mean=0.890937, MSE=0.09716825
## left son=26 (124 obs) right son=27 (527 obs)
## Primary splits:
## ailenum splits as -------------------------------LR----------R-----------R-----RR-----L---R------R-------R--R-R----------L-R-----------------R-------------R-------R------------------------R----------------R--------------------------------------------------R--------R----------------------------R-R-------------------R--------------------R---------R--------------------R--L-----R----------R-----R---R--R----------R---------L-R-R--------L--------------------R-------------------------R-----R--R----------L------------------R-R------R---R--------------------------R----------------------------------------------L----L---------------R--R---R---------------------------------R--------R-------R--R--------------------------------------------------------L-R----------R-----------L----------R-----RR---R----------------------------R----------------R-----------------------------------R-----R----L--R----R----R-------------R---R-----R-------------L-------------------------------R-R--------------RR-------------------------R------R----------------------------------R----------------------R-------R-----R------------------L---------------------------------R-----------------R-------R-----------------R----------------------L--------R------------------R-------R----R-----------------------------------RL------------------------R----------------R------------------R--------R-----------------L--------R---------R-----------------------------L--------R---------------------------R------------------R-------------R-----------------------------------------------------------RR-R----------------RR--------R--L-----------------------------R-R---------------------------L------------------------------------------R-R-------------RR---------------------R-R----------R------RR--------------R--R------------------------R----------------------------------R---------R----RR-----------------------------------R------------RR----------------R---------------------------------------------R----------------------R------------------------------------------------R------------------------------R--------R------R------------R-----R------------R----------------------------------------R-----------------------------------------------------L------R-------L-----R------------------------R----------R---R---------R------R--------------R------------------RRR-----------------------L-RR----LR---R----R--------L--R------R--R----------R---R-----------------------------------R--------R-----------R----------------------RR-------R--R------R----R----R----R--L---R----------R-------------------------------------R------------------------------------L-------R-------------R---R----R---R----------------------------------------------------RL--L-----------------------------R---------R---R---------------------------------------R-------------------------------R---RR----------------R--------------------------------------------R----------------------R----------R-------------------R------------------R-------R-------R------------------------------R---R-------------R----R---L-R-----------------------------------------------------------R---------------------------------------------------------------------R-----------R---R-R-R---L--------------R--------------------------R---R-------------------------------------------R-----------------------------------------------R-------------R-----------------------RR---------------------R------R---------------R--R-----R-R----R---------------------------------------------R---R--------R-------R----------------L---------R--------------------------------------------------------------------RR--------R-------------R------------------------------------------------R---------------------------------------------------RL---------------R---------R---RR----------------------R-----R----------------------RR----------------------R-------LR---------R--------------------------LR------------------------------------------------------------------R-----------------------------------R--------------R------------------------R------R----------R-------RR---R-R----------R------------------R-------------------RRL-------------------------------------R-------------R------------------------------R---------------------------------------------------------------------RR-------------------------R-R---------R---R---------------------------------R---------R-------R------------R------------RRR---R-------------L---------R----R-----------R------RR------------------------------L-----L--------------L------R--R------------------------R--R----RR-R------------------------R----------------R--LR-----R----R----------R----------------L------R-R-----------------R--R--R-------R-----------------R-----R--------------------------------------------------R-R----------------------------L-------------------------------R-------R----------R------------------R---------------R------------R--------R---------------------L-----------------------R-------------------R------------------------------------------RL----------------------R---------------R----------------------------------------R-----R------R--------L-R-----R--------------------R------------RR---------R---R---------------------R----L------------R-----------------------------------------------R---R---------------------------R---------R---------------R-------------R-----------------------L------RR--------------------------R----R----R----------R----L--------------------R-R-----------------------R--------R-L-----R------L--------R--R---------R-------------R------RR--R---R---------------L---------R----------------------------------------R----RR------------------------R-------R---R-----------R--R-----------------------------------------------R-------R----------RR--------R-------------------------------------------R-------------R--------R--------------R---RR---------R-R------------RL--R----R----------------------------------R--------------------R------------------R-------R-------L---------------------------R---------------R----R---------------------------------------------------R--------R-----------------LR---R---------------------------R-------R---R---------RR--L---L---L--------R-----------------R-----------R----R---L----R--------L--------------------R-----R-----R-R-------------------------------, improve=0.45036460, (0 missing)
## deck splits as RRRRLRL-, improve=0.11754260, (16 missing)
## HomePlanet splits as LRRR, improve=0.09976461, (0 missing)
## Age < 12.5 to the left, improve=0.02356719, (0 missing)
## Destination splits as RLLR, improve=0.01419214, (0 missing)
## Surrogate splits:
## ailesira splits as RRRRRLRL, agree=0.817, adj=0.04, (0 split)
##
## Node number 24: 317 observations
## mean=0.006309148, MSE=0.006269343
##
## Node number 25: 708 observations
## mean=0.5437853, MSE=0.2480828
##
## Node number 26: 124 observations
## mean=0.4596774, MSE=0.2483741
##
## Node number 27: 527 observations
## mean=0.9924099, MSE=0.007532523“rpart” paketini kullanarak bir karar ağacı (decision tree) modelini eğitir ve bu modelin özet istatistiklerini görüntüler.
rpart.plot(fit_tree)## Warning: labs do not fit even at cex 0.15, there may be some overplotting
rpart.plot fonksiyonu, “rpart” paketindeki bir karar ağacını görselleştirmek için kullanılır.
y_pred = ifelse(preds == TRUE,TRUE,FALSE)“preds” veri çerçevesindeki her bir gözlem için “TRUE.” sütunundaki değeri kontrol eder ve bu değer “TRUE” ise 1, değilse 0 olarak sınıflandırır.
Transported <- as.character(y_pred)
PassengerId<- test$PassengerIddaha önce oluşturulan “y_pred” vektöründeki sınıflandırılmış sonuçları karakter dizisine (as.character) dönüştürür ve bu değerleri “Transported” adlı yeni bir vektöre atar. Aynı zamanda, “test” veri çerçevesindeki “PassengerId” sütununu “PassengerId” adlı başka bir vektöre atar
Transported<- as.vector(Transported)“Transported” adlı vektörü as.vector fonksiyonu kullanarak bir vektör olarak günceller. as.vector fonksiyonu, belirli bir veri yapısını vektöre dönüştürmek için kullanılır
submission <- cbind(PassengerId, Transported)“PassengerId” ve “Transported” vektörlerini birleştirerek yeni bir veri çerçevesi olan “sample_submission”ı oluşturur. cbind fonksiyonu, sütunları birleştirmek için kullanılır ve bu durumda “PassengerId” ve “Transported” sütunları “sample_submission” veri çerçevesini oluşturur.
submission <- as.data.frame(submission)“sample_submission” adlı nesnenin tipini veri çerçevesine dönüştürür. Yani, “sample_submission” nesnesinin içeriği aynı kalmakla birlikte, artık bir veri çerçevesi olarak ele alınacaktır
submission$Transported <- str_to_title(submission$Transported)str_to_title fonksiyonu, “stringr” paketinde bulunan bir fonksiyondur ve bir karakter dizisindeki her kelimenin baş harfini büyük yapar.
write.csv(submission, "submission_.dtree", row.names =FALSE, quote=FALSE)Bu kodu çalıştırdığınızda, “submission” veri çerçevesi “submission_.dtree” adlı bir CSV dosyasına yazılır. Bu dosya, tahmin sonuçları veya diğer veri çerçevesi içeriğini içerir ve genellikle analiz veya raporlama amacıyla kullanılır.
