CARET
Adrian Morales A01722532
2025-08-22
Teoria
El paquete CARET (Classification And Regression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automatico.
Instalar paquetes y cargar librerias
#install.packages("ggplot2") # Gráficas
library(ggplot2)
#install.packages("lattice") # Crear gráficos
library(lattice)
#install.packages ("caret") # Algoritmos de aprendizaje automático
library (caret)
#install.packages ("datasets") # Usar bases de datos, en este caso Iris
library(datasets)
#install.packages ("DataExplorer") # Análisis Exploratorio
library (DataExplorer)
#install.packages("kernlab")
library(kernlab)##
## Attaching package: 'kernlab'## The following object is masked from 'package:ggplot2':
##
## alphaCrear la base de datos
df <- data.frame(iris)Entender la base de datos
summary(df)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## str(df)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...plot_missing(df)
plot_histogram(df)
plot_correlation(df)
Partir la base de datos
# Normalmente 80-20
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$Species, p=0.8, list = FALSE)
entrenamiento <- iris[renglones_entrenamiento, ]
prueba <- iris[-renglones_entrenamiento, ]Distintos tipos de métodos para Modelar
Los métodos más utilizados para modelar aprendizaje automático:
SVM: Support Vector Machine o Máquina de Vectores de Soporte. Hay varios subtipos: Lineal (svmLinear), Radial (svmRadial), Polinómico (svmPoly), etc.
Árbol de Decisión: rpart
Redes Neuronales: nnet
Random Forest o Bosques Aleatorios: rf
Modelo 1. SVM Lineal
modelo1 <- train(Species ~ ., data=entrenamiento,
method = "svmLinear", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(C=1) #Cambiar
)
resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)
#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.
#Matriz de confusion del resultado del entrenamiento
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$Species)
mcre1## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 0
## virginica 0 1 40
##
## Overall Statistics
##
## Accuracy : 0.9917
## 95% CI : (0.9544, 0.9998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9875
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 1.0000
## Specificity 1.0000 1.0000 0.9875
## Pos Pred Value 1.0000 1.0000 0.9756
## Neg Pred Value 1.0000 0.9877 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3333
## Detection Prevalence 0.3333 0.3250 0.3417
## Balanced Accuracy 1.0000 0.9875 0.9938# Matriz de confusion del resultado de la prueba
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$Species)
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 1
## virginica 0 0 9
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.9000
## Specificity 1.0000 0.9500 1.0000
## Pos Pred Value 1.0000 0.9091 1.0000
## Neg Pred Value 1.0000 1.0000 0.9524
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3000
## Detection Prevalence 0.3333 0.3667 0.3000
## Balanced Accuracy 1.0000 0.9750 0.9500Modelo 2. SVM Radial
modelo2 <- train(Species ~ ., data=entrenamiento,
method = "svmRadial", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(sigma=1, C=1) #Cambiar
)
resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)
#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.
#Matriz de confusion del resultado del entrenamiento
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$Species)
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 0
## virginica 0 1 40
##
## Overall Statistics
##
## Accuracy : 0.9917
## 95% CI : (0.9544, 0.9998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9875
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 1.0000
## Specificity 1.0000 1.0000 0.9875
## Pos Pred Value 1.0000 1.0000 0.9756
## Neg Pred Value 1.0000 0.9877 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3333
## Detection Prevalence 0.3333 0.3250 0.3417
## Balanced Accuracy 1.0000 0.9875 0.9938# Matriz de confusion del resultado de la prueba
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$Species)
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.9000Modelo 3. SVM Poly
modelo3 <- train(Species ~ ., data=entrenamiento,
method = "svmRadial", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(degree=1, C=1, scale=1) #Cambiar
)
resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)
#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.
#Matriz de confusion del resultado del entrenamiento
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$Species)
mcre3## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 38 0
## virginica 0 2 40
##
## Overall Statistics
##
## Accuracy : 0.9833
## 95% CI : (0.9411, 0.998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.975
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9500 1.0000
## Specificity 1.0000 1.0000 0.9750
## Pos Pred Value 1.0000 1.0000 0.9524
## Neg Pred Value 1.0000 0.9756 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3167 0.3333
## Detection Prevalence 0.3333 0.3167 0.3500
## Balanced Accuracy 1.0000 0.9750 0.9875# Matriz de confusion del resultado de la prueba
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$Species)
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.9000Modelo 4. Arboles de decision
modelo4 <- train(Species ~ ., data=entrenamiento,
method = "rpart", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneLength = 10
)
resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo3, prueba)
#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.
#Matriz de confusion del resultado del entrenamiento
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$Species)
mcre4## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 3
## virginica 0 1 37
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.9169, 0.9908)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 0.9250
## Specificity 1.0000 0.9625 0.9875
## Pos Pred Value 1.0000 0.9286 0.9737
## Neg Pred Value 1.0000 0.9872 0.9634
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3083
## Detection Prevalence 0.3333 0.3500 0.3167
## Balanced Accuracy 1.0000 0.9688 0.9563# Matriz de confusion del resultado de la prueba
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$Species)
mcrp4## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.9000Modelo 5. Redes Neuronales
modelo5 <- train(Species ~ ., data=entrenamiento,
method = "nnet", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10)
)## # weights: 11
## initial value 119.364237
## iter 10 value 50.064888
## iter 20 value 48.670264
## iter 30 value 47.831568
## iter 40 value 47.729855
## iter 50 value 47.630183
## iter 60 value 46.093819
## iter 70 value 43.722824
## iter 80 value 18.376958
## iter 90 value 2.742274
## iter 100 value 1.801628
## final value 1.801628
## stopped after 100 iterations
## # weights: 27
## initial value 123.793414
## iter 10 value 5.868269
## iter 20 value 0.081242
## iter 30 value 0.000303
## final value 0.000076
## converged
## # weights: 43
## initial value 122.731863
## iter 10 value 3.624311
## iter 20 value 0.010911
## final value 0.000055
## converged
## # weights: 11
## initial value 118.612128
## iter 10 value 59.262402
## iter 20 value 44.973063
## iter 30 value 43.405883
## final value 43.399628
## converged
## # weights: 27
## initial value 135.631812
## iter 10 value 26.153324
## iter 20 value 19.519935
## iter 30 value 19.159834
## iter 40 value 19.141040
## final value 19.140707
## converged
## # weights: 43
## initial value 121.810346
## iter 10 value 25.282006
## iter 20 value 18.168535
## iter 30 value 18.106805
## iter 40 value 18.042974
## iter 50 value 17.658982
## iter 60 value 17.547503
## iter 70 value 17.312348
## iter 80 value 17.282247
## iter 90 value 17.277857
## iter 100 value 17.277302
## final value 17.277302
## stopped after 100 iterations
## # weights: 11
## initial value 133.417421
## iter 10 value 50.157026
## iter 20 value 49.977347
## iter 30 value 49.964410
## iter 40 value 48.859866
## iter 50 value 31.567030
## iter 60 value 7.857236
## iter 70 value 2.148236
## iter 80 value 2.065323
## iter 90 value 2.039104
## iter 100 value 1.999937
## final value 1.999937
## stopped after 100 iterations
## # weights: 27
## initial value 110.745240
## iter 10 value 3.752499
## iter 20 value 0.193975
## iter 30 value 0.185500
## iter 40 value 0.176438
## iter 50 value 0.167373
## iter 60 value 0.164644
## iter 70 value 0.162408
## iter 80 value 0.159110
## iter 90 value 0.155530
## iter 100 value 0.152384
## final value 0.152384
## stopped after 100 iterations
## # weights: 43
## initial value 116.918727
## iter 10 value 3.580954
## iter 20 value 0.259672
## iter 30 value 0.237494
## iter 40 value 0.221850
## iter 50 value 0.183126
## iter 60 value 0.174202
## iter 70 value 0.158959
## iter 80 value 0.145791
## iter 90 value 0.141314
## iter 100 value 0.131996
## final value 0.131996
## stopped after 100 iterations
## # weights: 11
## initial value 131.916635
## iter 10 value 52.434019
## iter 20 value 49.536656
## iter 30 value 48.762687
## iter 40 value 48.424392
## iter 50 value 47.459406
## iter 60 value 36.638325
## iter 70 value 7.255328
## iter 80 value 3.432719
## iter 90 value 3.028823
## iter 100 value 2.715973
## final value 2.715973
## stopped after 100 iterations
## # weights: 27
## initial value 116.560224
## iter 10 value 22.377209
## iter 20 value 1.434404
## iter 30 value 0.015301
## final value 0.000063
## converged
## # weights: 43
## initial value 118.392692
## iter 10 value 3.944569
## iter 20 value 0.306409
## iter 30 value 0.004517
## iter 40 value 0.000251
## final value 0.000083
## converged
## # weights: 11
## initial value 118.460051
## iter 10 value 64.880183
## iter 20 value 50.068934
## iter 30 value 43.488436
## final value 43.487537
## converged
## # weights: 27
## initial value 126.586947
## iter 10 value 43.279225
## iter 20 value 21.224741
## iter 30 value 20.749248
## iter 40 value 20.749048
## final value 20.749047
## converged
## # weights: 43
## initial value 151.937613
## iter 10 value 23.734242
## iter 20 value 18.753404
## iter 30 value 18.118863
## iter 40 value 17.940374
## iter 50 value 17.930575
## iter 60 value 17.929988
## iter 70 value 17.929922
## final value 17.929921
## converged
## # weights: 11
## initial value 132.070198
## iter 10 value 52.355646
## iter 20 value 48.275663
## iter 30 value 18.372238
## iter 40 value 3.649689
## iter 50 value 3.406804
## iter 60 value 3.276053
## iter 70 value 3.273048
## iter 80 value 3.268892
## iter 90 value 3.259668
## iter 100 value 3.258851
## final value 3.258851
## stopped after 100 iterations
## # weights: 27
## initial value 113.529901
## iter 10 value 6.241670
## iter 20 value 1.208916
## iter 30 value 0.397811
## iter 40 value 0.386273
## iter 50 value 0.368184
## iter 60 value 0.361141
## iter 70 value 0.359369
## iter 80 value 0.350759
## iter 90 value 0.347615
## iter 100 value 0.342804
## final value 0.342804
## stopped after 100 iterations
## # weights: 43
## initial value 111.717272
## iter 10 value 3.416090
## iter 20 value 0.377952
## iter 30 value 0.317075
## iter 40 value 0.312838
## iter 50 value 0.309274
## iter 60 value 0.303265
## iter 70 value 0.297596
## iter 80 value 0.295684
## iter 90 value 0.294852
## iter 100 value 0.289916
## final value 0.289916
## stopped after 100 iterations
## # weights: 11
## initial value 128.608870
## iter 10 value 27.776794
## iter 20 value 5.087471
## iter 30 value 3.563326
## iter 40 value 3.394366
## iter 50 value 3.217410
## iter 60 value 2.980093
## iter 70 value 2.883858
## iter 80 value 2.838704
## iter 90 value 2.675153
## iter 100 value 2.610229
## final value 2.610229
## stopped after 100 iterations
## # weights: 27
## initial value 118.190633
## iter 10 value 13.123265
## iter 20 value 1.221350
## iter 30 value 0.000379
## final value 0.000081
## converged
## # weights: 43
## initial value 137.653895
## iter 10 value 5.426639
## iter 20 value 0.178492
## iter 30 value 0.000237
## final value 0.000096
## converged
## # weights: 11
## initial value 119.446713
## iter 10 value 64.603563
## iter 20 value 43.882482
## iter 30 value 43.662641
## final value 43.660320
## converged
## # weights: 27
## initial value 121.223486
## iter 10 value 42.509333
## iter 20 value 21.539342
## iter 30 value 20.363180
## iter 40 value 20.229352
## iter 50 value 20.224014
## final value 20.223979
## converged
## # weights: 43
## initial value 148.735730
## iter 10 value 24.147018
## iter 20 value 19.161635
## iter 30 value 19.000060
## iter 40 value 18.956075
## iter 50 value 18.954353
## final value 18.954194
## converged
## # weights: 11
## initial value 122.206168
## iter 10 value 28.418454
## iter 20 value 5.277740
## iter 30 value 4.113257
## iter 40 value 4.004701
## iter 50 value 3.877920
## iter 60 value 3.872033
## iter 70 value 3.871948
## iter 80 value 3.871938
## final value 3.871933
## converged
## # weights: 27
## initial value 119.896820
## iter 10 value 9.755985
## iter 20 value 2.721336
## iter 30 value 0.781265
## iter 40 value 0.654267
## iter 50 value 0.538525
## iter 60 value 0.490774
## iter 70 value 0.486090
## iter 80 value 0.484447
## iter 90 value 0.482461
## iter 100 value 0.479810
## final value 0.479810
## stopped after 100 iterations
## # weights: 43
## initial value 132.364707
## iter 10 value 18.212595
## iter 20 value 2.692662
## iter 30 value 0.488049
## iter 40 value 0.427922
## iter 50 value 0.379209
## iter 60 value 0.335707
## iter 70 value 0.324587
## iter 80 value 0.312451
## iter 90 value 0.296794
## iter 100 value 0.283337
## final value 0.283337
## stopped after 100 iterations
## # weights: 11
## initial value 114.770789
## iter 10 value 50.895915
## iter 20 value 21.594661
## iter 30 value 8.579404
## iter 40 value 4.491888
## iter 50 value 1.888615
## iter 60 value 1.774330
## iter 70 value 1.435249
## iter 80 value 1.417706
## iter 90 value 1.274634
## iter 100 value 1.239344
## final value 1.239344
## stopped after 100 iterations
## # weights: 27
## initial value 121.671018
## iter 10 value 7.637314
## iter 20 value 1.023347
## iter 30 value 0.000176
## iter 30 value 0.000084
## iter 30 value 0.000084
## final value 0.000084
## converged
## # weights: 43
## initial value 125.122579
## iter 10 value 4.878708
## iter 20 value 0.082765
## iter 30 value 0.000501
## final value 0.000065
## converged
## # weights: 11
## initial value 118.540638
## iter 10 value 55.901291
## iter 20 value 44.190846
## iter 30 value 44.122993
## final value 44.122341
## converged
## # weights: 27
## initial value 128.560755
## iter 10 value 31.362040
## iter 20 value 21.703871
## iter 30 value 21.365892
## iter 40 value 21.277271
## final value 21.274727
## converged
## # weights: 43
## initial value 135.332676
## iter 10 value 36.608914
## iter 20 value 20.428722
## iter 30 value 18.841831
## iter 40 value 18.538820
## iter 50 value 18.465358
## iter 60 value 18.457341
## iter 70 value 18.457287
## iter 70 value 18.457287
## iter 70 value 18.457287
## final value 18.457287
## converged
## # weights: 11
## initial value 122.464598
## iter 10 value 48.486860
## iter 20 value 27.244665
## iter 30 value 7.563582
## iter 40 value 4.541484
## iter 50 value 4.194044
## iter 60 value 4.061244
## iter 70 value 3.913031
## iter 80 value 3.888366
## iter 90 value 3.865349
## iter 100 value 3.862664
## final value 3.862664
## stopped after 100 iterations
## # weights: 27
## initial value 109.388538
## iter 10 value 4.747327
## iter 20 value 0.751945
## iter 30 value 0.726463
## iter 40 value 0.654058
## iter 50 value 0.591235
## iter 60 value 0.564247
## iter 70 value 0.544641
## iter 80 value 0.507726
## iter 90 value 0.474908
## iter 100 value 0.453801
## final value 0.453801
## stopped after 100 iterations
## # weights: 43
## initial value 123.169841
## iter 10 value 4.382165
## iter 20 value 1.563711
## iter 30 value 0.479811
## iter 40 value 0.448049
## iter 50 value 0.432482
## iter 60 value 0.398597
## iter 70 value 0.388515
## iter 80 value 0.377880
## iter 90 value 0.370443
## iter 100 value 0.361109
## final value 0.361109
## stopped after 100 iterations
## # weights: 11
## initial value 119.256445
## iter 10 value 56.285210
## iter 20 value 7.469969
## iter 30 value 4.099817
## iter 40 value 3.635803
## iter 50 value 3.208790
## iter 60 value 2.977949
## iter 70 value 2.893926
## iter 80 value 2.838948
## iter 90 value 2.732318
## iter 100 value 2.619618
## final value 2.619618
## stopped after 100 iterations
## # weights: 27
## initial value 114.122617
## iter 10 value 5.558564
## iter 20 value 0.243031
## iter 30 value 0.000334
## final value 0.000072
## converged
## # weights: 43
## initial value 143.357526
## iter 10 value 5.815688
## iter 20 value 1.869046
## iter 30 value 0.005197
## final value 0.000051
## converged
## # weights: 11
## initial value 121.113960
## iter 10 value 74.640372
## iter 20 value 46.951522
## iter 30 value 43.380961
## final value 43.380924
## converged
## # weights: 27
## initial value 121.861199
## iter 10 value 35.206448
## iter 20 value 20.961318
## iter 30 value 19.985500
## iter 40 value 19.932686
## iter 50 value 19.922197
## iter 60 value 19.921722
## final value 19.921712
## converged
## # weights: 43
## initial value 131.939376
## iter 10 value 25.702828
## iter 20 value 18.608282
## iter 30 value 18.122011
## iter 40 value 18.053090
## iter 50 value 18.039842
## iter 60 value 18.038080
## final value 18.038075
## converged
## # weights: 11
## initial value 117.982337
## iter 10 value 51.103593
## iter 20 value 48.035321
## iter 30 value 33.273056
## iter 40 value 8.919168
## iter 50 value 4.838973
## iter 60 value 4.530733
## iter 70 value 4.028449
## iter 80 value 3.905338
## iter 90 value 3.870748
## iter 100 value 3.864652
## final value 3.864652
## stopped after 100 iterations
## # weights: 27
## initial value 144.068507
## iter 10 value 4.404914
## iter 20 value 1.124010
## iter 30 value 0.871467
## iter 40 value 0.719849
## iter 50 value 0.601527
## iter 60 value 0.575084
## iter 70 value 0.560945
## iter 80 value 0.510113
## iter 90 value 0.486598
## iter 100 value 0.474407
## final value 0.474407
## stopped after 100 iterations
## # weights: 43
## initial value 140.508226
## iter 10 value 9.593176
## iter 20 value 0.781330
## iter 30 value 0.461086
## iter 40 value 0.445335
## iter 50 value 0.411271
## iter 60 value 0.390060
## iter 70 value 0.372303
## iter 80 value 0.363494
## iter 90 value 0.357105
## iter 100 value 0.344014
## final value 0.344014
## stopped after 100 iterations
## # weights: 11
## initial value 129.031039
## iter 10 value 61.978707
## iter 20 value 58.008696
## iter 30 value 57.768938
## iter 40 value 55.583867
## iter 50 value 52.889223
## iter 60 value 43.759611
## iter 70 value 39.263004
## iter 80 value 37.489744
## iter 90 value 23.536776
## iter 100 value 4.035245
## final value 4.035245
## stopped after 100 iterations
## # weights: 27
## initial value 126.475926
## iter 10 value 7.096031
## iter 20 value 0.639765
## iter 30 value 0.000106
## iter 30 value 0.000052
## iter 30 value 0.000052
## final value 0.000052
## converged
## # weights: 43
## initial value 134.270158
## iter 10 value 5.930885
## iter 20 value 1.228181
## iter 30 value 0.010612
## final value 0.000084
## converged
## # weights: 11
## initial value 123.227860
## iter 10 value 53.165906
## iter 20 value 43.229062
## final value 43.228624
## converged
## # weights: 27
## initial value 127.334763
## iter 10 value 24.083708
## iter 20 value 19.538749
## iter 30 value 19.295874
## iter 40 value 19.174203
## iter 50 value 19.034503
## final value 19.034362
## converged
## # weights: 43
## initial value 131.621100
## iter 10 value 35.666079
## iter 20 value 19.486685
## iter 30 value 18.420880
## iter 40 value 17.192733
## iter 50 value 17.154065
## iter 60 value 17.148652
## iter 70 value 17.148346
## final value 17.148327
## converged
## # weights: 11
## initial value 128.213413
## iter 10 value 49.978543
## iter 20 value 48.871330
## iter 30 value 48.360713
## iter 40 value 44.556051
## iter 50 value 17.898217
## iter 60 value 4.580083
## iter 70 value 3.877043
## iter 80 value 3.204017
## iter 90 value 3.135699
## iter 100 value 2.976457
## final value 2.976457
## stopped after 100 iterations
## # weights: 27
## initial value 127.197897
## iter 10 value 4.354141
## iter 20 value 1.009145
## iter 30 value 0.462364
## iter 40 value 0.402815
## iter 50 value 0.365000
## iter 60 value 0.341482
## iter 70 value 0.333382
## iter 80 value 0.324477
## iter 90 value 0.314822
## iter 100 value 0.295699
## final value 0.295699
## stopped after 100 iterations
## # weights: 43
## initial value 120.457941
## iter 10 value 4.476405
## iter 20 value 1.443496
## iter 30 value 0.600594
## iter 40 value 0.498873
## iter 50 value 0.401362
## iter 60 value 0.376749
## iter 70 value 0.357924
## iter 80 value 0.348783
## iter 90 value 0.331318
## iter 100 value 0.326491
## final value 0.326491
## stopped after 100 iterations
## # weights: 11
## initial value 135.093527
## iter 10 value 21.860640
## iter 20 value 3.813716
## iter 30 value 2.509928
## iter 40 value 2.384751
## iter 50 value 2.276547
## iter 60 value 2.250532
## iter 70 value 2.153859
## iter 80 value 1.699918
## iter 90 value 0.834569
## iter 100 value 0.573835
## final value 0.573835
## stopped after 100 iterations
## # weights: 27
## initial value 126.256021
## iter 10 value 5.294353
## iter 20 value 0.037650
## final value 0.000071
## converged
## # weights: 43
## initial value 130.459188
## iter 10 value 5.484744
## iter 20 value 0.810428
## iter 30 value 0.002412
## final value 0.000073
## converged
## # weights: 11
## initial value 127.596687
## iter 10 value 53.593172
## iter 20 value 44.128618
## iter 30 value 43.966078
## final value 43.966037
## converged
## # weights: 27
## initial value 155.037982
## iter 10 value 27.774198
## iter 20 value 20.783237
## iter 30 value 19.876683
## iter 40 value 19.868884
## final value 19.868385
## converged
## # weights: 43
## initial value 171.725834
## iter 10 value 23.134324
## iter 20 value 18.599155
## iter 30 value 18.436512
## iter 40 value 18.422714
## iter 50 value 18.422201
## final value 18.422189
## converged
## # weights: 11
## initial value 130.488537
## iter 10 value 85.816017
## iter 20 value 50.748640
## iter 30 value 48.927856
## iter 40 value 47.709677
## iter 50 value 47.582838
## iter 60 value 47.519960
## iter 70 value 47.507163
## iter 80 value 47.501926
## iter 90 value 47.497119
## final value 47.494823
## converged
## # weights: 27
## initial value 122.543665
## iter 10 value 19.518466
## iter 20 value 2.052800
## iter 30 value 0.886034
## iter 40 value 0.804745
## iter 50 value 0.648603
## iter 60 value 0.585834
## iter 70 value 0.553642
## iter 80 value 0.497885
## iter 90 value 0.458749
## iter 100 value 0.449044
## final value 0.449044
## stopped after 100 iterations
## # weights: 43
## initial value 153.095425
## iter 10 value 7.901518
## iter 20 value 0.690893
## iter 30 value 0.636107
## iter 40 value 0.584777
## iter 50 value 0.567513
## iter 60 value 0.534749
## iter 70 value 0.522132
## iter 80 value 0.506614
## iter 90 value 0.501144
## iter 100 value 0.474534
## final value 0.474534
## stopped after 100 iterations
## # weights: 11
## initial value 135.364864
## iter 10 value 59.004600
## iter 20 value 49.914439
## iter 30 value 49.908413
## final value 49.906914
## converged
## # weights: 27
## initial value 128.151297
## iter 10 value 6.337487
## iter 20 value 0.146259
## iter 30 value 0.000104
## iter 30 value 0.000057
## iter 30 value 0.000056
## final value 0.000056
## converged
## # weights: 43
## initial value 139.035430
## iter 10 value 5.342379
## iter 20 value 0.250943
## iter 30 value 0.000708
## final value 0.000094
## converged
## # weights: 11
## initial value 118.420609
## iter 10 value 64.974347
## iter 20 value 56.804050
## iter 30 value 44.067872
## final value 42.994871
## converged
## # weights: 27
## initial value 136.089534
## iter 10 value 27.295517
## iter 20 value 19.795109
## iter 30 value 18.797967
## iter 40 value 18.724373
## iter 50 value 18.718133
## final value 18.718133
## converged
## # weights: 43
## initial value 117.387025
## iter 10 value 26.556247
## iter 20 value 17.899514
## iter 30 value 17.809471
## iter 40 value 17.764813
## iter 50 value 17.756615
## final value 17.756610
## converged
## # weights: 11
## initial value 136.528004
## iter 10 value 23.039236
## iter 20 value 3.818892
## iter 30 value 3.195511
## iter 40 value 3.122077
## iter 50 value 3.106837
## iter 60 value 3.094371
## iter 70 value 3.093128
## iter 80 value 3.092464
## iter 90 value 3.092407
## iter 100 value 3.092292
## final value 3.092292
## stopped after 100 iterations
## # weights: 27
## initial value 123.626570
## iter 10 value 5.162502
## iter 20 value 0.512650
## iter 30 value 0.387310
## iter 40 value 0.363980
## iter 50 value 0.342939
## iter 60 value 0.322494
## iter 70 value 0.309017
## iter 80 value 0.303599
## iter 90 value 0.292640
## iter 100 value 0.274828
## final value 0.274828
## stopped after 100 iterations
## # weights: 43
## initial value 129.709743
## iter 10 value 6.399049
## iter 20 value 1.484154
## iter 30 value 0.424066
## iter 40 value 0.333323
## iter 50 value 0.290299
## iter 60 value 0.271136
## iter 70 value 0.254488
## iter 80 value 0.244931
## iter 90 value 0.241006
## iter 100 value 0.235645
## final value 0.235645
## stopped after 100 iterations
## # weights: 11
## initial value 120.630519
## iter 10 value 59.934734
## iter 20 value 50.061401
## iter 30 value 48.902842
## iter 40 value 40.689201
## iter 50 value 9.246081
## iter 60 value 4.798559
## iter 70 value 4.402083
## iter 80 value 3.747997
## iter 90 value 2.052315
## iter 100 value 1.813680
## final value 1.813680
## stopped after 100 iterations
## # weights: 27
## initial value 115.288383
## iter 10 value 17.230857
## iter 20 value 0.332887
## iter 30 value 0.000452
## final value 0.000047
## converged
## # weights: 43
## initial value 115.350758
## iter 10 value 15.083811
## iter 20 value 1.751399
## iter 30 value 0.182284
## iter 40 value 0.001019
## iter 50 value 0.000302
## final value 0.000086
## converged
## # weights: 11
## initial value 127.529840
## iter 10 value 55.578483
## iter 20 value 44.112002
## final value 44.104970
## converged
## # weights: 27
## initial value 147.959026
## iter 10 value 29.305877
## iter 20 value 20.335408
## iter 30 value 20.112010
## iter 40 value 20.104860
## final value 20.104627
## converged
## # weights: 43
## initial value 131.392852
## iter 10 value 32.272199
## iter 20 value 19.766570
## iter 30 value 18.836523
## iter 40 value 18.443541
## iter 50 value 18.340096
## iter 60 value 18.328809
## iter 70 value 18.327322
## final value 18.327123
## converged
## # weights: 11
## initial value 125.205767
## iter 10 value 48.380736
## iter 20 value 35.618174
## iter 30 value 11.397083
## iter 40 value 4.475791
## iter 50 value 4.074765
## iter 60 value 4.016958
## iter 70 value 3.974500
## iter 80 value 3.912831
## iter 90 value 3.848654
## iter 100 value 3.844326
## final value 3.844326
## stopped after 100 iterations
## # weights: 27
## initial value 128.588080
## iter 10 value 5.384040
## iter 20 value 1.619297
## iter 30 value 0.882242
## iter 40 value 0.851849
## iter 50 value 0.641502
## iter 60 value 0.619737
## iter 70 value 0.595281
## iter 80 value 0.535622
## iter 90 value 0.493317
## iter 100 value 0.486438
## final value 0.486438
## stopped after 100 iterations
## # weights: 43
## initial value 126.933423
## iter 10 value 11.502520
## iter 20 value 2.523881
## iter 30 value 0.724044
## iter 40 value 0.650789
## iter 50 value 0.604091
## iter 60 value 0.567950
## iter 70 value 0.545173
## iter 80 value 0.515780
## iter 90 value 0.502725
## iter 100 value 0.469805
## final value 0.469805
## stopped after 100 iterations
## # weights: 11
## initial value 128.841813
## iter 10 value 49.888557
## iter 20 value 48.854861
## iter 30 value 46.284065
## iter 40 value 45.933053
## iter 50 value 44.949410
## iter 60 value 42.929579
## iter 70 value 8.036083
## iter 80 value 4.112127
## iter 90 value 3.437175
## iter 100 value 1.621614
## final value 1.621614
## stopped after 100 iterations
## # weights: 27
## initial value 155.763709
## iter 10 value 22.044760
## iter 20 value 3.180958
## iter 30 value 0.258484
## iter 40 value 0.001788
## final value 0.000094
## converged
## # weights: 43
## initial value 122.457708
## iter 10 value 3.485470
## iter 20 value 0.019486
## final value 0.000064
## converged
## # weights: 11
## initial value 115.895686
## iter 10 value 59.149623
## iter 20 value 51.309942
## iter 30 value 43.719794
## final value 43.715117
## converged
## # weights: 27
## initial value 114.668249
## iter 10 value 28.092323
## iter 20 value 20.979628
## iter 30 value 20.625230
## iter 40 value 20.624005
## iter 40 value 20.624005
## iter 40 value 20.624005
## final value 20.624005
## converged
## # weights: 43
## initial value 129.863362
## iter 10 value 27.825492
## iter 20 value 19.487986
## iter 30 value 18.594719
## iter 40 value 18.496859
## iter 50 value 18.474010
## iter 60 value 18.420539
## iter 70 value 18.058704
## iter 80 value 17.803440
## iter 90 value 17.708693
## iter 100 value 17.696027
## final value 17.696027
## stopped after 100 iterations
## # weights: 11
## initial value 129.196853
## iter 10 value 49.961302
## iter 20 value 49.655075
## iter 30 value 48.919385
## iter 40 value 16.000681
## iter 50 value 5.572172
## iter 60 value 3.368035
## iter 70 value 3.145750
## iter 80 value 2.993983
## iter 90 value 2.943685
## iter 100 value 2.929524
## final value 2.929524
## stopped after 100 iterations
## # weights: 27
## initial value 136.352067
## iter 10 value 19.746721
## iter 20 value 1.762322
## iter 30 value 0.773155
## iter 40 value 0.739820
## iter 50 value 0.683111
## iter 60 value 0.531832
## iter 70 value 0.499564
## iter 80 value 0.416175
## iter 90 value 0.395498
## iter 100 value 0.327184
## final value 0.327184
## stopped after 100 iterations
## # weights: 43
## initial value 130.762361
## iter 10 value 7.155063
## iter 20 value 0.314087
## iter 30 value 0.295210
## iter 40 value 0.287887
## iter 50 value 0.264294
## iter 60 value 0.251011
## iter 70 value 0.228303
## iter 80 value 0.207026
## iter 90 value 0.200442
## iter 100 value 0.194906
## final value 0.194906
## stopped after 100 iterations
## # weights: 11
## initial value 145.900216
## iter 10 value 64.757220
## iter 20 value 48.348164
## iter 30 value 46.796576
## final value 46.796573
## convergedresultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.
#Matriz de confusion del resultado del entrenamiento
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$Species)
mcre5## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 36 0
## virginica 0 4 40
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.9169, 0.9908)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9000 1.0000
## Specificity 1.0000 1.0000 0.9500
## Pos Pred Value 1.0000 1.0000 0.9091
## Neg Pred Value 1.0000 0.9524 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3000 0.3333
## Detection Prevalence 0.3333 0.3000 0.3667
## Balanced Accuracy 1.0000 0.9500 0.9750# Matriz de confusion del resultado de la prueba
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$Species)
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 9 0
## virginica 0 1 10
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9000 1.0000
## Specificity 1.0000 1.0000 0.9500
## Pos Pred Value 1.0000 1.0000 0.9091
## Neg Pred Value 1.0000 0.9524 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3000 0.3333
## Detection Prevalence 0.3333 0.3000 0.3667
## Balanced Accuracy 1.0000 0.9500 0.9750Modelo 6. Random Forest
modelo6 <- train(Species ~ ., data=entrenamiento,
method = "rf", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = expand.grid(mtry = c(2,4,6))
)## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid rangeresultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)
#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.
#Matriz de confusion del resultado del entrenamiento
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$Species)
mcre6## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 40 0
## virginica 0 0 40
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9697, 1)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 1.0000
## Specificity 1.0000 1.0000 1.0000
## Pos Pred Value 1.0000 1.0000 1.0000
## Neg Pred Value 1.0000 1.0000 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3333
## Detection Prevalence 0.3333 0.3333 0.3333
## Balanced Accuracy 1.0000 1.0000 1.0000# Matriz de confusion del resultado de la prueba
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$Species)
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.9000Resultados
resultados <- data.frame(
"svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
"svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"rf" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisón de entrenamiento", "Precisión de Prueba")
resultados## svmLinear svmRadial svmPoly rpart nnet
## Precisón de entrenamiento 0.9916667 0.9916667 0.9833333 0.9666667 0.9666667
## Precisión de Prueba 0.9666667 0.9333333 0.9333333 0.9333333 0.9666667
## rf
## Precisón de entrenamiento 1.0000000
## Precisión de Prueba 0.9333333Conclusiones
Acorde a la tabla de resultados, observamos que ningún método presenta sobreajuste. Podemos seleccionar el de redes neuronales por su desempeño.
---
title: "CARET"
author: "Adrian Morales A01722532"
date: "2025-08-22"
output: 
  html_document:
    toc: True
    toc_float: True
    code_download: true
    theme: yeti
---

<center>
![](https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcRNrkWGY3XSVrIIcDiggl_yJpiRPugibDkH79v-gA7GIL0YwE9ZSEwqxJCFu3kBinNOAHvzwIR4vNeLVCiCp_vTMg)

</center>

# <span style="color:blue;"> Teoria </span>
El paquete CARET (Classification And Regression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automatico.

# <span style="color:blue;"> Instalar paquetes y cargar librerias </span>
```{r}
#install.packages("ggplot2") # Gráficas
library(ggplot2)
#install.packages("lattice") # Crear gráficos
library(lattice)
#install.packages ("caret") # Algoritmos de aprendizaje automático
library (caret)
#install.packages ("datasets") # Usar bases de datos, en este caso Iris
library(datasets)
#install.packages ("DataExplorer") # Análisis Exploratorio
library (DataExplorer)
#install.packages("kernlab")
library(kernlab)
```


# <span style="color:blue;"> Crear la base de datos </span>

```{r}
df <- data.frame(iris)
```

# <span style="color:blue;"> Entender la base de datos </span>
```{r}
summary(df)
str(df)
plot_missing(df)
plot_histogram(df)
plot_correlation(df)
```

# <span style="color:blue;"> Partir la base de datos </span>

```{r}
# Normalmente 80-20
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$Species, p=0.8, list = FALSE)
entrenamiento <- iris[renglones_entrenamiento, ]
prueba <- iris[-renglones_entrenamiento, ]
```

# <span style="color:blue;"> Distintos tipos de métodos para Modelar </span>

Los métodos más utilizados para modelar aprendizaje automático:

* *SVM: *Support Vector Machine o Máquina de Vectores de Soporte. Hay varios subtipos: Lineal (svmLinear), Radial (svmRadial), Polinómico (svmPoly), etc.

* *Árbol de Decisión*: rpart
* *Redes Neuronales*: nnet
* *Random Forest* o Bosques Aleatorios: rf

# <span style="color:blue;"> Modelo 1. SVM Lineal </span>
```{r}
modelo1 <- train(Species ~ ., data=entrenamiento,
                 method = "svmLinear", #Cambiar
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10),
                 tuneGride = data.frame(C=1) #Cambiar
                 )

resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)

#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.

#Matriz de confusion del resultado del entrenamiento
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$Species)
mcre1
# Matriz de confusion del resultado de la prueba
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$Species)
mcrp1
```

# <span style="color:blue;"> Modelo 2. SVM Radial </span>
```{r}
modelo2 <- train(Species ~ ., data=entrenamiento,
                 method = "svmRadial", #Cambiar
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10),
                 tuneGride = data.frame(sigma=1, C=1) #Cambiar
                 )

resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)

#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.

#Matriz de confusion del resultado del entrenamiento
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$Species)
mcre2
# Matriz de confusion del resultado de la prueba
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$Species)
mcrp2
```

# <span style="color:blue;"> Modelo 3. SVM Poly </span>
```{r}
modelo3 <- train(Species ~ ., data=entrenamiento,
                 method = "svmRadial", #Cambiar
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10),
                 tuneGride = data.frame(degree=1, C=1, scale=1) #Cambiar
                 )

resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)

#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.

#Matriz de confusion del resultado del entrenamiento
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$Species)
mcre3
# Matriz de confusion del resultado de la prueba
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$Species)
mcrp3
```

# <span style="color:blue;"> Modelo 4. Arboles de decision </span>
```{r message=FALSE, warning=FALSE}
modelo4 <- train(Species ~ ., data=entrenamiento,
                 method = "rpart", #Cambiar
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10),
                 tuneLength = 10
                 )

resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo3, prueba)

#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.

#Matriz de confusion del resultado del entrenamiento
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$Species)
mcre4
# Matriz de confusion del resultado de la prueba
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$Species)
mcrp4
```

# <span style="color:blue;"> Modelo 5. Redes Neuronales </span>
```{r}
modelo5 <- train(Species ~ ., data=entrenamiento,
                 method = "nnet", #Cambiar
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10)
                 
                 )

resultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)

#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.

#Matriz de confusion del resultado del entrenamiento
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$Species)
mcre5
# Matriz de confusion del resultado de la prueba
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$Species)
mcrp5
```

# <span style="color:blue;"> Modelo 6. Random Forest </span>
```{r}
modelo6 <- train(Species ~ ., data=entrenamiento,
                 method = "rf", #Cambiar
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10),
                 tuneGrid = expand.grid(mtry = c(2,4,6))
                 )

resultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)

#Matrices de confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasficiación.

#Matriz de confusion del resultado del entrenamiento
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$Species)
mcre6
# Matriz de confusion del resultado de la prueba
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$Species)
mcrp6
```

# <span style="color:blue;"> Resultados</span>
```{r}
resultados <- data.frame(
  "svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
  "svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
  "svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
  "rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
  "nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
  "rf" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisón de entrenamiento", "Precisión de Prueba")
resultados
```

# <span style="color: blue"> Conclusiones </span>
Acorde a la tabla de resultados, observamos que ningún método presenta sobreajuste. Podemos seleccionar el de *redes neuronales* por su desempeño.