Machine Learning - IRIS
Gabriel Medina - A01275763
2024-02-20
Teoría
El paquete caret (Classification And REgression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.
Librerías
library(caret)
library(ggplot2) # Crear gráficos
library(datasets) # Usar la base de datos "Iris"
library(lattice) # Crear gráficos
library(DataExplorer)
library(mlbench)Base de datos
df <- data.frame(iris)Análisis exploratorio 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 ...#create_report(df)
plot_missing(df)
plot_histogram(df)
plot_correlation(df)
La variable será factor
Partición
set.seed(123)
renglones_entrenamiento <-createDataPartition(df$Species, p=0.8, list=FALSE)
entrenamiento <- iris[renglones_entrenamiento, ]
prueba <- iris[-renglones_entrenamiento, ]Diferentes modelos
Los métodos más utilizados para modelar aprendizaje automático son:
SVM: Support Vector Machine o Máquina de Vectores de Soporte. Hay varios subtipos: Lineal (svmLinear), Radial (svmRadial), Polinómicos (svmPoly), etc.
Árbol de Decisión: rpart
Redes Neuronales: nnet
Random Forests o Bosques aleatorios: rf
Lineal
modelo1 <- train(Species ~ ., data= entrenamiento, method = "svmLinear", preProcess= c("scale", "center"), trControl = trainControl(method = "cv", number = 10), tuneGrid = data.frame(C=1))
resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)Matriz de confusión
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$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 0.9938mcrp1 <- confusionMatrix(resultado_prueba1, prueba$Species)
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 1
## virginica 0 0 9
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.9000
## Specificity 1.0000 0.9500 1.0000
## Pos Pred Value 1.0000 0.9091 1.0000
## Neg Pred Value 1.0000 1.0000 0.9524
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3000
## Detection Prevalence 0.3333 0.3667 0.3000
## Balanced Accuracy 1.0000 0.9750 0.9500Modelo svmRadial
modelo2 <- train(Species ~ ., data= entrenamiento, method = "svmRadial", preProcess= c("scale", "center"), trControl = trainControl(method = "cv", number = 10), tuneGrid = data.frame(sigma=1, C=1)) #Cambiar
resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)Matriz de confusión
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$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 0.9938mcrp2 <- confusionMatrix(resultado_prueba2, prueba$Species)
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.9000Modelo svmPoly
modelo3 <- train(Species ~ ., data= entrenamiento, method = "svmPoly", preProcess= c("scale", "center"), trControl = trainControl(method = "cv", number = 10), tuneGrid = data.frame(degree=1,scale=1, C=1)) #Cambiar
resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)Matriz de confusión
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$Species)
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 0.9938mcrp3 <- confusionMatrix(resultado_prueba3, prueba$Species)
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 1
## virginica 0 0 9
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.9000
## Specificity 1.0000 0.9500 1.0000
## Pos Pred Value 1.0000 0.9091 1.0000
## Neg Pred Value 1.0000 1.0000 0.9524
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3000
## Detection Prevalence 0.3333 0.3667 0.3000
## Balanced Accuracy 1.0000 0.9750 0.9500Árbol de decisión
modelo4 <- train(Species ~ ., data = entrenamiento, method = "rpart", preProcess = c("scale", "center"), trControl = trainControl(method="cv", number = 10), tuneLength = 10)
resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)Matriz de confusión
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$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 0.9563mcrp4 <- 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 natural net
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
## convergedresultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)Matriz de confusión
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 0.9750mcrp5 <- confusionMatrix(resultado_prueba5, prueba$Species)
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 9 0
## virginica 0 1 10
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9000 1.0000
## Specificity 1.0000 1.0000 0.9500
## Pos Pred Value 1.0000 1.0000 0.9091
## Neg Pred Value 1.0000 0.9524 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3000 0.3333
## Detection Prevalence 0.3333 0.3000 0.3667
## Balanced Accuracy 1.0000 0.9500 0.9750Random forest
modelo6 <- train(Species ~ ., data = entrenamiento, method = "rf", preProcess = c("scale", "center"), trControl = trainControl(method="cv", number = 10), tuneGrid = expand.grid(mtry =c(2,4,6)))## Warning in randomForest.default(x, y, mtry = param$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$mtry, ...): invalid mtry:
## reset to within valid rangeresultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)Matriz de confusión/span>
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$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 1.0000mcrp6 <- confusionMatrix(resultado_prueba6, prueba$Species)
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.9000Resumen de resultados
resumen <- data.frame(
"svmLinear" = c(mcre1$overall["Accuracy"], mcre1$overall["Accuracy"]),
"svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"NeuralNet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"RandomForest" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resumen) <- c("Precision de entrenamiento", "Precision de prueba")
resumen## svmLinear svmRadial svmPoly rpart NeuralNet
## Precision de entrenamiento 0.9916667 0.9916667 0.9916667 0.9666667 0.9666667
## Precision de prueba 0.9916667 0.9333333 0.9666667 0.9333333 0.9666667
## RandomForest
## Precision de entrenamiento 1.0000000
## Precision de prueba 0.9333333Resultados
El modelo empleando la técnica de bosques aleatorios muestra indicios de sobreajuste, evidenciado por su elevada precisión en la fase de entrenamiento en contraste con una precisión significativamente menor durante la fase de evaluación.
Según el análisis de los datos obtenidos, el modelo más eficaz resulta ser el basado en Máquina de Vectores de Soporte Lineal.
---
title: "Machine Learning - IRIS"
author: "Gabriel Medina - A01275763"
date: "2024-02-20"
output:
  html_document:
    code_folding: hide
    toc: yes
    toc_float: yes
    code_download: yes
    theme: yeti
    highlight: tango
  pdf_document:
    toc: yes
---

![](/Users/gabrielmedina/Downloads/M2/iris.gif)



```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## <span style="color: Purple;">Teoría</span>

El paquete *caret* (Classification And REgression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.


## <span style="color: Purple;">Librerías</span>
```{r message=FALSE, warning=FALSE}

library(caret)
library(ggplot2) # Crear gráficos
library(datasets) # Usar la base de datos "Iris"
library(lattice) # Crear gráficos
library(DataExplorer)
library(mlbench)

```

## <span style="color: Purple;"> Base de datos</span>
```{r}
df <- data.frame(iris)
```


## <span style="color: Purple;">Análisis exploratorio de datos</span>
```{r}
summary(df)
str(df)
#create_report(df)
plot_missing(df)
plot_histogram(df)
plot_correlation(df)
```

La variable será factor

## <span style="color: Purple;">Partición</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: Purple;">Diferentes modelos</span>

Los métodos más utilizados para modelar aprendizaje automático son:

* **SVM**: *Support Vector Machine* o Máquina de Vectores de Soporte. Hay varios subtipos: Lineal (svmLinear), Radial (svmRadial), Polinómicos (svmPoly), etc.

* **Árbol de Decisión**: rpart

* **Redes Neuronales**: nnet

* **Random Forests** o Bosques aleatorios: rf


## <span style="color: Purple;">Lineal</span>

```{r}
modelo1 <- train(Species ~ ., data= entrenamiento, method = "svmLinear", preProcess= c("scale", "center"), trControl = trainControl(method = "cv", number = 10), tuneGrid = data.frame(C=1)) 

resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)


```

### <span style="color: Purple;">Matriz de confusión</span>

```{r}
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$Species)
mcre1 

mcrp1 <- confusionMatrix(resultado_prueba1, prueba$Species)
mcrp1
```

## <span style="color: Purple;">Modelo 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)) #Cambiar



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


```

### <span style="color: Purple;">Matriz de confusión</span>

```{r}
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$Species)
mcre2 

mcrp2 <- confusionMatrix(resultado_prueba2, prueba$Species)
mcrp2
```



## <span style="color: Purple;">Modelo svmPoly</span>
```{r}



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)) #Cambiar


resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)


```

### <span style="color: Purple;">Matriz de confusión</span>

```{r}

mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$Species)
mcre3 

mcrp3 <- confusionMatrix(resultado_prueba3, prueba$Species)
mcrp3
```


## <span style="color: Purple;">Árbol de decisión</span>
```{r}
modelo4 <- train(Species ~ ., data = entrenamiento, method = "rpart", preProcess = c("scale", "center"), trControl = trainControl(method="cv", number = 10), tuneLength = 10)

resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)


```

### <span style="color: Purple;">Matriz de confusión</span>

```{r}
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$Species)
mcre4 

mcrp4 <- confusionMatrix(resultado_prueba4, prueba$Species)
mcrp4
```


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

resultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)


```

### <span style="color: Purple;">Matriz de confusión</span>

```{r}
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$Species)
mcre5 

mcrp5 <- confusionMatrix(resultado_prueba5, prueba$Species)
mcrp5
```


## <span style="color: Purple;">Random forest</span>
```{r}
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)))

resultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)


```

### <span style="color: Purple;">Matriz de confusión/span>

```{r}

mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$Species)
mcre6 

mcrp6 <- confusionMatrix(resultado_prueba6, prueba$Species)
mcrp6
```



## <span style="color: Purple;">Resumen de resultados</span>

```{r}
resumen <- data.frame(
  
  "svmLinear" = c(mcre1$overall["Accuracy"], mcre1$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"]),
  "NeuralNet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
  "RandomForest" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
  
)

rownames(resumen) <- c("Precision de entrenamiento", "Precision de prueba")
resumen
```

## <span style="color: Purple;">Resultados</span>

El modelo empleando la técnica de bosques aleatorios muestra indicios de sobreajuste, evidenciado por su elevada precisión en la fase de entrenamiento en contraste con una precisión significativamente menor durante la fase de evaluación.

Según el análisis de los datos obtenidos, el modelo más eficaz resulta ser el basado en Máquina de Vectores de Soporte Lineal.

