CARET Iris
Dulce Johana Rios Alba A00839495
2026-09-02
Teoría
El paquete CARET (Clasification And Regression Training) es un paquete integral con una amplia variedad de algoritmos paara el aprendizaje automático.
Instalar paquetes y llamar librerías
#install.packages("caret") # Algoritmos de aprendizaje automatico
library(caret)## Cargando paquete requerido: ggplot2## Cargando paquete requerido: lattice#install.packages("ggplot2") # Gáficas
library(ggplot2)
#install.packages("lattice") # Crear gráficos
library(lattice)
#install.packages("datasets") # Usar bases de datos precargadas
library(datasets)
#install.packages("DataExplorer") # Análisis Descriptivo
library(DataExplorer)
#install.packages("kernlab")
library(kernlab)##
## Adjuntando el paquete: 'kernlab'## The following object is masked from 'package:ggplot2':
##
## alpha#install.packages("randomForest")
library(randomForest)## randomForest 4.7-1.2## Type rfNews() to see new features/changes/bug fixes.##
## Adjuntando el paquete: 'randomForest'## The following object is masked from 'package:ggplot2':
##
## margin#install.packages("readxl")
library(readxl)Cargar la base de datos
df <- read_excel("C:/Users/dulce/OneDrive/Escritorio/IA Empresarial/heart.xlsx")Entender la base de datos
summary(df)## age sex cp trestbps
## Min. :29.00 Min. :0.0000 Min. :0.0000 Min. : 94.0
## 1st Qu.:48.00 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:120.0
## Median :56.00 Median :1.0000 Median :1.0000 Median :130.0
## Mean :54.43 Mean :0.6956 Mean :0.9424 Mean :131.6
## 3rd Qu.:61.00 3rd Qu.:1.0000 3rd Qu.:2.0000 3rd Qu.:140.0
## Max. :77.00 Max. :1.0000 Max. :3.0000 Max. :200.0
## chol fbs restecg thalach
## Min. :126 Min. :0.0000 Min. :0.0000 Min. : 71.0
## 1st Qu.:211 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:132.0
## Median :240 Median :0.0000 Median :1.0000 Median :152.0
## Mean :246 Mean :0.1493 Mean :0.5298 Mean :149.1
## 3rd Qu.:275 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:166.0
## Max. :564 Max. :1.0000 Max. :2.0000 Max. :202.0
## exang oldpeak slope ca
## Min. :0.0000 Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:1.000 1st Qu.:0.0000
## Median :0.0000 Median :0.800 Median :1.000 Median :0.0000
## Mean :0.3366 Mean :1.072 Mean :1.385 Mean :0.7541
## 3rd Qu.:1.0000 3rd Qu.:1.800 3rd Qu.:2.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :6.200 Max. :2.000 Max. :4.0000
## thal target
## Min. :0.000 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:0.0000
## Median :2.000 Median :1.0000
## Mean :2.324 Mean :0.5132
## 3rd Qu.:3.000 3rd Qu.:1.0000
## Max. :3.000 Max. :1.0000str(df)## tibble [1,025 × 14] (S3: tbl_df/tbl/data.frame)
## $ age : num [1:1025] 52 53 70 61 62 58 58 55 46 54 ...
## $ sex : num [1:1025] 1 1 1 1 0 0 1 1 1 1 ...
## $ cp : num [1:1025] 0 0 0 0 0 0 0 0 0 0 ...
## $ trestbps: num [1:1025] 125 140 145 148 138 100 114 160 120 122 ...
## $ chol : num [1:1025] 212 203 174 203 294 248 318 289 249 286 ...
## $ fbs : num [1:1025] 0 1 0 0 1 0 0 0 0 0 ...
## $ restecg : num [1:1025] 1 0 1 1 1 0 2 0 0 0 ...
## $ thalach : num [1:1025] 168 155 125 161 106 122 140 145 144 116 ...
## $ exang : num [1:1025] 0 1 1 0 0 0 0 1 0 1 ...
## $ oldpeak : num [1:1025] 1 3.1 2.6 0 1.9 1 4.4 0.8 0.8 3.2 ...
## $ slope : num [1:1025] 2 0 0 2 1 1 0 1 2 1 ...
## $ ca : num [1:1025] 2 0 0 1 3 0 3 1 0 2 ...
## $ thal : num [1:1025] 3 3 3 3 2 2 1 3 3 2 ...
## $ target : num [1:1025] 0 0 0 0 0 1 0 0 0 0 ...# create report(df)
plot_missing(df)
plot_histogram(df)
plot_correlation(df)
df$target <- as.factor(df$target)NOTA: En modelos de clasificación, la variable que queremos predecir debe de tener formato de FACTOR
Partir la base
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$target, p=0.8, list=FALSE)
entrenamiento <- df[renglones_entrenamiento, ]
prueba <- df[-renglones_entrenamiento, ]Distintos tipos de Métodos para Modelar
Los métodos más utilizados para modelar aprendizaje automático son:
- SVM: Support Vector Machine o Máquina de
Vectores de Soporte. Hay varios subtipos: Lineal (svmLinear), Radial
(svRadial), Polinómico (svmPoly), etc.
- Árbol de Decisión: rpart
- Redes Neuronales: nnet
- Random Forest o Bosques Aleatorios: rf
Modelo 1. SVM Lineal
modelo1 <- train(target~., data=entrenamiento,
method="svmLinear", #se cambia metodo para cambiar modelo
preProcess=c("scale", "center"),
trControl= trainControl(method = "CV", number = 10),
tuneGride = data.frame(c=1) #se pueden cambiar parametros
)
resultado_entrenamiento1 <- predict(modelo1,entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificaciión
# Matriz de Confusión del Resultado del Entrenamiento
mcre1 <- confusionMatrix(resultado_entrenamiento1,entrenamiento$target)
mcre1## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 301 37
## 1 99 384
##
## Accuracy : 0.8343
## 95% CI : (0.8071, 0.8592)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6672
##
## Mcnemar's Test P-Value : 1.689e-07
##
## Sensitivity : 0.7525
## Specificity : 0.9121
## Pos Pred Value : 0.8905
## Neg Pred Value : 0.7950
## Prevalence : 0.4872
## Detection Rate : 0.3666
## Detection Prevalence : 0.4117
## Balanced Accuracy : 0.8323
##
## 'Positive' Class : 0
## # Matriz de Confusión del Resultado de la Prueba
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$target)
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 78 10
## 1 21 95
##
## Accuracy : 0.848
## 95% CI : (0.7913, 0.8944)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.6948
##
## Mcnemar's Test P-Value : 0.07249
##
## Sensitivity : 0.7879
## Specificity : 0.9048
## Pos Pred Value : 0.8864
## Neg Pred Value : 0.8190
## Prevalence : 0.4853
## Detection Rate : 0.3824
## Detection Prevalence : 0.4314
## Balanced Accuracy : 0.8463
##
## 'Positive' Class : 0
## Modelo 2. SVM Radial
modelo2 <- train( target ~ .,data = entrenamiento,
method = "svmRadial",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number = 10),
tuneGrid = data.frame(sigma=1,C = 1)
)
resultado_entrenamiento2 <- predict(modelo2,entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificaciión
# Matriz de Confusión del Resultado del Entrenamiento
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$target)
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 400 0
## 1 0 421
##
## Accuracy : 1
## 95% CI : (0.9955, 1)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.4872
## Detection Rate : 0.4872
## Detection Prevalence : 0.4872
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : 0
## # Matriz de Confusión del Resultado de la Prueba
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$target)
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 99 0
## 1 0 105
##
## Accuracy : 1
## 95% CI : (0.9821, 1)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.4853
## Detection Rate : 0.4853
## Detection Prevalence : 0.4853
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : 0
## Modelo 3. SVM Polinómico
modelo3 <- train( target ~ .,data = entrenamiento,
method = "svmPoly",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number = 10),
tuneGride = data.frame(degree=1, scale=1, C = 1) #Cambiar
)
resultado_entrenamiento3 <- predict(modelo3,entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificaciión
# Matriz de Confusión del Resultado del Entrenamiento
mcre3 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$target)
mcre3## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 400 0
## 1 0 421
##
## Accuracy : 1
## 95% CI : (0.9955, 1)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.4872
## Detection Rate : 0.4872
## Detection Prevalence : 0.4872
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : 0
## # Matriz de Confusión del Resultado de la Prueba
mcrp3 <- confusionMatrix(resultado_prueba2, prueba$target)
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 99 0
## 1 0 105
##
## Accuracy : 1
## 95% CI : (0.9821, 1)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.4853
## Detection Rate : 0.4853
## Detection Prevalence : 0.4853
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : 0
## Modelo 4. Árbol de Decisión
modelo4 <- train(target~., data=entrenamiento,
method="rpart", #se cambia metodo para cambiar modelo
preProcess=c("scale", "center"),
trControl= trainControl(method = "CV", number = 10),
tuneLength = 10 #se pueden cambiar parametros
)
resultado_entrenamiento4 <- predict(modelo4,entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificaciión
# Matriz de Confusión del Resultado del Entrenamiento
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$target)
mcre4## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 361 23
## 1 39 398
##
## Accuracy : 0.9245
## 95% CI : (0.9042, 0.9416)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.8487
##
## Mcnemar's Test P-Value : 0.05678
##
## Sensitivity : 0.9025
## Specificity : 0.9454
## Pos Pred Value : 0.9401
## Neg Pred Value : 0.9108
## Prevalence : 0.4872
## Detection Rate : 0.4397
## Detection Prevalence : 0.4677
## Balanced Accuracy : 0.9239
##
## 'Positive' Class : 0
## # Matriz de Confusión del Resultado de la Prueba
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$target)
mcrp4## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 82 7
## 1 17 98
##
## Accuracy : 0.8824
## 95% CI : (0.83, 0.9231)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.7638
##
## Mcnemar's Test P-Value : 0.06619
##
## Sensitivity : 0.8283
## Specificity : 0.9333
## Pos Pred Value : 0.9213
## Neg Pred Value : 0.8522
## Prevalence : 0.4853
## Detection Rate : 0.4020
## Detection Prevalence : 0.4363
## Balanced Accuracy : 0.8808
##
## 'Positive' Class : 0
## Modelo 5. Bosques Aleatorios
modelo5 <- train(target~., data=entrenamiento,
method="rf", #se cambia metodo para cambiar modelo
preProcess=c("scale", "center"),
trControl= trainControl(method = "CV", number = 10),
tuneGrid = expand.grid(mtry=c(2,4,6)) #se pueden cambiar parametros
)
resultado_entrenamiento5 <- predict(modelo5,entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificaciión
# Matriz de Confusión del Resultado del Entrenamiento
mcre5 <- confusionMatrix(resultado_entrenamiento5,entrenamiento$target)
mcre5## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 400 0
## 1 0 421
##
## Accuracy : 1
## 95% CI : (0.9955, 1)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.4872
## Detection Rate : 0.4872
## Detection Prevalence : 0.4872
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : 0
## # Matriz de Confusión del Resultado de la Prueba
mcrp5 <- confusionMatrix(resultado_prueba5,prueba$target)
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 99 0
## 1 0 105
##
## Accuracy : 1
## 95% CI : (0.9821, 1)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.0000
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 1.0000
## Prevalence : 0.4853
## Detection Rate : 0.4853
## Detection Prevalence : 0.4853
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : 0
## Modelo 6. Redes Neuronales
modelo6 <- train(target~., data=entrenamiento,
method="nnet", #se cambia metodo para cambiar modelo
preProcess=c("scale", "center"),
trControl= trainControl(method = "CV", number = 10)
#se pueden cambiar parametros
)## # weights: 16
## initial value 479.323995
## iter 10 value 281.094620
## iter 20 value 266.097422
## iter 30 value 263.164024
## iter 40 value 262.869027
## iter 50 value 262.691724
## iter 60 value 262.430851
## iter 70 value 262.360300
## iter 80 value 262.356188
## iter 90 value 262.341163
## iter 100 value 262.310302
## final value 262.310302
## stopped after 100 iterations
## # weights: 46
## initial value 576.606727
## iter 10 value 245.601378
## iter 20 value 207.598271
## iter 30 value 176.962840
## iter 40 value 169.341552
## iter 50 value 160.435924
## iter 60 value 159.243849
## iter 70 value 159.233665
## iter 80 value 159.223644
## iter 90 value 159.158245
## iter 100 value 159.105082
## final value 159.105082
## stopped after 100 iterations
## # weights: 76
## initial value 514.573372
## iter 10 value 221.494580
## iter 20 value 158.350078
## iter 30 value 123.980193
## iter 40 value 106.517787
## iter 50 value 92.102000
## iter 60 value 83.023293
## iter 70 value 80.502763
## iter 80 value 80.354804
## iter 90 value 80.348850
## iter 100 value 80.347398
## final value 80.347398
## stopped after 100 iterations
## # weights: 16
## initial value 519.240949
## iter 10 value 343.013541
## iter 20 value 301.460505
## iter 30 value 269.828379
## iter 40 value 269.597902
## iter 50 value 269.461076
## final value 269.424526
## converged
## # weights: 46
## initial value 563.523812
## iter 10 value 316.292946
## iter 20 value 237.337085
## iter 30 value 207.108728
## iter 40 value 198.785210
## iter 50 value 196.698272
## iter 60 value 195.725048
## iter 70 value 195.455886
## iter 80 value 195.455148
## final value 195.455143
## converged
## # weights: 76
## initial value 561.426541
## iter 10 value 268.577672
## iter 20 value 204.257074
## iter 30 value 166.620173
## iter 40 value 156.940203
## iter 50 value 152.499353
## iter 60 value 146.384744
## iter 70 value 144.012989
## iter 80 value 141.543079
## iter 90 value 132.477215
## iter 100 value 130.377243
## final value 130.377243
## stopped after 100 iterations
## # weights: 16
## initial value 532.605918
## iter 10 value 319.764807
## iter 20 value 267.665237
## iter 30 value 264.292536
## iter 40 value 263.291254
## iter 50 value 258.992091
## iter 60 value 250.146665
## iter 70 value 249.836945
## iter 80 value 249.665013
## iter 90 value 249.618013
## iter 100 value 249.617296
## final value 249.617296
## stopped after 100 iterations
## # weights: 46
## initial value 514.607875
## iter 10 value 269.253157
## iter 20 value 203.495964
## iter 30 value 169.037334
## iter 40 value 154.058898
## iter 50 value 149.287408
## iter 60 value 145.761651
## iter 70 value 144.624718
## iter 80 value 143.863877
## iter 90 value 143.255866
## iter 100 value 143.043620
## final value 143.043620
## stopped after 100 iterations
## # weights: 76
## initial value 630.693860
## iter 10 value 224.369961
## iter 20 value 131.654723
## iter 30 value 113.823429
## iter 40 value 106.316220
## iter 50 value 102.423388
## iter 60 value 99.930122
## iter 70 value 99.293405
## iter 80 value 98.508003
## iter 90 value 97.894697
## iter 100 value 97.422338
## final value 97.422338
## stopped after 100 iterations
## # weights: 16
## initial value 531.883367
## iter 10 value 374.578692
## iter 20 value 295.542048
## iter 30 value 262.596408
## iter 40 value 261.859582
## iter 50 value 260.576573
## iter 60 value 259.059402
## iter 70 value 258.701344
## iter 80 value 258.666543
## iter 90 value 258.560648
## iter 100 value 258.517423
## final value 258.517423
## stopped after 100 iterations
## # weights: 46
## initial value 481.312877
## iter 10 value 238.116260
## iter 20 value 196.813857
## iter 30 value 172.214256
## iter 40 value 157.575733
## iter 50 value 154.947409
## iter 60 value 154.927746
## final value 154.927698
## converged
## # weights: 76
## initial value 520.415569
## iter 10 value 219.490627
## iter 20 value 158.050979
## iter 30 value 117.315828
## iter 40 value 102.724198
## iter 50 value 98.119898
## iter 60 value 96.983485
## iter 70 value 96.823212
## iter 80 value 96.819185
## iter 90 value 96.816955
## final value 96.816837
## converged
## # weights: 16
## initial value 527.147093
## iter 10 value 278.801823
## iter 20 value 265.758838
## iter 30 value 262.626293
## iter 40 value 262.612655
## iter 40 value 262.612653
## iter 40 value 262.612653
## final value 262.612653
## converged
## # weights: 46
## initial value 541.963470
## iter 10 value 290.865796
## iter 20 value 254.512116
## iter 30 value 243.383522
## iter 40 value 222.832206
## iter 50 value 203.969757
## iter 60 value 191.367815
## iter 70 value 182.044271
## iter 80 value 179.826872
## iter 90 value 179.340183
## iter 100 value 179.286464
## final value 179.286464
## stopped after 100 iterations
## # weights: 76
## initial value 705.394127
## iter 10 value 236.709587
## iter 20 value 193.346257
## iter 30 value 169.773658
## iter 40 value 156.568339
## iter 50 value 145.206150
## iter 60 value 138.182905
## iter 70 value 134.243962
## iter 80 value 131.007489
## iter 90 value 127.761127
## iter 100 value 124.929389
## final value 124.929389
## stopped after 100 iterations
## # weights: 16
## initial value 518.776586
## iter 10 value 277.220066
## iter 20 value 263.552638
## iter 30 value 259.604008
## iter 40 value 258.765646
## iter 50 value 258.702162
## iter 60 value 258.567297
## iter 70 value 258.548887
## iter 80 value 258.547661
## iter 90 value 258.547470
## iter 90 value 258.547469
## iter 90 value 258.547469
## final value 258.547469
## converged
## # weights: 46
## initial value 530.289763
## iter 10 value 274.799504
## iter 20 value 237.577864
## iter 30 value 201.273543
## iter 40 value 173.204805
## iter 50 value 158.526485
## iter 60 value 156.785567
## iter 70 value 156.596735
## iter 80 value 156.370472
## iter 90 value 155.992033
## iter 100 value 155.315053
## final value 155.315053
## stopped after 100 iterations
## # weights: 76
## initial value 534.038038
## iter 10 value 222.564685
## iter 20 value 147.489557
## iter 30 value 102.774571
## iter 40 value 93.713045
## iter 50 value 91.763100
## iter 60 value 91.236508
## iter 70 value 90.973289
## iter 80 value 90.942756
## iter 90 value 90.926177
## iter 100 value 90.913772
## final value 90.913772
## stopped after 100 iterations
## # weights: 16
## initial value 529.838949
## iter 10 value 287.472309
## iter 20 value 261.577357
## iter 30 value 247.493272
## iter 40 value 239.756994
## iter 50 value 238.787366
## iter 60 value 238.320164
## iter 70 value 236.725613
## iter 80 value 236.120563
## iter 90 value 235.813525
## iter 100 value 235.717103
## final value 235.717103
## stopped after 100 iterations
## # weights: 46
## initial value 529.263063
## iter 10 value 239.248412
## iter 20 value 202.512264
## iter 30 value 183.138696
## iter 40 value 165.175903
## iter 50 value 144.860547
## iter 60 value 133.229658
## iter 70 value 130.727048
## iter 80 value 130.662585
## iter 90 value 130.658542
## final value 130.658516
## converged
## # weights: 76
## initial value 561.852776
## iter 10 value 243.019494
## iter 20 value 169.184088
## iter 30 value 129.871524
## iter 40 value 122.058893
## iter 50 value 118.327378
## iter 60 value 115.510727
## iter 70 value 114.226455
## iter 80 value 113.791439
## iter 90 value 113.745458
## iter 100 value 113.727913
## final value 113.727913
## stopped after 100 iterations
## # weights: 16
## initial value 562.080095
## iter 10 value 287.398809
## iter 20 value 268.899882
## iter 30 value 267.921805
## final value 267.905113
## converged
## # weights: 46
## initial value 577.189429
## iter 10 value 225.363533
## iter 20 value 200.040477
## iter 30 value 189.424196
## iter 40 value 176.333329
## iter 50 value 169.755557
## iter 60 value 167.422543
## iter 70 value 166.964593
## iter 80 value 166.549095
## iter 90 value 165.841142
## iter 100 value 165.717775
## final value 165.717775
## stopped after 100 iterations
## # weights: 76
## initial value 537.237848
## iter 10 value 242.347336
## iter 20 value 194.874893
## iter 30 value 160.795978
## iter 40 value 142.568748
## iter 50 value 132.823309
## iter 60 value 129.882449
## iter 70 value 128.479945
## iter 80 value 127.906554
## iter 90 value 127.871063
## iter 100 value 127.869349
## final value 127.869349
## stopped after 100 iterations
## # weights: 16
## initial value 566.091077
## iter 10 value 315.542837
## iter 20 value 281.779519
## iter 30 value 274.842688
## iter 40 value 271.801253
## iter 50 value 256.296129
## iter 60 value 254.050256
## iter 70 value 254.042144
## iter 80 value 254.034415
## final value 254.032443
## converged
## # weights: 46
## initial value 594.937549
## iter 10 value 299.934192
## iter 20 value 215.325479
## iter 30 value 193.954647
## iter 40 value 177.299312
## iter 50 value 164.179887
## iter 60 value 162.097352
## iter 70 value 159.095876
## iter 80 value 156.521976
## iter 90 value 155.348777
## iter 100 value 153.105664
## final value 153.105664
## stopped after 100 iterations
## # weights: 76
## initial value 496.489178
## iter 10 value 204.204626
## iter 20 value 134.031405
## iter 30 value 118.007212
## iter 40 value 112.258582
## iter 50 value 106.658386
## iter 60 value 106.346645
## iter 70 value 106.132061
## iter 80 value 105.895077
## iter 90 value 105.750048
## iter 100 value 105.454938
## final value 105.454938
## stopped after 100 iterations
## # weights: 16
## initial value 514.132003
## iter 10 value 315.511971
## iter 20 value 260.808178
## iter 30 value 252.781711
## iter 40 value 251.359841
## iter 50 value 244.662458
## iter 60 value 244.266300
## final value 244.265737
## converged
## # weights: 46
## initial value 535.229407
## iter 10 value 271.557251
## iter 20 value 200.959510
## iter 30 value 168.204356
## iter 40 value 158.529623
## iter 50 value 151.026184
## iter 60 value 146.038276
## iter 70 value 144.728827
## iter 80 value 144.556814
## iter 90 value 144.490421
## iter 100 value 144.466191
## final value 144.466191
## stopped after 100 iterations
## # weights: 76
## initial value 580.681103
## iter 10 value 216.449866
## iter 20 value 144.019661
## iter 30 value 95.847380
## iter 40 value 76.319858
## iter 50 value 67.800412
## iter 60 value 67.494853
## iter 70 value 67.484987
## iter 80 value 67.483560
## iter 90 value 67.482936
## iter 100 value 67.482684
## final value 67.482684
## stopped after 100 iterations
## # weights: 16
## initial value 592.896015
## iter 10 value 301.253449
## iter 20 value 276.439575
## iter 30 value 269.913844
## iter 40 value 268.665755
## iter 50 value 268.584324
## final value 268.570269
## converged
## # weights: 46
## initial value 526.727258
## iter 10 value 266.478529
## iter 20 value 229.985375
## iter 30 value 213.070432
## iter 40 value 206.957167
## iter 50 value 198.429177
## iter 60 value 183.557217
## iter 70 value 175.011389
## iter 80 value 173.071808
## iter 90 value 172.817305
## iter 100 value 172.809721
## final value 172.809721
## stopped after 100 iterations
## # weights: 76
## initial value 563.234488
## iter 10 value 241.146489
## iter 20 value 194.822147
## iter 30 value 166.158168
## iter 40 value 156.278668
## iter 50 value 145.410342
## iter 60 value 130.600823
## iter 70 value 123.247016
## iter 80 value 120.133034
## iter 90 value 117.533894
## iter 100 value 111.992229
## final value 111.992229
## stopped after 100 iterations
## # weights: 16
## initial value 532.857433
## iter 10 value 285.262647
## iter 20 value 265.502978
## iter 30 value 256.637823
## iter 40 value 255.153012
## iter 50 value 253.143235
## iter 60 value 252.602000
## iter 70 value 245.716903
## iter 80 value 244.881637
## iter 90 value 244.868268
## final value 244.867919
## converged
## # weights: 46
## initial value 528.478166
## iter 10 value 255.888368
## iter 20 value 212.069594
## iter 30 value 184.804858
## iter 40 value 176.898926
## iter 50 value 168.163435
## iter 60 value 164.342288
## iter 70 value 164.202701
## iter 80 value 164.103704
## iter 90 value 163.962987
## iter 100 value 163.680429
## final value 163.680429
## stopped after 100 iterations
## # weights: 76
## initial value 627.800777
## iter 10 value 345.533864
## iter 20 value 258.272383
## iter 30 value 170.896768
## iter 40 value 104.486395
## iter 50 value 74.104175
## iter 60 value 63.633314
## iter 70 value 58.523124
## iter 80 value 56.537365
## iter 90 value 51.265459
## iter 100 value 47.848623
## final value 47.848623
## stopped after 100 iterations
## # weights: 16
## initial value 537.208464
## iter 10 value 312.961584
## iter 20 value 276.829942
## iter 30 value 272.497716
## iter 40 value 268.435549
## iter 50 value 258.001721
## iter 60 value 257.869974
## final value 257.869847
## converged
## # weights: 46
## initial value 511.618448
## iter 10 value 245.352316
## iter 20 value 212.714573
## iter 30 value 184.610891
## iter 40 value 161.676612
## iter 50 value 148.457938
## iter 60 value 143.668949
## iter 70 value 143.462728
## iter 80 value 143.442018
## iter 90 value 143.227651
## iter 100 value 143.226006
## final value 143.226006
## stopped after 100 iterations
## # weights: 76
## initial value 536.158271
## iter 10 value 218.323948
## iter 20 value 159.775741
## iter 30 value 134.025294
## iter 40 value 122.517043
## iter 50 value 112.661024
## iter 60 value 109.253109
## iter 70 value 108.553817
## iter 80 value 108.544012
## iter 90 value 108.543611
## final value 108.543602
## converged
## # weights: 16
## initial value 512.791205
## iter 10 value 288.253355
## iter 20 value 273.295951
## iter 30 value 270.197403
## iter 40 value 269.870283
## iter 50 value 269.762331
## iter 60 value 268.443632
## iter 70 value 267.646836
## final value 267.642546
## converged
## # weights: 46
## initial value 490.200816
## iter 10 value 263.282365
## iter 20 value 220.354707
## iter 30 value 196.758552
## iter 40 value 191.398868
## iter 50 value 190.520169
## iter 60 value 190.345012
## iter 70 value 190.313492
## final value 190.313463
## converged
## # weights: 76
## initial value 518.533720
## iter 10 value 238.635355
## iter 20 value 189.014455
## iter 30 value 153.670317
## iter 40 value 135.980670
## iter 50 value 128.894757
## iter 60 value 125.497160
## iter 70 value 124.197872
## iter 80 value 122.642718
## iter 90 value 122.098856
## iter 100 value 121.948875
## final value 121.948875
## stopped after 100 iterations
## # weights: 16
## initial value 589.126589
## iter 10 value 362.958252
## iter 20 value 270.152736
## iter 30 value 261.629098
## iter 40 value 261.481850
## iter 50 value 261.418891
## iter 60 value 261.295381
## iter 70 value 261.269401
## iter 70 value 261.269399
## final value 261.269245
## converged
## # weights: 46
## initial value 561.035545
## iter 10 value 247.480764
## iter 20 value 190.939766
## iter 30 value 165.278781
## iter 40 value 157.401520
## iter 50 value 146.580429
## iter 60 value 145.437498
## iter 70 value 145.091433
## iter 80 value 144.348494
## iter 90 value 143.986925
## iter 100 value 143.694710
## final value 143.694710
## stopped after 100 iterations
## # weights: 76
## initial value 573.003064
## iter 10 value 219.269925
## iter 20 value 136.899443
## iter 30 value 106.440135
## iter 40 value 92.836655
## iter 50 value 88.457277
## iter 60 value 85.071448
## iter 70 value 81.383835
## iter 80 value 79.662076
## iter 90 value 79.070265
## iter 100 value 78.888587
## final value 78.888587
## stopped after 100 iterations
## # weights: 16
## initial value 505.808968
## iter 10 value 257.652950
## iter 20 value 247.215652
## iter 30 value 231.600340
## iter 40 value 231.148056
## iter 50 value 231.142935
## iter 60 value 231.138845
## final value 231.137577
## converged
## # weights: 46
## initial value 630.632737
## iter 10 value 264.986084
## iter 20 value 211.242727
## iter 30 value 188.571426
## iter 40 value 166.329370
## iter 50 value 157.777528
## iter 60 value 157.392684
## iter 70 value 157.389595
## final value 157.389591
## converged
## # weights: 76
## initial value 515.399720
## iter 10 value 204.772626
## iter 20 value 135.901691
## iter 30 value 92.631119
## iter 40 value 71.046045
## iter 50 value 64.287345
## iter 60 value 62.648911
## iter 70 value 62.008254
## iter 80 value 61.093677
## iter 90 value 60.430869
## iter 100 value 60.079144
## final value 60.079144
## stopped after 100 iterations
## # weights: 16
## initial value 527.795133
## iter 10 value 290.631413
## iter 20 value 266.438590
## iter 30 value 259.430456
## iter 40 value 259.406328
## final value 259.406297
## converged
## # weights: 46
## initial value 545.042938
## iter 10 value 245.253415
## iter 20 value 210.956199
## iter 30 value 203.224383
## iter 40 value 201.266565
## iter 50 value 198.084918
## iter 60 value 193.522732
## iter 70 value 187.363693
## iter 80 value 180.494100
## iter 90 value 175.768305
## iter 100 value 174.505369
## final value 174.505369
## stopped after 100 iterations
## # weights: 76
## initial value 531.826145
## iter 10 value 343.048712
## iter 20 value 228.581573
## iter 30 value 196.747767
## iter 40 value 185.313629
## iter 50 value 173.813093
## iter 60 value 164.137393
## iter 70 value 159.035802
## iter 80 value 154.873527
## iter 90 value 153.335641
## iter 100 value 152.779474
## final value 152.779474
## stopped after 100 iterations
## # weights: 16
## initial value 544.654254
## iter 10 value 267.652772
## iter 20 value 257.843158
## iter 30 value 255.185977
## iter 40 value 247.725631
## iter 50 value 243.693943
## iter 60 value 241.512469
## iter 70 value 238.338396
## iter 80 value 238.135970
## iter 90 value 238.089580
## final value 237.914707
## converged
## # weights: 46
## initial value 522.483917
## iter 10 value 276.764635
## iter 20 value 215.254964
## iter 30 value 189.781182
## iter 40 value 173.941259
## iter 50 value 152.401781
## iter 60 value 144.277755
## iter 70 value 143.841431
## iter 80 value 143.585440
## iter 90 value 143.519492
## iter 100 value 143.442614
## final value 143.442614
## stopped after 100 iterations
## # weights: 76
## initial value 603.697432
## iter 10 value 305.940201
## iter 20 value 164.707637
## iter 30 value 135.087575
## iter 40 value 110.688248
## iter 50 value 79.675824
## iter 60 value 63.319931
## iter 70 value 51.610374
## iter 80 value 44.286748
## iter 90 value 38.552639
## iter 100 value 36.092398
## final value 36.092398
## stopped after 100 iterations
## # weights: 16
## initial value 521.723042
## iter 10 value 338.018428
## iter 20 value 266.719536
## iter 30 value 259.722176
## iter 40 value 252.725203
## iter 50 value 252.366509
## iter 60 value 249.228038
## iter 70 value 248.332764
## iter 80 value 240.840111
## final value 240.719301
## converged
## # weights: 46
## initial value 496.616858
## iter 10 value 314.324653
## iter 20 value 227.535974
## iter 30 value 197.539380
## iter 40 value 175.062288
## iter 50 value 157.768868
## iter 60 value 149.207887
## iter 70 value 143.208025
## iter 80 value 143.081951
## final value 143.081194
## converged
## # weights: 76
## initial value 554.331172
## iter 10 value 241.189864
## iter 20 value 162.007178
## iter 30 value 112.807809
## iter 40 value 79.004963
## iter 50 value 63.835878
## iter 60 value 58.082303
## iter 70 value 56.610539
## iter 80 value 55.392274
## iter 90 value 55.241657
## iter 100 value 55.226076
## final value 55.226076
## stopped after 100 iterations
## # weights: 16
## initial value 573.852513
## iter 10 value 420.372549
## iter 20 value 296.195614
## iter 30 value 269.998578
## iter 40 value 269.927703
## iter 50 value 269.913774
## final value 269.911491
## converged
## # weights: 46
## initial value 480.218901
## iter 10 value 232.871998
## iter 20 value 206.833485
## iter 30 value 189.882646
## iter 40 value 186.547527
## iter 50 value 185.936429
## iter 60 value 184.749961
## iter 70 value 184.159300
## iter 80 value 184.156523
## final value 184.156517
## converged
## # weights: 76
## initial value 545.096277
## iter 10 value 252.242812
## iter 20 value 190.193180
## iter 30 value 167.817507
## iter 40 value 153.055099
## iter 50 value 148.158622
## iter 60 value 145.117124
## iter 70 value 143.168515
## iter 80 value 142.977220
## iter 90 value 142.811244
## iter 100 value 141.907019
## final value 141.907019
## stopped after 100 iterations
## # weights: 16
## initial value 509.395473
## iter 10 value 266.451483
## iter 20 value 256.483142
## iter 30 value 249.594329
## iter 40 value 249.405039
## iter 50 value 249.303159
## iter 60 value 249.297745
## iter 70 value 249.260624
## iter 80 value 249.256753
## iter 90 value 249.256006
## iter 100 value 249.244035
## final value 249.244035
## stopped after 100 iterations
## # weights: 46
## initial value 516.412380
## iter 10 value 255.217894
## iter 20 value 211.933261
## iter 30 value 193.652621
## iter 40 value 183.327089
## iter 50 value 171.240703
## iter 60 value 164.303422
## iter 70 value 163.774873
## iter 80 value 163.660806
## iter 90 value 163.432542
## iter 100 value 163.217015
## final value 163.217015
## stopped after 100 iterations
## # weights: 76
## initial value 545.229421
## iter 10 value 238.747021
## iter 20 value 157.987694
## iter 30 value 100.628384
## iter 40 value 86.892449
## iter 50 value 82.475188
## iter 60 value 81.030198
## iter 70 value 79.343621
## iter 80 value 78.900438
## iter 90 value 77.104322
## iter 100 value 76.882348
## final value 76.882348
## stopped after 100 iterations
## # weights: 16
## initial value 511.479290
## iter 10 value 324.974368
## iter 20 value 267.480321
## iter 30 value 262.674574
## iter 40 value 250.987445
## iter 50 value 249.527959
## iter 60 value 243.512545
## final value 243.124045
## converged
## # weights: 46
## initial value 553.751897
## iter 10 value 241.880973
## iter 20 value 208.142057
## iter 30 value 190.770969
## iter 40 value 174.243983
## iter 50 value 162.006115
## iter 60 value 161.790300
## final value 161.789058
## converged
## # weights: 76
## initial value 549.419223
## iter 10 value 208.009560
## iter 20 value 162.484257
## iter 30 value 119.443258
## iter 40 value 92.902334
## iter 50 value 80.387775
## iter 60 value 68.385355
## iter 70 value 65.560818
## iter 80 value 63.558919
## iter 90 value 62.317037
## iter 100 value 61.769609
## final value 61.769609
## stopped after 100 iterations
## # weights: 16
## initial value 509.486166
## iter 10 value 304.000637
## iter 20 value 282.909614
## iter 30 value 272.846314
## iter 40 value 271.081949
## iter 50 value 270.790226
## final value 270.758212
## converged
## # weights: 46
## initial value 540.581776
## iter 10 value 275.162107
## iter 20 value 236.327758
## iter 30 value 223.169347
## iter 40 value 216.765423
## iter 50 value 215.929940
## iter 60 value 215.833860
## iter 70 value 215.821109
## final value 215.821080
## converged
## # weights: 76
## initial value 518.620888
## iter 10 value 233.460411
## iter 20 value 169.291554
## iter 30 value 151.865637
## iter 40 value 132.295050
## iter 50 value 116.280345
## iter 60 value 107.244506
## iter 70 value 104.148443
## iter 80 value 103.159974
## iter 90 value 101.929647
## iter 100 value 100.290992
## final value 100.290992
## stopped after 100 iterations
## # weights: 16
## initial value 510.830535
## iter 10 value 267.705760
## iter 20 value 266.329867
## iter 30 value 265.383025
## iter 40 value 264.657327
## iter 50 value 264.411568
## iter 60 value 263.756599
## iter 70 value 263.636739
## iter 80 value 263.626470
## iter 90 value 263.591147
## iter 100 value 263.564573
## final value 263.564573
## stopped after 100 iterations
## # weights: 46
## initial value 523.529754
## iter 10 value 232.664687
## iter 20 value 197.317218
## iter 30 value 169.865639
## iter 40 value 161.650453
## iter 50 value 151.745712
## iter 60 value 145.804326
## iter 70 value 145.177672
## iter 80 value 145.054912
## iter 90 value 144.971424
## iter 100 value 144.485505
## final value 144.485505
## stopped after 100 iterations
## # weights: 76
## initial value 648.167951
## iter 10 value 228.183378
## iter 20 value 167.384377
## iter 30 value 141.298931
## iter 40 value 114.430134
## iter 50 value 100.664861
## iter 60 value 98.235539
## iter 70 value 97.952655
## iter 80 value 97.775710
## iter 90 value 97.664210
## iter 100 value 97.546565
## final value 97.546565
## stopped after 100 iterations
## # weights: 16
## initial value 518.631344
## iter 10 value 297.733583
## iter 20 value 260.956695
## iter 30 value 254.695938
## iter 40 value 251.286726
## iter 50 value 246.329184
## iter 60 value 244.871333
## final value 244.869219
## converged
## # weights: 46
## initial value 543.434605
## iter 10 value 251.773542
## iter 20 value 202.449008
## iter 30 value 187.161173
## iter 40 value 181.274720
## iter 50 value 175.099144
## iter 60 value 173.794870
## iter 70 value 173.778221
## final value 173.778191
## converged
## # weights: 76
## initial value 543.461199
## iter 10 value 235.244784
## iter 20 value 179.972798
## iter 30 value 123.832865
## iter 40 value 107.043590
## iter 50 value 99.547832
## iter 60 value 93.482729
## iter 70 value 88.319011
## iter 80 value 85.801047
## iter 90 value 85.347770
## iter 100 value 85.314066
## final value 85.314066
## stopped after 100 iterations
## # weights: 16
## initial value 513.170807
## iter 10 value 312.964923
## iter 20 value 266.704263
## iter 30 value 265.512215
## final value 265.511803
## converged
## # weights: 46
## initial value 534.279847
## iter 10 value 306.662731
## iter 20 value 267.643306
## iter 30 value 233.913316
## iter 40 value 229.932655
## iter 50 value 228.385067
## iter 60 value 228.013867
## iter 70 value 227.803003
## iter 80 value 227.391911
## iter 90 value 227.360693
## iter 100 value 227.356496
## final value 227.356496
## stopped after 100 iterations
## # weights: 76
## initial value 558.186278
## iter 10 value 233.229991
## iter 20 value 188.997413
## iter 30 value 170.180863
## iter 40 value 149.921224
## iter 50 value 129.834174
## iter 60 value 122.719798
## iter 70 value 119.568951
## iter 80 value 115.913620
## iter 90 value 114.913262
## iter 100 value 114.758297
## final value 114.758297
## stopped after 100 iterations
## # weights: 16
## initial value 472.156491
## iter 10 value 264.535757
## iter 20 value 257.975551
## iter 30 value 248.550883
## iter 40 value 248.323590
## iter 50 value 248.219685
## iter 60 value 248.186525
## iter 70 value 248.160270
## iter 80 value 248.143012
## iter 90 value 248.118058
## iter 100 value 248.084320
## final value 248.084320
## stopped after 100 iterations
## # weights: 46
## initial value 531.016769
## iter 10 value 264.347459
## iter 20 value 196.711316
## iter 30 value 172.568370
## iter 40 value 158.354419
## iter 50 value 152.872161
## iter 60 value 152.191889
## iter 70 value 149.646750
## iter 80 value 149.423263
## iter 90 value 149.246248
## iter 100 value 149.175043
## final value 149.175043
## stopped after 100 iterations
## # weights: 76
## initial value 561.548118
## iter 10 value 237.508345
## iter 20 value 155.875623
## iter 30 value 116.534178
## iter 40 value 102.780769
## iter 50 value 95.488081
## iter 60 value 86.509149
## iter 70 value 83.177609
## iter 80 value 79.786885
## iter 90 value 77.250115
## iter 100 value 73.812826
## final value 73.812826
## stopped after 100 iterations
## # weights: 16
## initial value 532.977013
## iter 10 value 270.983700
## iter 20 value 253.194966
## iter 30 value 249.215656
## iter 40 value 240.339295
## iter 50 value 237.833782
## iter 60 value 236.851897
## iter 70 value 236.614834
## iter 80 value 236.610099
## iter 90 value 236.609496
## final value 236.609423
## converged
## # weights: 46
## initial value 555.309195
## iter 10 value 248.751626
## iter 20 value 196.117498
## iter 30 value 171.944366
## iter 40 value 158.680935
## iter 50 value 154.443715
## iter 60 value 154.408120
## iter 70 value 154.399516
## iter 80 value 154.399376
## iter 80 value 154.399376
## iter 80 value 154.399376
## final value 154.399376
## converged
## # weights: 76
## initial value 511.367232
## iter 10 value 203.953530
## iter 20 value 133.752607
## iter 30 value 98.333401
## iter 40 value 91.918591
## iter 50 value 86.902632
## iter 60 value 83.998305
## iter 70 value 82.698892
## iter 80 value 82.647219
## iter 90 value 82.616309
## iter 100 value 82.545014
## final value 82.545014
## stopped after 100 iterations
## # weights: 16
## initial value 521.256289
## iter 10 value 306.708264
## iter 20 value 267.130443
## iter 30 value 263.111969
## iter 40 value 260.736099
## iter 50 value 260.637888
## final value 260.631193
## converged
## # weights: 46
## initial value 549.379237
## iter 10 value 251.166479
## iter 20 value 207.779892
## iter 30 value 181.959171
## iter 40 value 175.769951
## iter 50 value 172.516848
## iter 60 value 171.398946
## iter 70 value 171.142492
## iter 80 value 171.130628
## final value 171.130576
## converged
## # weights: 76
## initial value 643.677356
## iter 10 value 237.084284
## iter 20 value 180.173768
## iter 30 value 157.440679
## iter 40 value 145.052806
## iter 50 value 133.147812
## iter 60 value 122.795644
## iter 70 value 120.825028
## iter 80 value 120.300896
## iter 90 value 120.129467
## iter 100 value 120.095093
## final value 120.095093
## stopped after 100 iterations
## # weights: 16
## initial value 529.671145
## iter 10 value 308.422455
## iter 20 value 263.324123
## iter 30 value 254.820470
## iter 40 value 250.074065
## iter 50 value 247.071239
## iter 60 value 238.529961
## iter 70 value 238.354462
## iter 80 value 238.286467
## iter 90 value 238.279468
## iter 100 value 238.276363
## final value 238.276363
## stopped after 100 iterations
## # weights: 46
## initial value 520.533673
## iter 10 value 249.080315
## iter 20 value 198.974673
## iter 30 value 162.924143
## iter 40 value 154.136832
## iter 50 value 147.357539
## iter 60 value 146.540975
## iter 70 value 146.382485
## iter 80 value 146.308529
## iter 90 value 146.280443
## iter 100 value 146.270210
## final value 146.270210
## stopped after 100 iterations
## # weights: 76
## initial value 545.507231
## iter 10 value 241.416736
## iter 20 value 165.260247
## iter 30 value 134.268320
## iter 40 value 118.129674
## iter 50 value 107.845060
## iter 60 value 105.485286
## iter 70 value 104.660331
## iter 80 value 103.865263
## iter 90 value 103.135034
## iter 100 value 103.067318
## final value 103.067318
## stopped after 100 iterations
## # weights: 76
## initial value 583.510367
## iter 10 value 245.448391
## iter 20 value 146.635783
## iter 30 value 109.820768
## iter 40 value 100.792281
## iter 50 value 97.386416
## iter 60 value 95.372109
## iter 70 value 94.626991
## iter 80 value 93.976713
## iter 90 value 93.667606
## iter 100 value 93.270533
## final value 93.270533
## stopped after 100 iterationsresultado_entrenamiento6 <- predict(modelo6,entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificaciión
#Matriz de confusión del resultado de entrenamiento
mcre6 <- confusionMatrix(resultado_entrenamiento6,entrenamiento$target)
mcre6## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 387 10
## 1 13 411
##
## Accuracy : 0.972
## 95% CI : (0.9583, 0.9822)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9439
##
## Mcnemar's Test P-Value : 0.6767
##
## Sensitivity : 0.9675
## Specificity : 0.9762
## Pos Pred Value : 0.9748
## Neg Pred Value : 0.9693
## Prevalence : 0.4872
## Detection Rate : 0.4714
## Detection Prevalence : 0.4836
## Balanced Accuracy : 0.9719
##
## 'Positive' Class : 0
## # Matriz de Confusión del Resultado de la Prueba
mcrp6 <- confusionMatrix(resultado_prueba6,prueba$target)
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 98 5
## 1 1 100
##
## Accuracy : 0.9706
## 95% CI : (0.9371, 0.9891)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9412
##
## Mcnemar's Test P-Value : 0.2207
##
## Sensitivity : 0.9899
## Specificity : 0.9524
## Pos Pred Value : 0.9515
## Neg Pred Value : 0.9901
## Prevalence : 0.4853
## Detection Rate : 0.4804
## Detection Prevalence : 0.5049
## Balanced Accuracy : 0.9711
##
## 'Positive' Class : 0
## Tabla de Resultados
resultados <- data.frame(
"svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
"svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"rf" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"nnet" = c(mcre6$overall["Accuracy"], mcrp5$overall["Accuracy"]))
rownames(resultados) <- c("Exactitud del Entrenamiento", "Exactitud de la Prueba")
resultados## svmLinear svmRadial svmPoly rpart rf nnet
## Exactitud del Entrenamiento 0.8343484 1 1 0.9244823 1 0.9719854
## Exactitud de la Prueba 0.8480392 1 1 0.8823529 1 1.0000000Conclusión
En conclusión, el modelo de Bosques aleatorios es el recomendado debido a que muestra una predicción muy buena tanto con los datos de entrenamiento como los de prueba.