Ejercicio Clase 1 - Árboles de Decisión
Ana Estefanía López - A01284416
2023-02-14
Importar Base de Datos
titanic <- read.csv("titanic.csv")
head(titanic)## pclass survived name sex
## 1 1 1 Allen, Miss. Elisabeth Walton female
## 2 1 1 Allison, Master. Hudson Trevor male
## 3 1 0 Allison, Miss. Helen Loraine female
## 4 1 0 Allison, Mr. Hudson Joshua Creighton male
## 5 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female
## 6 1 1 Anderson, Mr. Harry male
## age sibsp parch ticket fare cabin embarked boat body
## 1 29.0000 0 0 24160 211.3375 B5 S 2 NA
## 2 0.9167 1 2 113781 151.5500 C22 C26 S 11 NA
## 3 2.0000 1 2 113781 151.5500 C22 C26 S NA
## 4 30.0000 1 2 113781 151.5500 C22 C26 S 135
## 5 25.0000 1 2 113781 151.5500 C22 C26 S NA
## 6 48.0000 0 0 19952 26.5500 E12 S 3 NA
## home.dest
## 1 St Louis, MO
## 2 Montreal, PQ / Chesterville, ON
## 3 Montreal, PQ / Chesterville, ON
## 4 Montreal, PQ / Chesterville, ON
## 5 Montreal, PQ / Chesterville, ON
## 6 New York, NYEntender la Base de Datos
summary(titanic)## pclass survived name sex
## Min. :1.000 Min. :0.000 Length:1310 Length:1310
## 1st Qu.:2.000 1st Qu.:0.000 Class :character Class :character
## Median :3.000 Median :0.000 Mode :character Mode :character
## Mean :2.295 Mean :0.382
## 3rd Qu.:3.000 3rd Qu.:1.000
## Max. :3.000 Max. :1.000
## NA's :1 NA's :1
## age sibsp parch ticket
## Min. : 0.1667 Min. :0.0000 Min. :0.000 Length:1310
## 1st Qu.:21.0000 1st Qu.:0.0000 1st Qu.:0.000 Class :character
## Median :28.0000 Median :0.0000 Median :0.000 Mode :character
## Mean :29.8811 Mean :0.4989 Mean :0.385
## 3rd Qu.:39.0000 3rd Qu.:1.0000 3rd Qu.:0.000
## Max. :80.0000 Max. :8.0000 Max. :9.000
## NA's :264 NA's :1 NA's :1
## fare cabin embarked boat
## Min. : 0.000 Length:1310 Length:1310 Length:1310
## 1st Qu.: 7.896 Class :character Class :character Class :character
## Median : 14.454 Mode :character Mode :character Mode :character
## Mean : 33.295
## 3rd Qu.: 31.275
## Max. :512.329
## NA's :2
## body home.dest
## Min. : 1.0 Length:1310
## 1st Qu.: 72.0 Class :character
## Median :155.0 Mode :character
## Mean :160.8
## 3rd Qu.:256.0
## Max. :328.0
## NA's :1189str(titanic)## 'data.frame': 1310 obs. of 14 variables:
## $ pclass : int 1 1 1 1 1 1 1 1 1 1 ...
## $ survived : int 1 1 0 0 0 1 1 0 1 0 ...
## $ name : chr "Allen, Miss. Elisabeth Walton" "Allison, Master. Hudson Trevor" "Allison, Miss. Helen Loraine" "Allison, Mr. Hudson Joshua Creighton" ...
## $ sex : chr "female" "male" "female" "male" ...
## $ age : num 29 0.917 2 30 25 ...
## $ sibsp : int 0 1 1 1 1 0 1 0 2 0 ...
## $ parch : int 0 2 2 2 2 0 0 0 0 0 ...
## $ ticket : chr "24160" "113781" "113781" "113781" ...
## $ fare : num 211 152 152 152 152 ...
## $ cabin : chr "B5" "C22 C26" "C22 C26" "C22 C26" ...
## $ embarked : chr "S" "S" "S" "S" ...
## $ boat : chr "2" "11" "" "" ...
## $ body : int NA NA NA 135 NA NA NA NA NA 22 ...
## $ home.dest: chr "St Louis, MO" "Montreal, PQ / Chesterville, ON" "Montreal, PQ / Chesterville, ON" "Montreal, PQ / Chesterville, ON" ...Filtrar Base de Datos
Titanic <- titanic[,c("pclass", "age", "sex", "survived")]
Titanic$survived <- as.factor(ifelse(Titanic$survived==0, "Murio", "Sobrevivió"))
Titanic$pclass <- as.factor(Titanic$pclass)
Titanic$sex <- as.factor(Titanic$sex)
str(Titanic)## 'data.frame': 1310 obs. of 4 variables:
## $ pclass : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ age : num 29 0.917 2 30 25 ...
## $ sex : Factor w/ 3 levels "","female","male": 2 3 2 3 2 3 2 3 2 3 ...
## $ survived: Factor w/ 2 levels "Murio","Sobrevivió": 2 2 1 1 1 2 2 1 2 1 ...sum(is.na(Titanic))## [1] 266sapply(Titanic, function(x) sum(is.na(x)))## pclass age sex survived
## 1 264 0 1Titanic <- na.omit(Titanic)Crear Árbol de Decisión
#install.packages("rpart")
library(rpart)
arbol <- rpart(formula=survived ~ ., data = Titanic)
arbol## n= 1046
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 1046 427 Murio (0.59177820 0.40822180)
## 2) sex=male 658 135 Murio (0.79483283 0.20516717)
## 4) age>=9.5 615 110 Murio (0.82113821 0.17886179) *
## 5) age< 9.5 43 18 Sobrevivió (0.41860465 0.58139535)
## 10) pclass=3 29 11 Murio (0.62068966 0.37931034) *
## 11) pclass=1,2 14 0 Sobrevivió (0.00000000 1.00000000) *
## 3) sex=female 388 96 Sobrevivió (0.24742268 0.75257732)
## 6) pclass=3 152 72 Murio (0.52631579 0.47368421)
## 12) age>=1.5 145 66 Murio (0.54482759 0.45517241) *
## 13) age< 1.5 7 1 Sobrevivió (0.14285714 0.85714286) *
## 7) pclass=1,2 236 16 Sobrevivió (0.06779661 0.93220339) *#install.packages("rpart.plot")
library(rpart.plot)
rpart.plot(arbol)
prp(arbol,extra = 7, prefix = "fracción")
Conclusiones
- Las probabilidades de supervivencia más altas en el Titanic son los
niños menores de 9.5 años de primera y segunda clase(100%), y mujeres de
primera y segunda clase (93%).
- Las probabilidades de supervivencia más bajas en el Titanic son los hombres mayores de 9.5 años (18%), y hombres de tercera clase menores de 9.5 (38%).
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