Caret
Sebastián Fajardo- A01412035
2024-08-19
Teoría
El paquete caret (clasificación And regression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.
Teoría
#install.packages("ggplot2")
library(ggplot2)
#install.packages("lattice")
library(lattice)
#install.packages("caret")
library(caret)
#install.packages("datasets")
library(datasets)
#install.packages("DataExplorer")
library(DataExplorer)
#install.packages("kernlab")
library(kernlab)
#install.packages("randomForest")
library(randomForest)Crear base de datos
df<- data.frame(iris)Analisis exploratorio
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)
** NOTA: La variable que queremos predecir debe tener formato de FACTOR.**
Partir los datos 80-20
set.seed(123)
renglones_entrenamiento<-createDataPartition(df$Species, p=0.8, list =FALSE)
entrenamiento <- iris[renglones_entrenamiento,]
prueba <- iris[-renglones_entrenamiento, ]Métodos para modelar
Los metodos más utilizados para modelar aprendizaje automático son:
- SVM: Support Vector Machine o Máquina de
vectores de soporte. Hay varios subtipos:
Lineal (svmlinear), Radial(svmRadial), Polinomico(svmPoly),etc. - Árbol de Decisiones: rpart
- Redes Neuronales: nnet
- Random Forest o Bosques Aleatorios: rf
1.Modelo con el Método svmLinear
modelo1 <- train(Species ~ ., data = entrenamiento,
method = "svmLinear",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number = 10),
tuneGrid = data.frame(C = 1))
# Predicciones
resultado_entrenamiento1 <- predict(modelo1, newdata = entrenamiento)
resultado_prueba1 <- predict(modelo1, newdata = prueba)
# Matrices de confusión
mcre1 <- confusionMatrix(data = resultado_entrenamiento1, reference = entrenamiento$Species)
mcrp1 <- confusionMatrix(data = resultado_prueba1, reference = prueba$Species)
# Imprimir resultados
print(mcre1)## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 0
## virginica 0 1 40
##
## Overall Statistics
##
## Accuracy : 0.9917
## 95% CI : (0.9544, 0.9998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9875
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 1.0000
## Specificity 1.0000 1.0000 0.9875
## Pos Pred Value 1.0000 1.0000 0.9756
## Neg Pred Value 1.0000 0.9877 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3333
## Detection Prevalence 0.3333 0.3250 0.3417
## Balanced Accuracy 1.0000 0.9875 0.9938print(mcrp1)## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 1
## virginica 0 0 9
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.9000
## Specificity 1.0000 0.9500 1.0000
## Pos Pred Value 1.0000 0.9091 1.0000
## Neg Pred Value 1.0000 1.0000 0.9524
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3000
## Detection Prevalence 0.3333 0.3667 0.3000
## Balanced Accuracy 1.0000 0.9750 0.95002.Modelo con el Método svmRadial
modelo2 <- train(Species ~ ., data = entrenamiento,
method = "svmRadial",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number = 10),
tuneGrid = data.frame(sigma = 1, C = 1))
resultado_entrenamiento2 <- predict(modelo2, newdata = entrenamiento)
resultado_prueba2 <- predict(modelo2, newdata = prueba)
mcre2 <- confusionMatrix(data = resultado_entrenamiento2, reference = entrenamiento$Species)
mcrp2 <- confusionMatrix(data = resultado_prueba2, reference = prueba$Species)
print(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 0.9938print(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 0.90003.Modelo con el Método svmPoly
# Entrenamiento del modelo SVM Polinómico
modelo3 <- train(Species ~ ., data = entrenamiento,
method = "svmPoly",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number = 10),
tuneGrid = data.frame(degree = 1, scale = 1, C = 1))
# Predicciones en el conjunto de entrenamiento y prueba
resultado_entrenamiento3 <- predict(modelo3, newdata = entrenamiento)
resultado_prueba3 <- predict(modelo3, newdata = prueba)
# Matrices de confusión
mcre3 <- confusionMatrix(data = resultado_entrenamiento3, reference = entrenamiento$Species)
mcrp3 <- confusionMatrix(data = resultado_prueba3, reference = prueba$Species)
# Imprimir resultados
print(mcre3)## 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 0.9938print(mcrp3)## 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 0.95004.Modelo con el Método rpart
modelo4 <- train(Species ~ ., data = entrenamiento,
method = "rpart",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number = 10),
tuneLength = 10) # Corregido: Añadido paréntesis de cierre
# Predicciones en el conjunto de entrenamiento y prueba
resultado_entrenamiento4 <- predict(modelo4, newdata = entrenamiento)
resultado_prueba4 <- predict(modelo4, newdata = prueba)
# Matrices de confusión
mcre4 <- confusionMatrix(data = resultado_entrenamiento4, reference = entrenamiento$Species)
mcrp4 <- confusionMatrix(data = resultado_prueba4, reference = prueba$Species)
# Imprimir resultados
print(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 0.9563print(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 0.90004.Modelo con el Método Nnet
modelo5 <- train(Species ~ ., data = entrenamiento,
method = "nnet",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number = 10)
)## # weights: 11
## initial value 130.530132
## iter 10 value 50.031494
## iter 20 value 48.622939
## iter 30 value 46.051782
## iter 40 value 45.435982
## iter 50 value 45.023331
## iter 60 value 41.544637
## iter 70 value 18.345218
## iter 80 value 4.630621
## iter 90 value 3.573146
## iter 100 value 2.696218
## final value 2.696218
## stopped after 100 iterations
## # weights: 27
## initial value 132.517409
## iter 10 value 22.263231
## iter 20 value 2.574680
## iter 30 value 0.008513
## final value 0.000051
## converged
## # weights: 43
## initial value 136.160730
## iter 10 value 3.642258
## iter 20 value 0.051614
## iter 30 value 0.013220
## iter 40 value 0.001249
## final value 0.000086
## converged
## # weights: 11
## initial value 124.472165
## iter 10 value 57.985437
## iter 20 value 43.232595
## final value 43.170440
## converged
## # weights: 27
## initial value 118.611044
## iter 10 value 30.413305
## iter 20 value 21.077103
## iter 30 value 20.192922
## iter 40 value 20.153936
## final value 20.153924
## converged
## # weights: 43
## initial value 131.301286
## iter 10 value 26.646865
## iter 20 value 17.682102
## iter 30 value 17.633586
## iter 40 value 17.623573
## iter 50 value 17.364993
## iter 60 value 17.295129
## iter 70 value 17.290694
## final value 17.290666
## converged
## # weights: 11
## initial value 115.622911
## iter 10 value 33.350769
## iter 20 value 4.676969
## iter 30 value 3.131052
## iter 40 value 2.922591
## iter 50 value 2.825976
## iter 60 value 2.769974
## iter 70 value 2.741299
## iter 80 value 2.741136
## iter 90 value 2.739093
## final value 2.739035
## converged
## # weights: 27
## initial value 139.822975
## iter 10 value 37.447376
## iter 20 value 1.445699
## iter 30 value 0.316497
## iter 40 value 0.287713
## iter 50 value 0.260591
## iter 60 value 0.236249
## iter 70 value 0.224761
## iter 80 value 0.215415
## iter 90 value 0.194816
## iter 100 value 0.189471
## final value 0.189471
## stopped after 100 iterations
## # weights: 43
## initial value 123.298044
## iter 10 value 4.177632
## iter 20 value 0.257205
## iter 30 value 0.224601
## iter 40 value 0.200241
## iter 50 value 0.193031
## iter 60 value 0.182082
## iter 70 value 0.164800
## iter 80 value 0.149792
## iter 90 value 0.144373
## iter 100 value 0.142810
## final value 0.142810
## stopped after 100 iterations
## # weights: 11
## initial value 123.243079
## iter 10 value 49.923348
## iter 20 value 49.909994
## iter 30 value 49.907880
## final value 49.906719
## converged
## # weights: 27
## initial value 117.894759
## iter 10 value 9.481781
## iter 20 value 0.026637
## iter 30 value 0.001156
## final value 0.000052
## converged
## # weights: 43
## initial value 131.870976
## iter 10 value 17.010430
## iter 20 value 0.698814
## iter 30 value 0.001401
## final value 0.000067
## converged
## # weights: 11
## initial value 141.804121
## iter 10 value 63.315182
## iter 20 value 44.532148
## iter 30 value 42.998412
## final value 42.994034
## converged
## # weights: 27
## initial value 129.180442
## iter 10 value 44.217928
## iter 20 value 19.729677
## iter 30 value 18.527378
## iter 40 value 18.411074
## iter 50 value 18.393711
## iter 60 value 18.393129
## final value 18.393125
## converged
## # weights: 43
## initial value 143.533117
## iter 10 value 21.063126
## iter 20 value 17.843661
## iter 30 value 17.106737
## iter 40 value 16.985544
## iter 50 value 16.981278
## iter 60 value 16.980626
## final value 16.980585
## converged
## # weights: 11
## initial value 123.091645
## iter 10 value 49.148390
## iter 20 value 35.943210
## iter 30 value 10.736283
## iter 40 value 2.021433
## iter 50 value 1.687393
## iter 60 value 1.640809
## iter 70 value 1.636953
## iter 80 value 1.613389
## iter 90 value 1.611928
## iter 100 value 1.611136
## final value 1.611136
## stopped after 100 iterations
## # weights: 27
## initial value 113.416728
## iter 10 value 6.236444
## iter 20 value 0.187917
## iter 30 value 0.166748
## iter 40 value 0.155642
## iter 50 value 0.144249
## iter 60 value 0.141208
## iter 70 value 0.138463
## iter 80 value 0.136774
## iter 90 value 0.134567
## iter 100 value 0.132971
## final value 0.132971
## stopped after 100 iterations
## # weights: 43
## initial value 124.153763
## iter 10 value 6.673362
## iter 20 value 0.166533
## iter 30 value 0.154159
## iter 40 value 0.149227
## iter 50 value 0.136832
## iter 60 value 0.125718
## iter 70 value 0.121478
## iter 80 value 0.115540
## iter 90 value 0.113390
## iter 100 value 0.110992
## final value 0.110992
## stopped after 100 iterations
## # weights: 11
## initial value 128.347385
## iter 10 value 55.157651
## iter 20 value 47.800562
## iter 30 value 47.763719
## iter 40 value 47.763542
## iter 50 value 47.762534
## final value 47.762465
## converged
## # weights: 27
## initial value 115.590774
## iter 10 value 5.054265
## iter 20 value 1.048058
## iter 30 value 0.000979
## final value 0.000072
## converged
## # weights: 43
## initial value 123.951869
## iter 10 value 13.178443
## iter 20 value 0.965118
## iter 30 value 0.002392
## final value 0.000078
## converged
## # weights: 11
## initial value 123.195822
## iter 10 value 53.656490
## iter 20 value 43.803131
## iter 30 value 43.734766
## final value 43.734347
## converged
## # weights: 27
## initial value 123.651803
## iter 10 value 29.880588
## iter 20 value 19.921143
## iter 30 value 19.707388
## iter 40 value 19.705704
## final value 19.705624
## converged
## # weights: 43
## initial value 148.336280
## iter 10 value 27.474145
## iter 20 value 18.301737
## iter 30 value 18.138015
## iter 40 value 18.086240
## iter 50 value 18.084155
## iter 60 value 18.083934
## final value 18.083909
## converged
## # weights: 11
## initial value 122.563728
## iter 10 value 32.122176
## iter 20 value 10.269949
## iter 30 value 4.526292
## iter 40 value 3.900620
## iter 50 value 3.805816
## iter 60 value 3.743349
## iter 70 value 3.733207
## iter 80 value 3.721238
## iter 90 value 3.713938
## iter 100 value 3.705684
## final value 3.705684
## stopped after 100 iterations
## # weights: 27
## initial value 130.631378
## iter 10 value 4.944652
## iter 20 value 0.903581
## iter 30 value 0.602599
## iter 40 value 0.449328
## iter 50 value 0.416076
## iter 60 value 0.405323
## iter 70 value 0.397568
## iter 80 value 0.392801
## iter 90 value 0.386606
## iter 100 value 0.380965
## final value 0.380965
## stopped after 100 iterations
## # weights: 43
## initial value 152.884265
## iter 10 value 11.737646
## iter 20 value 1.402922
## iter 30 value 0.553654
## iter 40 value 0.456488
## iter 50 value 0.433353
## iter 60 value 0.391721
## iter 70 value 0.350673
## iter 80 value 0.322382
## iter 90 value 0.309362
## iter 100 value 0.302224
## final value 0.302224
## stopped after 100 iterations
## # weights: 11
## initial value 133.677265
## iter 10 value 49.425529
## iter 20 value 45.125104
## iter 30 value 24.714814
## iter 40 value 6.951374
## iter 50 value 3.962940
## iter 60 value 3.585057
## iter 70 value 2.556588
## iter 80 value 2.219301
## iter 90 value 2.033936
## iter 100 value 2.011517
## final value 2.011517
## stopped after 100 iterations
## # weights: 27
## initial value 120.219437
## iter 10 value 20.105178
## iter 20 value 0.691846
## iter 30 value 0.000424
## final value 0.000094
## converged
## # weights: 43
## initial value 130.013247
## iter 10 value 6.990719
## iter 20 value 0.117056
## final value 0.000078
## converged
## # weights: 11
## initial value 122.587894
## iter 10 value 55.646479
## iter 20 value 44.073616
## iter 30 value 44.056707
## final value 44.056649
## converged
## # weights: 27
## initial value 122.488484
## iter 10 value 30.042105
## iter 20 value 22.364237
## iter 30 value 21.402694
## iter 40 value 21.391770
## final value 21.391728
## converged
## # weights: 43
## initial value 151.848122
## iter 10 value 27.150882
## iter 20 value 20.889994
## iter 30 value 19.061592
## iter 40 value 18.857339
## iter 50 value 18.636402
## iter 60 value 18.597842
## iter 70 value 18.581420
## final value 18.581304
## converged
## # weights: 11
## initial value 125.447189
## iter 10 value 42.432302
## iter 20 value 14.708081
## iter 30 value 5.928158
## iter 40 value 4.717183
## iter 50 value 4.261072
## iter 60 value 3.990872
## iter 70 value 3.894029
## iter 80 value 3.877352
## iter 90 value 3.868847
## iter 100 value 3.865924
## final value 3.865924
## stopped after 100 iterations
## # weights: 27
## initial value 141.522247
## iter 10 value 19.693351
## iter 20 value 2.060082
## iter 30 value 0.713635
## iter 40 value 0.684010
## iter 50 value 0.651024
## iter 60 value 0.599068
## iter 70 value 0.534727
## iter 80 value 0.525302
## iter 90 value 0.477461
## iter 100 value 0.468105
## final value 0.468105
## stopped after 100 iterations
## # weights: 43
## initial value 117.492171
## iter 10 value 5.474776
## iter 20 value 0.633193
## iter 30 value 0.523049
## iter 40 value 0.506835
## iter 50 value 0.486677
## iter 60 value 0.470314
## iter 70 value 0.423468
## iter 80 value 0.413761
## iter 90 value 0.406423
## iter 100 value 0.383741
## final value 0.383741
## stopped after 100 iterations
## # weights: 11
## initial value 128.494859
## iter 10 value 67.868204
## iter 20 value 40.370984
## iter 30 value 8.030160
## iter 40 value 3.602779
## iter 50 value 3.354454
## iter 60 value 3.245703
## iter 70 value 3.148407
## iter 80 value 3.017263
## iter 90 value 2.916368
## iter 100 value 2.697585
## final value 2.697585
## stopped after 100 iterations
## # weights: 27
## initial value 121.387618
## iter 10 value 17.333188
## iter 20 value 6.562404
## iter 30 value 4.218606
## iter 40 value 0.023796
## iter 50 value 0.013835
## iter 60 value 0.007181
## iter 70 value 0.000265
## final value 0.000094
## converged
## # weights: 43
## initial value 131.764022
## iter 10 value 6.923964
## iter 20 value 0.585918
## iter 30 value 0.001510
## final value 0.000094
## converged
## # weights: 11
## initial value 117.924376
## iter 10 value 59.153858
## iter 20 value 45.980503
## iter 30 value 43.965813
## final value 43.965807
## converged
## # weights: 27
## initial value 122.524569
## iter 10 value 28.252379
## iter 20 value 20.308998
## iter 30 value 19.983255
## iter 40 value 19.969846
## final value 19.969845
## converged
## # weights: 43
## initial value 175.722543
## iter 10 value 24.152694
## iter 20 value 19.351652
## iter 30 value 18.570128
## iter 40 value 18.540253
## iter 50 value 18.531786
## iter 60 value 18.531273
## final value 18.531272
## converged
## # weights: 11
## initial value 125.626851
## iter 10 value 50.695359
## iter 20 value 28.615271
## iter 30 value 12.424432
## iter 40 value 5.029030
## iter 50 value 4.166888
## iter 60 value 3.979676
## iter 70 value 3.882211
## iter 80 value 3.873043
## iter 90 value 3.872674
## iter 100 value 3.871442
## final value 3.871442
## stopped after 100 iterations
## # weights: 27
## initial value 123.025871
## iter 10 value 27.020381
## iter 20 value 2.694706
## iter 30 value 1.092737
## iter 40 value 0.872715
## iter 50 value 0.758401
## iter 60 value 0.630276
## iter 70 value 0.571755
## iter 80 value 0.515264
## iter 90 value 0.475373
## iter 100 value 0.452081
## final value 0.452081
## stopped after 100 iterations
## # weights: 43
## initial value 134.385829
## iter 10 value 5.396493
## iter 20 value 1.952502
## iter 30 value 0.810078
## iter 40 value 0.740163
## iter 50 value 0.700944
## iter 60 value 0.648312
## iter 70 value 0.581811
## iter 80 value 0.540064
## iter 90 value 0.513923
## iter 100 value 0.483298
## final value 0.483298
## stopped after 100 iterations
## # weights: 11
## initial value 124.033991
## iter 10 value 53.598901
## iter 20 value 53.094417
## iter 30 value 51.710795
## iter 40 value 44.732729
## iter 50 value 17.279075
## iter 60 value 6.529735
## iter 70 value 3.465736
## iter 80 value 3.270944
## iter 90 value 3.153543
## iter 100 value 3.002420
## final value 3.002420
## stopped after 100 iterations
## # weights: 27
## initial value 126.207925
## iter 10 value 6.867316
## iter 20 value 0.342203
## iter 30 value 0.000889
## final value 0.000071
## converged
## # weights: 43
## initial value 146.268437
## iter 10 value 7.061711
## iter 20 value 1.073309
## iter 30 value 0.000467
## final value 0.000066
## converged
## # weights: 11
## initial value 120.866935
## iter 10 value 85.950877
## iter 20 value 60.671406
## iter 30 value 50.749580
## iter 40 value 43.846120
## final value 43.846095
## converged
## # weights: 27
## initial value 126.514320
## iter 10 value 46.451931
## iter 20 value 22.288378
## iter 30 value 21.611509
## iter 40 value 21.142364
## iter 50 value 20.374688
## iter 60 value 19.975509
## iter 70 value 19.860029
## final value 19.859991
## converged
## # weights: 43
## initial value 113.521981
## iter 10 value 27.307122
## iter 20 value 19.069629
## iter 30 value 18.496103
## iter 40 value 18.414947
## iter 50 value 18.412091
## iter 60 value 18.411932
## final value 18.411927
## converged
## # weights: 11
## initial value 119.931364
## iter 10 value 33.212563
## iter 20 value 6.825543
## iter 30 value 4.153607
## iter 40 value 3.996719
## iter 50 value 3.936301
## iter 60 value 3.900913
## iter 70 value 3.868653
## iter 80 value 3.868193
## iter 90 value 3.864798
## iter 100 value 3.860658
## final value 3.860658
## stopped after 100 iterations
## # weights: 27
## initial value 125.980953
## iter 10 value 3.828376
## iter 20 value 1.757039
## iter 30 value 1.084888
## iter 40 value 0.779504
## iter 50 value 0.534913
## iter 60 value 0.521705
## iter 70 value 0.515783
## iter 80 value 0.504124
## iter 90 value 0.485201
## iter 100 value 0.483827
## final value 0.483827
## stopped after 100 iterations
## # weights: 43
## initial value 143.013185
## iter 10 value 7.195354
## iter 20 value 1.984745
## iter 30 value 0.713672
## iter 40 value 0.552459
## iter 50 value 0.437450
## iter 60 value 0.403627
## iter 70 value 0.363382
## iter 80 value 0.356303
## iter 90 value 0.346628
## iter 100 value 0.337926
## final value 0.337926
## stopped after 100 iterations
## # weights: 11
## initial value 119.603843
## iter 10 value 66.519353
## iter 20 value 48.085237
## iter 30 value 10.691129
## iter 40 value 4.343493
## iter 50 value 3.486657
## iter 60 value 2.937962
## iter 70 value 2.185862
## iter 80 value 1.910157
## iter 90 value 1.802781
## iter 100 value 1.791733
## final value 1.791733
## stopped after 100 iterations
## # weights: 27
## initial value 120.493313
## iter 10 value 14.568437
## iter 20 value 1.413139
## iter 30 value 0.002421
## final value 0.000049
## converged
## # weights: 43
## initial value 131.990396
## iter 10 value 3.607345
## iter 20 value 0.869522
## iter 30 value 0.000776
## final value 0.000079
## converged
## # weights: 11
## initial value 127.213395
## iter 10 value 58.997762
## iter 20 value 44.424763
## final value 43.139243
## converged
## # weights: 27
## initial value 117.195869
## iter 10 value 28.619024
## iter 20 value 19.206476
## iter 30 value 18.621574
## iter 40 value 18.619068
## iter 40 value 18.619068
## iter 40 value 18.619068
## final value 18.619068
## converged
## # weights: 43
## initial value 165.598734
## iter 10 value 24.205649
## iter 20 value 17.629535
## iter 30 value 17.222776
## iter 40 value 17.168752
## iter 50 value 17.168464
## iter 60 value 17.168428
## iter 60 value 17.168428
## iter 60 value 17.168428
## final value 17.168428
## converged
## # weights: 11
## initial value 115.941037
## iter 10 value 48.705139
## iter 20 value 47.783092
## iter 30 value 43.562064
## iter 40 value 11.101593
## iter 50 value 4.031437
## iter 60 value 3.116711
## iter 70 value 3.019260
## iter 80 value 2.993105
## iter 90 value 2.981303
## iter 100 value 2.969047
## final value 2.969047
## stopped after 100 iterations
## # weights: 27
## initial value 132.813339
## iter 10 value 3.715700
## iter 20 value 1.056815
## iter 30 value 0.558748
## iter 40 value 0.530262
## iter 50 value 0.467614
## iter 60 value 0.445847
## iter 70 value 0.424130
## iter 80 value 0.373259
## iter 90 value 0.354379
## iter 100 value 0.342801
## final value 0.342801
## stopped after 100 iterations
## # weights: 43
## initial value 126.886256
## iter 10 value 3.942342
## iter 20 value 1.736816
## iter 30 value 0.630651
## iter 40 value 0.552680
## iter 50 value 0.489807
## iter 60 value 0.396264
## iter 70 value 0.356221
## iter 80 value 0.340605
## iter 90 value 0.328238
## iter 100 value 0.321360
## final value 0.321360
## stopped after 100 iterations
## # weights: 11
## initial value 128.489378
## iter 10 value 49.909576
## iter 20 value 49.876540
## iter 30 value 47.945970
## iter 40 value 39.847846
## iter 50 value 8.019855
## iter 60 value 4.613532
## iter 70 value 2.856566
## iter 80 value 1.479799
## iter 90 value 1.304505
## iter 100 value 1.264325
## final value 1.264325
## stopped after 100 iterations
## # weights: 27
## initial value 141.912242
## iter 10 value 7.102731
## iter 20 value 0.339738
## final value 0.000079
## converged
## # weights: 43
## initial value 128.771330
## iter 10 value 21.354630
## iter 20 value 2.784172
## iter 30 value 0.013786
## iter 40 value 0.000332
## final value 0.000076
## converged
## # weights: 11
## initial value 120.181179
## iter 10 value 46.347790
## iter 20 value 43.064428
## iter 30 value 43.054040
## final value 43.054021
## converged
## # weights: 27
## initial value 126.647230
## iter 10 value 25.682812
## iter 20 value 20.660342
## iter 30 value 19.500529
## iter 40 value 19.121600
## iter 50 value 19.088454
## iter 60 value 19.083697
## final value 19.083689
## converged
## # weights: 43
## initial value 132.234904
## iter 10 value 29.615687
## iter 20 value 19.279132
## iter 30 value 17.877712
## iter 40 value 17.806996
## iter 50 value 17.793960
## iter 60 value 17.793819
## final value 17.793686
## converged
## # weights: 11
## initial value 121.579687
## iter 10 value 49.472914
## iter 20 value 48.410085
## iter 30 value 45.340464
## iter 40 value 37.104905
## iter 50 value 8.129207
## iter 60 value 4.703713
## iter 70 value 4.278400
## iter 80 value 3.667245
## iter 90 value 3.604730
## iter 100 value 3.568160
## final value 3.568160
## stopped after 100 iterations
## # weights: 27
## initial value 135.360878
## iter 10 value 10.436945
## iter 20 value 2.222820
## iter 30 value 0.763058
## iter 40 value 0.725440
## iter 50 value 0.677966
## iter 60 value 0.570628
## iter 70 value 0.518380
## iter 80 value 0.502364
## iter 90 value 0.462332
## iter 100 value 0.455880
## final value 0.455880
## stopped after 100 iterations
## # weights: 43
## initial value 125.924213
## iter 10 value 3.865138
## iter 20 value 1.025246
## iter 30 value 0.422681
## iter 40 value 0.379135
## iter 50 value 0.353145
## iter 60 value 0.335865
## iter 70 value 0.319622
## iter 80 value 0.303895
## iter 90 value 0.289299
## iter 100 value 0.271561
## final value 0.271561
## stopped after 100 iterations
## # weights: 11
## initial value 114.925820
## iter 10 value 45.333263
## iter 20 value 21.250608
## iter 30 value 6.082611
## iter 40 value 4.448976
## iter 50 value 3.266614
## iter 60 value 1.880390
## iter 70 value 1.733764
## iter 80 value 1.089267
## iter 90 value 1.045776
## iter 100 value 0.950634
## final value 0.950634
## stopped after 100 iterations
## # weights: 27
## initial value 116.607224
## iter 10 value 6.159810
## iter 20 value 1.197702
## iter 30 value 0.000196
## final value 0.000057
## converged
## # weights: 43
## initial value 123.125697
## iter 10 value 4.793414
## iter 20 value 0.073094
## iter 30 value 0.000393
## final value 0.000088
## converged
## # weights: 11
## initial value 120.471214
## iter 10 value 45.420303
## iter 20 value 43.694661
## iter 30 value 43.690235
## final value 43.690202
## converged
## # weights: 27
## initial value 168.714249
## iter 10 value 28.073376
## iter 20 value 21.126580
## iter 30 value 20.968508
## iter 40 value 20.968134
## final value 20.968117
## converged
## # weights: 43
## initial value 134.057733
## iter 10 value 44.240823
## iter 20 value 19.621880
## iter 30 value 18.596469
## iter 40 value 18.220014
## iter 50 value 18.200869
## iter 60 value 18.194706
## final value 18.194547
## converged
## # weights: 11
## initial value 137.081572
## iter 10 value 53.546736
## iter 20 value 49.263649
## iter 30 value 49.116099
## iter 40 value 49.041348
## iter 50 value 48.683090
## iter 60 value 48.634845
## iter 70 value 48.489442
## iter 80 value 48.480790
## iter 90 value 48.451846
## iter 100 value 48.179017
## final value 48.179017
## stopped after 100 iterations
## # weights: 27
## initial value 143.490043
## iter 10 value 4.357251
## iter 20 value 1.321252
## iter 30 value 0.645280
## iter 40 value 0.616636
## iter 50 value 0.565996
## iter 60 value 0.521660
## iter 70 value 0.508617
## iter 80 value 0.487870
## iter 90 value 0.483152
## iter 100 value 0.479423
## final value 0.479423
## stopped after 100 iterations
## # weights: 43
## initial value 178.832632
## iter 10 value 8.121158
## iter 20 value 1.422046
## iter 30 value 0.568662
## iter 40 value 0.518952
## iter 50 value 0.434974
## iter 60 value 0.392568
## iter 70 value 0.345835
## iter 80 value 0.285289
## iter 90 value 0.268178
## iter 100 value 0.253675
## final value 0.253675
## stopped after 100 iterations
## # weights: 11
## initial value 123.307045
## iter 10 value 43.672929
## iter 20 value 8.049676
## iter 30 value 3.773651
## iter 40 value 3.173208
## iter 50 value 3.060201
## iter 60 value 2.971167
## iter 70 value 2.563371
## iter 80 value 2.471224
## iter 90 value 2.341221
## iter 100 value 2.320048
## final value 2.320048
## stopped after 100 iterations
## # weights: 27
## initial value 129.270569
## iter 10 value 10.575847
## iter 20 value 2.930770
## iter 30 value 1.689612
## iter 40 value 0.097359
## iter 50 value 0.000123
## iter 50 value 0.000057
## iter 50 value 0.000057
## final value 0.000057
## converged
## # weights: 43
## initial value 119.634242
## iter 10 value 6.310691
## iter 20 value 1.591412
## iter 30 value 0.028391
## iter 40 value 0.000902
## final value 0.000069
## converged
## # weights: 11
## initial value 120.069235
## iter 10 value 60.195069
## iter 20 value 51.394914
## iter 30 value 43.991436
## final value 43.991141
## converged
## # weights: 27
## initial value 152.809198
## iter 10 value 25.471737
## iter 20 value 21.511163
## iter 30 value 21.387357
## iter 40 value 21.386800
## final value 21.386800
## converged
## # weights: 43
## initial value 137.024287
## iter 10 value 22.447246
## iter 20 value 19.002967
## iter 30 value 18.519064
## iter 40 value 18.404215
## iter 50 value 18.397540
## iter 60 value 18.396716
## final value 18.396607
## converged
## # weights: 11
## initial value 121.726735
## iter 10 value 50.373336
## iter 20 value 50.105529
## iter 30 value 49.998791
## iter 40 value 49.958270
## iter 50 value 49.774790
## iter 60 value 48.541266
## iter 70 value 18.978385
## iter 80 value 6.743585
## iter 90 value 4.055527
## iter 100 value 3.921743
## final value 3.921743
## stopped after 100 iterations
## # weights: 27
## initial value 146.633351
## iter 10 value 6.579898
## iter 20 value 0.624311
## iter 30 value 0.562510
## iter 40 value 0.514462
## iter 50 value 0.457198
## iter 60 value 0.403961
## iter 70 value 0.382785
## iter 80 value 0.371306
## iter 90 value 0.358751
## iter 100 value 0.317469
## final value 0.317469
## stopped after 100 iterations
## # weights: 43
## initial value 127.981900
## iter 10 value 7.369546
## iter 20 value 0.839917
## iter 30 value 0.675447
## iter 40 value 0.617273
## iter 50 value 0.540482
## iter 60 value 0.477520
## iter 70 value 0.443309
## iter 80 value 0.359346
## iter 90 value 0.308424
## iter 100 value 0.292198
## final value 0.292198
## stopped after 100 iterations
## # weights: 11
## initial value 133.510869
## iter 10 value 66.279276
## iter 20 value 49.065891
## iter 30 value 46.607987
## final value 46.598156
## converged# Predicciones en el conjunto de entrenamiento y prueba
resultado_entrenamiento5 <- predict(modelo5, newdata = entrenamiento)
resultado_prueba5 <- predict(modelo5, newdata = prueba)
# Matrices de confusión
mcre5 <- confusionMatrix(data = resultado_entrenamiento5, reference = entrenamiento$Species)
mcrp5 <- confusionMatrix(data = resultado_prueba5, reference = prueba$Species)6.Modelo con el Método Bosques Aleatoriost
modelo6 <- train(Species ~ ., data = entrenamiento,
method = "rf",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number = 10),
tuneGrid = expand.grid(mtry = c(2,4,6))
)
# Predicciones en el conjunto de entrenamiento y prueba
resultado_entrenamiento6 <- predict(modelo6, newdata = entrenamiento)
resultado_prueba6 <- predict(modelo6, newdata = prueba)
# Matrices de confusión
mcre6 <- confusionMatrix(data = resultado_entrenamiento6, reference = entrenamiento$Species)
mcrp6 <- confusionMatrix(data = resultado_prueba6, reference = prueba$Species)Resumen de resultados
resultados <- data.frame(
"svmLinear" = c(mcre1$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("Precisión de Entrenamiento", "Precisión de Prueba")
resultados## svmLinear svmRadial svmPoly rpart nnet
## Precisión de Entrenamiento 0.9916667 0.9916667 0.9916667 0.9666667 0.9666667
## Precisión de Prueba 0.9666667 0.9333333 0.9666667 0.9333333 0.9666667
## rf
## Precisión de Entrenamiento 1.0000000
## Precisión de Prueba 0.9333333Conclusiones
El modelo con el método de bosques aleatorios (rf) presenta sobreajuste, ya que tiene una alta precision en entrenamiento, pero baja en prueba.
Acorde al resumen de resultados, el modelo mejor evaluado es el de Máquina de vectores de Soporte Lineal.
---
title: "Caret"
author: "Sebastián Fajardo- A01412035"
date: "2024-08-19"
output: 
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
    theme: cerulean
---


![](/Users/sebastianfajardo/Downloads/iris.gif)

# <span style="color: violet;">Teoría </span>
El paquete *caret (clasificación And regression Training)* es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.

# <span style="color: violet;">Teoría </span>
```{r message=FALSE, warning=FALSE}
#install.packages("ggplot2")
library(ggplot2)
#install.packages("lattice")
library(lattice)
#install.packages("caret")
library(caret)
#install.packages("datasets")
library(datasets)
#install.packages("DataExplorer")
library(DataExplorer)
#install.packages("kernlab")
library(kernlab)
#install.packages("randomForest")
library(randomForest)
```
# <span style="color: violet;">Crear base de datos </span>
```{r}
df<- data.frame(iris)
```

# <span style="color: violet;">Analisis exploratorio</span>
```{r}
summary(df)
str(df)
plot_missing(df)
```

** NOTA: La variable que queremos predecir debe tener formato de FACTOR.**

# <span style="color: violet;"> Partir los datos 80-20</span>
```{r}
set.seed(123)
renglones_entrenamiento<-createDataPartition(df$Species, p=0.8, list =FALSE)
entrenamiento <- iris[renglones_entrenamiento,]
prueba <- iris[-renglones_entrenamiento, ]
```

## <span style="color: violet;"> Métodos para modelar</span>
Los metodos más utilizados para modelar aprendizaje automático son:  

* **SVM**: *Support Vector Machine* o Máquina de vectores de soporte. Hay varios subtipos:  
Lineal (svmlinear), Radial(svmRadial), Polinomico(svmPoly),etc. 
* **Árbol de Decisiones**: rpart
* **Redes Neuronales**: nnet
* **Random Forest** o Bosques Aleatorios: rf

## <span style="color: violet;"> 1.Modelo con el Método svmLinear</span>
```{r}
modelo1 <- train(Species ~ ., data = entrenamiento,
                 method = "svmLinear",
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method = "cv", number = 10),
                 tuneGrid = data.frame(C = 1))

# Predicciones
resultado_entrenamiento1 <- predict(modelo1, newdata = entrenamiento)
resultado_prueba1 <- predict(modelo1, newdata = prueba)

# Matrices de confusión
mcre1 <- confusionMatrix(data = resultado_entrenamiento1, reference = entrenamiento$Species)
mcrp1 <- confusionMatrix(data = resultado_prueba1, reference = prueba$Species)

# Imprimir resultados
print(mcre1)
print(mcrp1)
```

## <span style="color: violet;"> 2.Modelo con el Método svmRadial</span>
```{r}


modelo2 <- train(Species ~ ., data = entrenamiento,
                 method = "svmRadial",
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method = "cv", number = 10),
                 tuneGrid = data.frame(sigma = 1, C = 1))

resultado_entrenamiento2 <- predict(modelo2, newdata = entrenamiento)
resultado_prueba2 <- predict(modelo2, newdata = prueba)

mcre2 <- confusionMatrix(data = resultado_entrenamiento2, reference = entrenamiento$Species)
mcrp2 <- confusionMatrix(data = resultado_prueba2, reference = prueba$Species)

print(mcre2)
print(mcrp2)
```

## <span style="color: violet;"> 3.Modelo con el Método svmPoly</span>
```{r}

# Entrenamiento del modelo SVM Polinómico
modelo3 <- train(Species ~ ., data = entrenamiento,
                 method = "svmPoly",
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method = "cv", number = 10),
                 tuneGrid = data.frame(degree = 1, scale = 1, C = 1))

# Predicciones en el conjunto de entrenamiento y prueba
resultado_entrenamiento3 <- predict(modelo3, newdata = entrenamiento)
resultado_prueba3 <- predict(modelo3, newdata = prueba)

# Matrices de confusión
mcre3 <- confusionMatrix(data = resultado_entrenamiento3, reference = entrenamiento$Species)
mcrp3 <- confusionMatrix(data = resultado_prueba3, reference = prueba$Species)

# Imprimir resultados
print(mcre3)
print(mcrp3)
```
## <span style="color: violet;"> 4.Modelo con el Método rpart</span>

```{r}
modelo4 <- train(Species ~ ., data = entrenamiento,
                 method = "rpart",
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method = "cv", number = 10),
                 tuneLength = 10)  # Corregido: Añadido paréntesis de cierre

# Predicciones en el conjunto de entrenamiento y prueba
resultado_entrenamiento4 <- predict(modelo4, newdata = entrenamiento)
resultado_prueba4 <- predict(modelo4, newdata = prueba)

# Matrices de confusión
mcre4 <- confusionMatrix(data = resultado_entrenamiento4, reference = entrenamiento$Species)
mcrp4 <- confusionMatrix(data = resultado_prueba4, reference = prueba$Species)

# Imprimir resultados
print(mcre4)
print(mcrp4)
```

## <span style="color: violet;"> 4.Modelo con el Método Nnet</span>
```{r}
modelo5 <- train(Species ~ ., data = entrenamiento,
                 method = "nnet",
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method = "cv", number = 10)
)

# Predicciones en el conjunto de entrenamiento y prueba
resultado_entrenamiento5 <- predict(modelo5, newdata = entrenamiento)
resultado_prueba5 <- predict(modelo5, newdata = prueba)

# Matrices de confusión
mcre5 <- confusionMatrix(data = resultado_entrenamiento5, reference = entrenamiento$Species)
mcrp5 <- confusionMatrix(data = resultado_prueba5, reference = prueba$Species)

```

## <span style="color: violet;"> 6.Modelo con el Método Bosques Aleatoriost</span>
```{r message=FALSE, warning=FALSE}
modelo6 <- train(Species ~ ., data = entrenamiento,
                 method = "rf",
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method = "cv", number = 10),
                 tuneGrid = expand.grid(mtry = c(2,4,6))
)

# Predicciones en el conjunto de entrenamiento y prueba
resultado_entrenamiento6 <- predict(modelo6, newdata = entrenamiento)
resultado_prueba6 <- predict(modelo6, newdata = prueba)

# Matrices de confusión
mcre6 <- confusionMatrix(data = resultado_entrenamiento6, reference = entrenamiento$Species)
mcrp6 <- confusionMatrix(data = resultado_prueba6, reference = prueba$Species)
```

## <span style="color: violet;"> Resumen de 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("Precisión de Entrenamiento", "Precisión de Prueba")
resultados
```
## <span style="color: violet;"> Conclusiones </span>
El modelo con el método de bosques aleatorios  *(rf)*
presenta sobreajuste, ya que tiene una alta precision en entrenamiento, pero baja en prueba. 

Acorde al resumen de resultados, el modelo mejor evaluado es el de  **Máquina de vectores de Soporte Lineal**.