Arboles de decisión
Dyann Estefanía Hernández Ayala A00837191
2025-08-20
Titanic
Instalar paquetes y llamar librerias
#install.packages("rpart")
library(rpart)
#install.packages("rpart.plot")
library(rpart.plot)
library(readr)Importar base de datos
titanic <- read_csv("~/Documents/Modulo2/titanic.csv")## Rows: 1310 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): name, sex, ticket, cabin, embarked, boat, home.dest
## dbl (7): pclass, survived, age, sibsp, parch, fare, body
##
## 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.Entender 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)## spc_tbl_ [1,310 × 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ pclass : num [1:1310] 1 1 1 1 1 1 1 1 1 1 ...
## $ survived : num [1:1310] 1 1 0 0 0 1 1 0 1 0 ...
## $ name : chr [1:1310] "Allen, Miss. Elisabeth Walton" "Allison, Master. Hudson Trevor" "Allison, Miss. Helen Loraine" "Allison, Mr. Hudson Joshua Creighton" ...
## $ sex : chr [1:1310] "female" "male" "female" "male" ...
## $ age : num [1:1310] 29 0.917 2 30 25 ...
## $ sibsp : num [1:1310] 0 1 1 1 1 0 1 0 2 0 ...
## $ parch : num [1:1310] 0 2 2 2 2 0 0 0 0 0 ...
## $ ticket : chr [1:1310] "24160" "113781" "113781" "113781" ...
## $ fare : num [1:1310] 211 152 152 152 152 ...
## $ cabin : chr [1:1310] "B5" "C22 C26" "C22 C26" "C22 C26" ...
## $ embarked : chr [1:1310] "S" "S" "S" "S" ...
## $ boat : chr [1:1310] "2" "11" NA NA ...
## $ body : num [1:1310] NA NA NA 135 NA NA NA NA NA 22 ...
## $ home.dest: chr [1:1310] "St Louis, MO" "Montreal, PQ / Chesterville, ON" "Montreal, PQ / Chesterville, ON" "Montreal, PQ / Chesterville, ON" ...
## - attr(*, "spec")=
## .. cols(
## .. pclass = col_double(),
## .. survived = col_double(),
## .. name = col_character(),
## .. sex = col_character(),
## .. age = col_double(),
## .. sibsp = col_double(),
## .. parch = col_double(),
## .. ticket = col_character(),
## .. fare = col_double(),
## .. cabin = col_character(),
## .. embarked = col_character(),
## .. boat = col_character(),
## .. body = col_double(),
## .. home.dest = col_character()
## .. )
## - attr(*, "problems")=<externalptr>head(titanic)## # A tibble: 6 × 14
## pclass survived name sex age sibsp parch ticket fare cabin embarked
## <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <chr> <chr>
## 1 1 1 Allen, M… fema… 29 0 0 24160 211. B5 S
## 2 1 1 Allison,… male 0.917 1 2 113781 152. C22 … S
## 3 1 0 Allison,… fema… 2 1 2 113781 152. C22 … S
## 4 1 0 Allison,… male 30 1 2 113781 152. C22 … S
## 5 1 0 Allison,… fema… 25 1 2 113781 152. C22 … S
## 6 1 1 Anderson… male 48 0 0 19952 26.6 E12 S
## # ℹ 3 more variables: boat <chr>, body <dbl>, home.dest <chr>Crear árbol de decisión
titanic <- titanic[,c("pclass", "age", "sex", "survived")]
titanic$survived <- as.factor (titanic$survived)
titanic$pclass <- as.factor (titanic$pclass)
titanic$sex <- as.factor (titanic$sex)
str(titanic)## tibble [1,310 × 4] (S3: tbl_df/tbl/data.frame)
## $ pclass : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ age : num [1:1310] 29 0.917 2 30 25 ...
## $ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ...
## $ survived: Factor w/ 2 levels "0","1": 2 2 1 1 1 2 2 1 2 1 ...arbol_titanic <- rpart (survived~., data=titanic)
rpart.plot(arbol_titanic)
prp(arbol_titanic, extra=7, prefix="fraccion\n")
# Conclusiones En conclusión, las más
altas probabilidades de sobrevivir en el naufragio del Titanic son: *
100%: si eres niño varón menor de 9.5 años de 1° o 2° clase. * 73%: si
eres mujer.
Y, por el contrario, las más bajas probabilidades de sobrevivir son: * 17%: si eres hombre mayor a 9.5 años. * 38%: si eres niño varón menor de 9.5 años de 3° clase.
Cancer de mama
# Importar la base de datos
cancer <- read_csv("~/Documents/cancer_de_mama.csv")## Rows: 569 Columns: 31
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): diagnosis
## dbl (30): radius_mean, texture_mean, perimeter_mean, area_mean, smoothness_m...
##
## 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.Analisis de base de datos
summary(cancer)## diagnosis radius_mean texture_mean perimeter_mean
## Length:569 Min. : 6.981 Min. : 9.71 Min. : 43.79
## Class :character 1st Qu.:11.700 1st Qu.:16.17 1st Qu.: 75.17
## Mode :character Median :13.370 Median :18.84 Median : 86.24
## Mean :14.127 Mean :19.29 Mean : 91.97
## 3rd Qu.:15.780 3rd Qu.:21.80 3rd Qu.:104.10
## Max. :28.110 Max. :39.28 Max. :188.50
## area_mean smoothness_mean compactness_mean concavity_mean
## Min. : 143.5 Min. :0.05263 Min. :0.01938 Min. :0.00000
## 1st Qu.: 420.3 1st Qu.:0.08637 1st Qu.:0.06492 1st Qu.:0.02956
## Median : 551.1 Median :0.09587 Median :0.09263 Median :0.06154
## Mean : 654.9 Mean :0.09636 Mean :0.10434 Mean :0.08880
## 3rd Qu.: 782.7 3rd Qu.:0.10530 3rd Qu.:0.13040 3rd Qu.:0.13070
## Max. :2501.0 Max. :0.16340 Max. :0.34540 Max. :0.42680
## concave_points_mean symmetry_mean fractal_dimension_mean radius_se
## Min. :0.00000 Min. :0.1060 Min. :0.04996 Min. :0.1115
## 1st Qu.:0.02031 1st Qu.:0.1619 1st Qu.:0.05770 1st Qu.:0.2324
## Median :0.03350 Median :0.1792 Median :0.06154 Median :0.3242
## Mean :0.04892 Mean :0.1812 Mean :0.06280 Mean :0.4052
## 3rd Qu.:0.07400 3rd Qu.:0.1957 3rd Qu.:0.06612 3rd Qu.:0.4789
## Max. :0.20120 Max. :0.3040 Max. :0.09744 Max. :2.8730
## texture_se perimeter_se area_se smoothness_se
## Min. :0.3602 Min. : 0.757 Min. : 6.802 Min. :0.001713
## 1st Qu.:0.8339 1st Qu.: 1.606 1st Qu.: 17.850 1st Qu.:0.005169
## Median :1.1080 Median : 2.287 Median : 24.530 Median :0.006380
## Mean :1.2169 Mean : 2.866 Mean : 40.337 Mean :0.007041
## 3rd Qu.:1.4740 3rd Qu.: 3.357 3rd Qu.: 45.190 3rd Qu.:0.008146
## Max. :4.8850 Max. :21.980 Max. :542.200 Max. :0.031130
## compactness_se concavity_se concave_points_se symmetry_se
## Min. :0.002252 Min. :0.00000 Min. :0.000000 Min. :0.007882
## 1st Qu.:0.013080 1st Qu.:0.01509 1st Qu.:0.007638 1st Qu.:0.015160
## Median :0.020450 Median :0.02589 Median :0.010930 Median :0.018730
## Mean :0.025478 Mean :0.03189 Mean :0.011796 Mean :0.020542
## 3rd Qu.:0.032450 3rd Qu.:0.04205 3rd Qu.:0.014710 3rd Qu.:0.023480
## Max. :0.135400 Max. :0.39600 Max. :0.052790 Max. :0.078950
## fractal_dimension_se radius_worst texture_worst perimeter_worst
## Min. :0.0008948 Min. : 7.93 Min. :12.02 Min. : 50.41
## 1st Qu.:0.0022480 1st Qu.:13.01 1st Qu.:21.08 1st Qu.: 84.11
## Median :0.0031870 Median :14.97 Median :25.41 Median : 97.66
## Mean :0.0037949 Mean :16.27 Mean :25.68 Mean :107.26
## 3rd Qu.:0.0045580 3rd Qu.:18.79 3rd Qu.:29.72 3rd Qu.:125.40
## Max. :0.0298400 Max. :36.04 Max. :49.54 Max. :251.20
## area_worst smoothness_worst compactness_worst concavity_worst
## Min. : 185.2 Min. :0.07117 Min. :0.02729 Min. :0.0000
## 1st Qu.: 515.3 1st Qu.:0.11660 1st Qu.:0.14720 1st Qu.:0.1145
## Median : 686.5 Median :0.13130 Median :0.21190 Median :0.2267
## Mean : 880.6 Mean :0.13237 Mean :0.25427 Mean :0.2722
## 3rd Qu.:1084.0 3rd Qu.:0.14600 3rd Qu.:0.33910 3rd Qu.:0.3829
## Max. :4254.0 Max. :0.22260 Max. :1.05800 Max. :1.2520
## concave_points_worst symmetry_worst fractal_dimension_worst
## Min. :0.00000 Min. :0.1565 Min. :0.05504
## 1st Qu.:0.06493 1st Qu.:0.2504 1st Qu.:0.07146
## Median :0.09993 Median :0.2822 Median :0.08004
## Mean :0.11461 Mean :0.2901 Mean :0.08395
## 3rd Qu.:0.16140 3rd Qu.:0.3179 3rd Qu.:0.09208
## Max. :0.29100 Max. :0.6638 Max. :0.20750str(cancer)## spc_tbl_ [569 × 31] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ diagnosis : chr [1:569] "M" "M" "M" "M" ...
## $ radius_mean : num [1:569] 18 20.6 19.7 11.4 20.3 ...
## $ texture_mean : num [1:569] 10.4 17.8 21.2 20.4 14.3 ...
## $ perimeter_mean : num [1:569] 122.8 132.9 130 77.6 135.1 ...
## $ area_mean : num [1:569] 1001 1326 1203 386 1297 ...
## $ smoothness_mean : num [1:569] 0.1184 0.0847 0.1096 0.1425 0.1003 ...
## $ compactness_mean : num [1:569] 0.2776 0.0786 0.1599 0.2839 0.1328 ...
## $ concavity_mean : num [1:569] 0.3001 0.0869 0.1974 0.2414 0.198 ...
## $ concave_points_mean : num [1:569] 0.1471 0.0702 0.1279 0.1052 0.1043 ...
## $ symmetry_mean : num [1:569] 0.242 0.181 0.207 0.26 0.181 ...
## $ fractal_dimension_mean : num [1:569] 0.0787 0.0567 0.06 0.0974 0.0588 ...
## $ radius_se : num [1:569] 1.095 0.543 0.746 0.496 0.757 ...
## $ texture_se : num [1:569] 0.905 0.734 0.787 1.156 0.781 ...
## $ perimeter_se : num [1:569] 8.59 3.4 4.58 3.44 5.44 ...
## $ area_se : num [1:569] 153.4 74.1 94 27.2 94.4 ...
## $ smoothness_se : num [1:569] 0.0064 0.00522 0.00615 0.00911 0.01149 ...
## $ compactness_se : num [1:569] 0.049 0.0131 0.0401 0.0746 0.0246 ...
## $ concavity_se : num [1:569] 0.0537 0.0186 0.0383 0.0566 0.0569 ...
## $ concave_points_se : num [1:569] 0.0159 0.0134 0.0206 0.0187 0.0188 ...
## $ symmetry_se : num [1:569] 0.03 0.0139 0.0225 0.0596 0.0176 ...
## $ fractal_dimension_se : num [1:569] 0.00619 0.00353 0.00457 0.00921 0.00511 ...
## $ radius_worst : num [1:569] 25.4 25 23.6 14.9 22.5 ...
## $ texture_worst : num [1:569] 17.3 23.4 25.5 26.5 16.7 ...
## $ perimeter_worst : num [1:569] 184.6 158.8 152.5 98.9 152.2 ...
## $ area_worst : num [1:569] 2019 1956 1709 568 1575 ...
## $ smoothness_worst : num [1:569] 0.162 0.124 0.144 0.21 0.137 ...
## $ compactness_worst : num [1:569] 0.666 0.187 0.424 0.866 0.205 ...
## $ concavity_worst : num [1:569] 0.712 0.242 0.45 0.687 0.4 ...
## $ concave_points_worst : num [1:569] 0.265 0.186 0.243 0.258 0.163 ...
## $ symmetry_worst : num [1:569] 0.46 0.275 0.361 0.664 0.236 ...
## $ fractal_dimension_worst: num [1:569] 0.1189 0.089 0.0876 0.173 0.0768 ...
## - attr(*, "spec")=
## .. cols(
## .. diagnosis = col_character(),
## .. radius_mean = col_double(),
## .. texture_mean = col_double(),
## .. perimeter_mean = col_double(),
## .. area_mean = col_double(),
## .. smoothness_mean = col_double(),
## .. compactness_mean = col_double(),
## .. concavity_mean = col_double(),
## .. concave_points_mean = col_double(),
## .. symmetry_mean = col_double(),
## .. fractal_dimension_mean = col_double(),
## .. radius_se = col_double(),
## .. texture_se = col_double(),
## .. perimeter_se = col_double(),
## .. area_se = col_double(),
## .. smoothness_se = col_double(),
## .. compactness_se = col_double(),
## .. concavity_se = col_double(),
## .. concave_points_se = col_double(),
## .. symmetry_se = col_double(),
## .. fractal_dimension_se = col_double(),
## .. radius_worst = col_double(),
## .. texture_worst = col_double(),
## .. perimeter_worst = col_double(),
## .. area_worst = col_double(),
## .. smoothness_worst = col_double(),
## .. compactness_worst = col_double(),
## .. concavity_worst = col_double(),
## .. concave_points_worst = col_double(),
## .. symmetry_worst = col_double(),
## .. fractal_dimension_worst = col_double()
## .. )
## - attr(*, "problems")=<externalptr>head(cancer)## # A tibble: 6 × 31
## diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 M 18.0 10.4 123. 1001 0.118
## 2 M 20.6 17.8 133. 1326 0.0847
## 3 M 19.7 21.2 130 1203 0.110
## 4 M 11.4 20.4 77.6 386. 0.142
## 5 M 20.3 14.3 135. 1297 0.100
## 6 M 12.4 15.7 82.6 477. 0.128
## # ℹ 25 more variables: compactness_mean <dbl>, concavity_mean <dbl>,
## # concave_points_mean <dbl>, symmetry_mean <dbl>,
## # fractal_dimension_mean <dbl>, radius_se <dbl>, texture_se <dbl>,
## # perimeter_se <dbl>, area_se <dbl>, smoothness_se <dbl>,
## # compactness_se <dbl>, concavity_se <dbl>, concave_points_se <dbl>,
## # symmetry_se <dbl>, fractal_dimension_se <dbl>, radius_worst <dbl>,
## # texture_worst <dbl>, perimeter_worst <dbl>, area_worst <dbl>, …Árbol de decisiones
cancer$texture_worst <- as.numeric(as.character(cancer$texture_worst))
cancer$radius_worst <- as.numeric(as.character(cancer$radius_worst))
arbol_cancer <- rpart(diagnosis ~ ., data = cancer)
rpart.plot(arbol_cancer)
prp(arbol_cancer, extra = 7, prefix = "fraccion\n")
Conclusiones
Los pacientes que presentan un valor de radius_worst mayor o igual a 17 tienen mayores probabilidades de tener un tumor maligno(94%).
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