Caret

Teoria

El paquete CARET (Classification and Regression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.

Instalar paquetes y llamar librerias

library(ggplot2)
#install.packages("lattice")
library(lattice)
library(caret)
library(datasets)
library(DataExplorer)
#install.packages("kernlab")
library(kernlab)
#install.packages("randomForest")
library(randomForest)

Crear base de datos

df <- data.frame(iris)

Análisis 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)

La variable que queremos predecir bede ser un 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 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), Polinómico (svmPoly), etc. * Árbol de Decisión: rpart * Redes Neuronales: nnet * Random Forest: 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)
                 )
resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)
#Matriz de confusión: resultado del entrenamiento
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$Species)
#Matriz de confusión: resultado de la prueba
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$Species)

2. 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, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)
#Matriz de confusión: resultado del entrenamiento
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$Species)
#Matriz de confusión: resultado de la prueba
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$Species)

3. Modelo con el Método 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)
                 )
resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)
#Matriz de confusión: resultado del entrenamiento
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$Species)
#Matriz de confusión: resultado de la prueba
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$Species)

4. Modelo con el Método rpart

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: resultado del entrenamiento
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$Species)
#Matriz de confusión: resultado de la prueba
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$Species)

5. 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 41.543860
## iter  70 value 18.481105
## iter  80 value 4.645715
## iter  90 value 3.798934
## iter 100 value 3.370556
## final  value 3.370556 
## 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 1.687392
## iter  60 value 1.640809
## iter  70 value 1.636953
## iter  80 value 1.613389
## iter  90 value 1.611928
## iter 100 value 1.611137
## final  value 1.611137 
## 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 3.894028
## iter  80 value 3.877352
## iter  90 value 3.868846
## 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 0.534726
## iter  80 value 0.525302
## iter  90 value 0.477461
## iter 100 value 0.468104
## final  value 0.468104 
## 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 3.354456
## iter  60 value 3.245703
## iter  70 value 3.148381
## iter  80 value 3.017232
## iter  90 value 2.916738
## iter 100 value 2.698927
## final  value 2.698927 
## 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 0.452080
## final  value 0.452080 
## 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 17.281418
## iter  60 value 6.528967
## iter  70 value 3.474412
## iter  80 value 3.279584
## iter  90 value 3.154635
## iter 100 value 2.949181
## final  value 2.949181 
## 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 1.791736
## final  value 1.791736 
## 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 0.321359
## final  value 0.321359 
## 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 39.847839
## iter  50 value 8.020015
## iter  60 value 4.613242
## iter  70 value 2.880080
## iter  80 value 1.630257
## iter  90 value 1.475646
## iter 100 value 1.383979
## final  value 1.383979 
## 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 8.129202
## iter  60 value 4.703745
## iter  70 value 4.278312
## iter  80 value 3.668066
## iter  90 value 3.605900
## iter 100 value 3.568123
## final  value 3.568123 
## 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 0.950636
## final  value 0.950636 
## 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 48.179023
## final  value 48.179023 
## 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 18.978222
## iter  80 value 6.742676
## iter  90 value 4.056469
## iter 100 value 3.922763
## final  value 3.922763 
## 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

resultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
#Matriz de confusión: resultado del entrenamiento
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$Species)
#Matriz de confusión: resultado de la prueba
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$Species)

6. Modelo con el Método rf

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)
#Matriz de confusión: resultado del entrenamiento
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$Species)
#Matriz de confusión: resultado de la prueba
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$Species)

Resumen de Resultado

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        0.9333333

Conclusiones

CARET es una librería que nos pueda ayudar a hacer diferentes análisis sobre variables que son factores aplicando diferentes tipos de entrenamiento que nos llevaran al modelo óptimo. Lo que cambiará entre ellos será la complejidad y debemos siempre elegir el que mejor se ajuste a los datos y a lo que queremos descubrir en ellos.