Redes Neuronales
Emilio Martínez - A01177730
2024-02-22
Una red neuronal artificial (ANN) modela la relación entre un conjunto de entradas y una salida, resolviendo un problema de aprendizaje.
Algunos ejemplos de aplicación de ANN son:
- La recomendación de contenido de Netflix.
- El feed de Instagram.
- Determinar el número escrito a mano.
Ejercicio 1. ¿Pasé la materia?
1. Instalar paquetes y llamar librerías
# install.packages("neuralnet")
library("neuralnet")## Warning: package 'neuralnet' was built under R version 4.3.22. Obtener datos
examen <- c(20,10,30,20,80,30)
proyecto <- c(90,20,40,50,50,80)
estatus <- c(1,0,0,0,0,1)
df1 <- data.frame(examen, proyecto, estatus)3. Crear Red Neuronal
set.seed(123)
rn1 <- neuralnet(estatus ~., data = df1)
plot(rn1, rep = "best")
## 4. Predecir Resultados Futuros
# Importante que las columnas estén colocadas en las mismas posiciones
prueba_examen <- c(30,40,85)
prueba_proyecto <- c(85,50,40)
prueba1 <- data.frame(prueba_examen,prueba_proyecto)prediccion <- compute(rn1, prueba1)
probabilidad <- prediccion$net.result
resultado <-- ifelse(probabilidad>0.5, 1, 0)
resultado## [,1]
## [1,] -1
## [2,] 0
## [3,] 0Ejercicio 2 Cancer de Mama
1. Instalar paquetes y llamar librerías
#install.packages("neuralnet")
library("neuralnet")
library(readxl)## Warning: package 'readxl' was built under R version 4.3.12. Obtener datos
df2 <- read_excel("C:\\Users\\memil\\OneDrive\\Desktop\\aaTecDeMonterrey\\6to Semestre\\cancer_de_mama.xlsx")
df2$diagnosis <- ifelse(df2$diagnosis == "M", 1, 0)3. Crear Red Neuronal
set.seed(123)
rn2 <- neuralnet(diagnosis ~., data = df2)
plot(rn2, rep = "best")
summary(df2)## diagnosis radius_mean texture_mean perimeter_mean
## Min. :0.0000 Min. : 6.981 Min. : 9.71 Min. : 43.79
## 1st Qu.:0.0000 1st Qu.:11.700 1st Qu.:16.17 1st Qu.: 75.17
## Median :0.0000 Median :13.370 Median :18.84 Median : 86.24
## Mean :0.3726 Mean :14.127 Mean :19.29 Mean : 91.97
## 3rd Qu.:1.0000 3rd Qu.:15.780 3rd Qu.:21.80 3rd Qu.:104.10
## Max. :1.0000 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. :0.207504. Predecir Resultados Futuros
# Importante que las columnas estén colocadas en las mismas posiciones
prueba_examen <- c(30,40,85)
prueba_proyecto <- c(85,50,40)
prueba1 <- data.frame(prueba_examen,prueba_proyecto)prediccion <- compute(rn1, prueba1)
probabilidad <- prediccion$net.result
resultado <-- ifelse(probabilidad>0.5, 1, 0)
resultado## [,1]
## [1,] -1
## [2,] 0
## [3,] 0