Machine learning
Luis Mendoza - A00829099
2024-02-26
Teoria
El paquete caret (Clasification and Regression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automatico.
Instalar paquetes y librerias
#install.packages("ggplot2") #Graficas con mejor diseno
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.2#install.packages("lattice") #Usar la base de datos de iris
library(lattice)## Warning: package 'lattice' was built under R version 4.3.2#install.packages("caret") #Algoritmos de aprendizajea automatico
library(caret)## Warning: package 'caret' was built under R version 4.3.2#install.packages("datasets") #Usar la base de datos de iris
library(datasets)
#install.packages("DataExplorer") #Usar la base de datos de iris
library(DataExplorer)## Warning: package 'DataExplorer' was built under R version 4.3.2Crear base de datos
df <- data.frame(iris)
df## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginicaEDA
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_boxplot(df, by = "Species")
plot_histogram(df)
plot_bar(df)
** Nota: La variable que queremos predecir debe tener formato de
FACTOR**
#Partir la base de datos 80-20
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$Species, p=0.8, list=FALSE)
entrenamiento <- iris[renglones_entrenamiento, ]
prueba <- iris[renglones_entrenamiento, ]#Distintos tipos de Metodos para Modelar Los metodos mas utilizados para modelar aprendizaje automatico son:
- SVM: Support Vector Machine o Maquina de
Vectores de soporte. Hay varios subtipos: Lineal (svmLineal),
Radial(svmRadial), Polinomico(svmPoly), etc.
- Arboles de Decision: rpart
- Redes Neuronales: nnet
- Random Forest o Bosque Aleatorios: rf
1.Modelo con svmLineal
modelo <- train(Species ~ .,data = entrenamiento, method = "svmLinear",preProcess=c("scale","center"), trControl = trainControl(method="cv",number=10),
tuneGrid = data.frame(C=1) #Cuando es svmLinear
)
resultado_entrenamiento <- predict(modelo,entrenamiento)
resultado_prueba <- predict(modelo,prueba)#Matriz de Confusion
mcre <- confusionMatrix(resultado_entrenamiento, entrenamiento$Species)
mcre## 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 0.9938mcrp <- confusionMatrix(resultado_prueba, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 0
## virginica 0 1 40
##
## Overall Statistics
##
## Accuracy : 0.9917
## 95% CI : (0.9544, 0.9998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9875
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 1.0000
## Specificity 1.0000 1.0000 0.9875
## Pos Pred Value 1.0000 1.0000 0.9756
## Neg Pred Value 1.0000 0.9877 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3333
## Detection Prevalence 0.3333 0.3250 0.3417
## Balanced Accuracy 1.0000 0.9875 0.99382.Modelo con svmRadial
modelo2 <- train(Species ~ .,data = entrenamiento, method = "svmRadial",preProcess=c("scale","center"), trControl = trainControl(method="cv",number=10),
tuneGrid = data.frame(sigma=1,C=1) #Cuando es svmRadial
)
resultado_entrenamiento2 <- predict(modelo2,entrenamiento)
resultado_prueba2 <- predict(modelo2,prueba)#Matriz de Confusion
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$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$Species) #matriz de confusión del resultado de la prueba
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 0
## virginica 0 1 40
##
## Overall Statistics
##
## Accuracy : 0.9917
## 95% CI : (0.9544, 0.9998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9875
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 1.0000
## Specificity 1.0000 1.0000 0.9875
## Pos Pred Value 1.0000 1.0000 0.9756
## Neg Pred Value 1.0000 0.9877 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3333
## Detection Prevalence 0.3333 0.3250 0.3417
## Balanced Accuracy 1.0000 0.9875 0.99383.Modelo con svmPoly
modelo3 <- train(Species ~ .,data = entrenamiento, method = "svmPoly",preProcess=c("scale","center"), trControl = trainControl(method="cv",number=10),
tuneGrid = data.frame(degree=1,scale=1,C=1) #Cuando es svmPoly
)
resultado_entrenamiento3 <- predict(modelo3,entrenamiento)
resultado_prueba3 <- predict(modelo3,prueba)#Matriz de Confusion
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$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$Species) #matriz de confusión del resultado de la prueba
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 0
## virginica 0 1 40
##
## Overall Statistics
##
## Accuracy : 0.9917
## 95% CI : (0.9544, 0.9998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9875
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 1.0000
## Specificity 1.0000 1.0000 0.9875
## Pos Pred Value 1.0000 1.0000 0.9756
## Neg Pred Value 1.0000 0.9877 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3333
## Detection Prevalence 0.3333 0.3250 0.3417
## Balanced Accuracy 1.0000 0.9875 0.99384.Modelo con metodo rpart
modelo4 <- train(Species ~ .,data = entrenamiento, method = "rpart",preProcess=c("scale","center"), trControl = trainControl(method="cv",number=10),
tuneLength = 10 #Cuando es metodo rpart
)
resultado_entrenamiento4 <- predict(modelo4,entrenamiento)
resultado_prueba4 <- predict(modelo4,prueba)Matriz de Confusion
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$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) #matriz de confusión del resultado de la prueba
mcrp4## 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 0.95635.Modelo con metodo 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 41.544443
## iter 70 value 18.376424
## iter 80 value 4.629967
## iter 90 value 3.675228
## iter 100 value 3.275824
## final value 3.275824
## 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 17.281237
## iter 60 value 6.529030
## iter 70 value 3.473730
## iter 80 value 3.279187
## iter 90 value 3.156556
## iter 100 value 2.981555
## final value 2.981555
## 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 39.847983
## iter 50 value 8.016537
## iter 60 value 4.619364
## iter 70 value 2.386452
## iter 80 value 1.338165
## iter 90 value 1.178344
## iter 100 value 1.100248
## final value 1.100248
## 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 48.179024
## final value 48.179024
## 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
## convergedresultado_entrenamiento5 <- predict(modelo5,entrenamiento)
resultado_prueba5 <- predict(modelo5,prueba)#Matriz de Confusion
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$Species) #matriz de confusión del resultado de la prueba
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 36 0
## virginica 0 4 40
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.9169, 0.9908)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9000 1.0000
## Specificity 1.0000 1.0000 0.9500
## Pos Pred Value 1.0000 1.0000 0.9091
## Neg Pred Value 1.0000 0.9524 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3000 0.3333
## Detection Prevalence 0.3333 0.3000 0.3667
## Balanced Accuracy 1.0000 0.9500 0.97506.Modelo con rf
modelo6 <- train(Species ~ .,data = entrenamiento, method = "rf",preProcess=c("scale","center"), trControl = trainControl(method="cv",number=10),
tuneGrid = expand.grid(mtry =c(2,4,6)) #Cuando es rf
)## Warning in randomForest.default(x, y, mtry = param$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$mtry, ...): invalid mtry:
## reset to within valid rangeresultado_entrenamiento6 <- predict(modelo6,entrenamiento)
resultado_prueba6 <- predict(modelo6,prueba)#Matriz de Confusion
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$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$Species) #matriz de confusión del resultado de la prueba
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 40 0
## virginica 0 0 40
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9697, 1)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 1.0000
## Specificity 1.0000 1.0000 1.0000
## Pos Pred Value 1.0000 1.0000 1.0000
## Neg Pred Value 1.0000 1.0000 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3333
## Detection Prevalence 0.3333 0.3333 0.3333
## Balanced Accuracy 1.0000 1.0000 1.0000Resumen de Resultados
resultados <- data.frame(
"1. svmLinear" = c(mcre$overall["Accuracy"], mcrp$overall["Accuracy"]),
"2. svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"3. svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"4. rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"5. nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"6. rf" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Presicion de entrenamiento", "Presicion de prueba")
resultados## X1..svmLinear X2..svmRadial X3..svmPoly X4..rpart
## Presicion de entrenamiento 0.9916667 0.9916667 0.9916667 0.9666667
## Presicion de prueba 0.9916667 0.9916667 0.9916667 0.9666667
## X5..nnet X6..rf
## Presicion de entrenamiento 0.9666667 1
## Presicion de prueba 0.9666667 1Conclusiones
El modelo con el metodo de bosques aleatorios presenta sobreajuste, ya que tiene una alta presicion en entrenamiento, pero baja en prueba.
Acorde al resumen de resultados, el mejor modelo es el de Maquina de Vectores de Soporte Lineal
---
title: "Machine learning"
author: "Luis Mendoza - A00829099"
date: "2024-02-26"
output: 
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
---

![](C:\\Users\\Luis Rodriguez\\Downloads\\Flores_de_Íris.png)

# Teoria 
El paquete *caret (Clasification and Regression Training)* es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automatico.

# Instalar paquetes y librerias
```{r}
#install.packages("ggplot2") #Graficas con mejor diseno
library(ggplot2)
#install.packages("lattice") #Usar la base de datos de iris
library(lattice)
#install.packages("caret") #Algoritmos de aprendizajea automatico
library(caret)
#install.packages("datasets") #Usar la base de datos de iris
library(datasets)
#install.packages("DataExplorer") #Usar la base de datos de iris
library(DataExplorer)
```

# Crear base de datos
```{r}
df <- data.frame(iris)
df
```

# EDA 
```{r}
summary(df)
str(df)
#create_report(df)
plot_missing(df)
plot_boxplot(df, by = "Species")
plot_histogram(df)
plot_bar(df)
```
** Nota: La variable que queremos predecir debe tener formato de FACTOR**

#Partir la base de datos 80-20
```{r}
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$Species, p=0.8, list=FALSE)
entrenamiento <- iris[renglones_entrenamiento, ]
prueba <- iris[renglones_entrenamiento, ]
```

#Distintos tipos de Metodos para Modelar
Los metodos mas utilizados para modelar aprendizaje automatico son:

* **SVM**: *Support Vector Machine* o Maquina de Vectores de soporte. Hay varios subtipos: Lineal (svmLineal), Radial(svmRadial), Polinomico(svmPoly), etc.  
* **Arboles de Decision**: rpart  
* **Redes Neuronales**: nnet
* **Random Forest** o Bosque Aleatorios: rf

# 1.Modelo con svmLineal
```{r}
modelo <- train(Species ~ .,data = entrenamiento, method = "svmLinear",preProcess=c("scale","center"), trControl = trainControl(method="cv",number=10),
                tuneGrid = data.frame(C=1) #Cuando es svmLinear
                )

resultado_entrenamiento <- predict(modelo,entrenamiento)
resultado_prueba <- predict(modelo,prueba)
```

#Matriz de Confusion
```{r}
mcre <- confusionMatrix(resultado_entrenamiento, entrenamiento$Species)
mcre
mcrp <- confusionMatrix(resultado_prueba, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp
```

# 2.Modelo con svmRadial
```{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) #Cuando es svmRadial
                )

resultado_entrenamiento2 <- predict(modelo2,entrenamiento)
resultado_prueba2 <- predict(modelo2,prueba)
```
#Matriz de Confusion
```{r}
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$Species)
mcre2
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp2
```

# 3.Modelo con svmPoly
```{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) #Cuando es svmPoly
                )

resultado_entrenamiento3 <- predict(modelo3,entrenamiento)
resultado_prueba3 <- predict(modelo3,prueba)
```
#Matriz de Confusion
```{r}
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$Species)
mcre3
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp3
```

# 4.Modelo con metodo rpart
```{r}
modelo4 <- train(Species ~ .,data = entrenamiento, method = "rpart",preProcess=c("scale","center"), trControl = trainControl(method="cv",number=10),
                tuneLength = 10 #Cuando es metodo rpart
                )

resultado_entrenamiento4 <- predict(modelo4,entrenamiento)
resultado_prueba4 <- predict(modelo4,prueba)
```
# Matriz de Confusion
```{r}
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$Species)
mcre4
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp4
```
# 5.Modelo con metodo nnet
```{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)
```
#Matriz de Confusion
```{r}
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$Species)
mcre5
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp5
```

# 6.Modelo con rf
```{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)) #Cuando es rf
                )

resultado_entrenamiento6 <- predict(modelo6,entrenamiento)
resultado_prueba6 <- predict(modelo6,prueba)
```
#Matriz de Confusion
```{r}
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$Species)
mcre6
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp6
```
 
# Resumen de Resultados
```{r}
resultados <- data.frame(
  "1. svmLinear" = c(mcre$overall["Accuracy"], mcrp$overall["Accuracy"]),
  "2. svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
  "3. svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
  "4. rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
  "5. nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
  "6. rf" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Presicion de entrenamiento", "Presicion de prueba")
resultados
```

# Conclusiones

El modelo con el metodo de bosques aleatorios presenta sobreajuste, ya que tiene una alta presicion en entrenamiento, pero baja en prueba.  

Acorde al resumen de resultados, el mejor modelo es el de **Maquina de Vectores de Soporte Lineal**