CARET - Heart
Santiago Jaramillo Nava
2026-08-26
Teoría
El paquete CARET (Classification And Regression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático .
Instalar paquetes y llamar librerías
#install.packages("caret") # algoriutmos de aprendizaje automático
library(caret)## Loading required package: ggplot2## Loading required package: lattice#install.packages("ggplot2") # Gráficas
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("readxl") # Leer archivos de Excel
library(readxl)Crear la base de datos
df <- data.frame(read_excel("/Users/santiagojaramillo/Downloads/heart.xlsx"))
df$target <- as.factor(df$target)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 0:499
## 1st Qu.:2.000 1:526
## Median :2.000
## Mean :2.324
## 3rd Qu.:3.000
## Max. :3.000str(df)## 'data.frame': 1025 obs. of 14 variables:
## $ age : num 52 53 70 61 62 58 58 55 46 54 ...
## $ sex : num 1 1 1 1 0 0 1 1 1 1 ...
## $ cp : num 0 0 0 0 0 0 0 0 0 0 ...
## $ trestbps: num 125 140 145 148 138 100 114 160 120 122 ...
## $ chol : num 212 203 174 203 294 248 318 289 249 286 ...
## $ fbs : num 0 1 0 0 1 0 0 0 0 0 ...
## $ restecg : num 1 0 1 1 1 0 2 0 0 0 ...
## $ thalach : num 168 155 125 161 106 122 140 145 144 116 ...
## $ exang : num 0 1 1 0 0 0 0 1 0 1 ...
## $ oldpeak : num 1 3.1 2.6 0 1.9 1 4.4 0.8 0.8 3.2 ...
## $ slope : num 2 0 0 2 1 1 0 1 2 1 ...
## $ ca : num 2 0 0 1 3 0 3 1 0 2 ...
## $ thal : num 3 3 3 3 2 2 1 3 3 2 ...
## $ target : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...#create_report(df)
plot_missing(df)
plot_histogram(df)
plot_correlation(df)
NOTA: en modeloss de clasificación, la variable que queremos
predecir debe tener formato de FACTOR
partir la base de datos
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$target, p=0.8, list=FALSE)
entrenamiento <- df[renglones_entrenamiento, ]
prueba <- df[-renglones_entrenamiento, ]Distintos tipos de métodos para Modelar
Los métodos más utilizados para modelar aprendizaje automático son:
- SVM: Support Vector Machine o Máquina de Vectores de Soporte. Hay varios subtipos: Lineal (svmLinear), Radial (svmRadial), Polinómico(svmPoly), etc. *Árbol de decisión: rpart
- Redes Neuronales: nnet
- Random Forest o Bosques Aleatorios: rf
Modelo 1. SVM Lineal
modelo1 <- train (target~., data=entrenamiento,
method="svmLinear", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(c=1) # Cambiar
)
resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)
# Matriz de confusión
# Es una tabla de evaluación que desglosa el rendimietno del modelo de clasificación
#Matriz de Confusión del Resultado de Entrenamiento
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$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$target)
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 78 10
## 1 21 95
##
## Accuracy : 0.848
## 95% CI : (0.7913, 0.8944)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.6948
##
## Mcnemar's Test P-Value : 0.07249
##
## Sensitivity : 0.7879
## Specificity : 0.9048
## Pos Pred Value : 0.8864
## Neg Pred Value : 0.8190
## Prevalence : 0.4853
## Detection Rate : 0.3824
## Detection Prevalence : 0.4314
## Balanced Accuracy : 0.8463
##
## 'Positive' Class : 0
## Modelo 2. SVM Radial
modelo2 <- train (target~., data=entrenamiento,
method="svmRadial", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(sigma=1, c=1) # Cambiar
)
resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)
# Matriz de confusión
# Es una tabla de evaluación que desglosa el rendimietno del modelo de clasificación
#Matriz de Confusión del Resultado de Entrenamiento
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$target)
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 356 20
## 1 44 401
##
## Accuracy : 0.922
## 95% CI : (0.9015, 0.9395)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.8438
##
## Mcnemar's Test P-Value : 0.00404
##
## Sensitivity : 0.8900
## Specificity : 0.9525
## Pos Pred Value : 0.9468
## Neg Pred Value : 0.9011
## Prevalence : 0.4872
## Detection Rate : 0.4336
## Detection Prevalence : 0.4580
## Balanced Accuracy : 0.9212
##
## 'Positive' Class : 0
## #Matriz de confusión del Resultado de la Prueba
mcrp2 <- confusionMatrix (resultado_prueba2, prueba$target)
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 90 7
## 1 9 98
##
## Accuracy : 0.9216
## 95% CI : (0.8758, 0.9545)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8429
##
## Mcnemar's Test P-Value : 0.8026
##
## Sensitivity : 0.9091
## Specificity : 0.9333
## Pos Pred Value : 0.9278
## Neg Pred Value : 0.9159
## Prevalence : 0.4853
## Detection Rate : 0.4412
## Detection Prevalence : 0.4755
## Balanced Accuracy : 0.9212
##
## 'Positive' Class : 0
## Modelo 3. SVM Polinómico
modelo3 <- train (target~., data=entrenamiento,
method="svmPoly", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(degree=1, scale=1, C=1) # Cambiar
)
resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)
# Matriz de confusión
# Es una tabla de evaluación que desglosa el rendimietno del modelo de clasificación
#Matriz de Confusión del Resultado de Entrenamiento
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$target)
mcre3## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 391 3
## 1 9 418
##
## Accuracy : 0.9854
## 95% CI : (0.9746, 0.9924)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9707
##
## Mcnemar's Test P-Value : 0.1489
##
## Sensitivity : 0.9775
## Specificity : 0.9929
## Pos Pred Value : 0.9924
## Neg Pred Value : 0.9789
## Prevalence : 0.4872
## Detection Rate : 0.4762
## Detection Prevalence : 0.4799
## Balanced Accuracy : 0.9852
##
## 'Positive' Class : 0
## #Matriz de confusión del Resultado de la Prueba
mcrp3 <- confusionMatrix (resultado_prueba3, prueba$target)
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 96 3
## 1 3 102
##
## Accuracy : 0.9706
## 95% CI : (0.9371, 0.9891)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9411
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9697
## Specificity : 0.9714
## Pos Pred Value : 0.9697
## Neg Pred Value : 0.9714
## Prevalence : 0.4853
## Detection Rate : 0.4706
## Detection Prevalence : 0.4853
## Balanced Accuracy : 0.9706
##
## 'Positive' Class : 0
## Modelo 4. Árbol de decisión
modelo4 <- train (target~., data=entrenamiento,
method="rpart", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneLength = 10 # Cambiar
)
resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)
# Matriz de confusión
# Es una tabla de evaluación que desglosa el rendimietno del modelo de clasificación
#Matriz de Confusión del Resultado de Entrenamiento
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$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$target)
mcrp4## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 82 7
## 1 17 98
##
## Accuracy : 0.8824
## 95% CI : (0.83, 0.9231)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.7638
##
## Mcnemar's Test P-Value : 0.06619
##
## Sensitivity : 0.8283
## Specificity : 0.9333
## Pos Pred Value : 0.9213
## Neg Pred Value : 0.8522
## Prevalence : 0.4853
## Detection Rate : 0.4020
## Detection Prevalence : 0.4363
## Balanced Accuracy : 0.8808
##
## 'Positive' Class : 0
## Modelo 5. Bosques Aleatorios
modelo5 <- train (target~., data=entrenamiento,
method="rf", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = expand.grid(mtry=c(2,4,6)) # Cambiar
)
resultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
# Matriz de confusión
# Es una tabla de evaluación que desglosa el rendimietno del modelo de clasificación
#Matriz de Confusión del Resultado de Entrenamiento
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$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", # Cambiar
preProcess = c("scale","center"),
trControl = trainControl(method="cv", number=10)
# Cambiar
)## # weights: 16
## initial value 535.375236
## iter 10 value 305.824212
## iter 20 value 275.855839
## iter 30 value 264.565475
## iter 40 value 258.976519
## iter 50 value 256.327139
## iter 60 value 250.205575
## iter 70 value 249.267138
## iter 80 value 235.514950
## final value 234.620074
## converged
## # weights: 46
## initial value 528.694101
## iter 10 value 260.313369
## iter 20 value 212.544446
## iter 30 value 203.186878
## iter 40 value 194.123780
## iter 50 value 184.425375
## iter 60 value 182.070930
## iter 70 value 182.064559
## final value 182.064552
## converged
## # weights: 76
## initial value 575.590475
## iter 10 value 242.612835
## iter 20 value 176.557309
## iter 30 value 118.018268
## iter 40 value 102.995754
## iter 50 value 93.799231
## iter 60 value 90.300023
## iter 70 value 89.622837
## iter 80 value 88.274726
## iter 90 value 84.823241
## iter 100 value 84.255918
## final value 84.255918
## stopped after 100 iterations
## # weights: 16
## initial value 520.993824
## iter 10 value 311.231466
## iter 20 value 273.050512
## iter 30 value 270.328982
## iter 40 value 269.928067
## iter 50 value 269.842192
## final value 269.829269
## converged
## # weights: 46
## initial value 528.085888
## iter 10 value 254.017819
## iter 20 value 230.433639
## iter 30 value 213.006060
## iter 40 value 200.024936
## iter 50 value 194.837339
## iter 60 value 193.958959
## iter 70 value 191.045989
## iter 80 value 189.173179
## iter 90 value 189.052634
## iter 100 value 189.041748
## final value 189.041748
## stopped after 100 iterations
## # weights: 76
## initial value 668.381505
## iter 10 value 277.367224
## iter 20 value 230.600874
## iter 30 value 201.758983
## iter 40 value 185.294345
## iter 50 value 169.188815
## iter 60 value 157.068340
## iter 70 value 148.655199
## iter 80 value 146.526722
## iter 90 value 145.138806
## iter 100 value 143.817384
## final value 143.817384
## stopped after 100 iterations
## # weights: 16
## initial value 532.893863
## iter 10 value 325.778152
## iter 20 value 265.787893
## iter 30 value 264.563899
## iter 40 value 264.512151
## iter 50 value 264.126920
## iter 60 value 263.513766
## iter 70 value 263.385091
## iter 80 value 263.381398
## iter 90 value 263.341664
## iter 100 value 263.309238
## final value 263.309238
## stopped after 100 iterations
## # weights: 46
## initial value 517.389902
## iter 10 value 228.723315
## iter 20 value 198.292421
## iter 30 value 172.278040
## iter 40 value 147.046437
## iter 50 value 137.988169
## iter 60 value 136.824516
## iter 70 value 136.580523
## iter 80 value 136.524577
## iter 90 value 136.462194
## iter 100 value 136.339876
## final value 136.339876
## stopped after 100 iterations
## # weights: 76
## initial value 614.457467
## iter 10 value 251.437132
## iter 20 value 191.377325
## iter 30 value 145.355880
## iter 40 value 127.651832
## iter 50 value 119.172995
## iter 60 value 117.334707
## iter 70 value 115.642958
## iter 80 value 115.237553
## iter 90 value 115.092696
## iter 100 value 114.975083
## final value 114.975083
## stopped after 100 iterations
## # weights: 16
## initial value 508.742022
## iter 10 value 299.156677
## iter 20 value 274.699351
## iter 30 value 263.576398
## iter 40 value 262.315740
## iter 50 value 260.859671
## iter 60 value 260.631404
## iter 70 value 260.597992
## iter 80 value 260.457191
## iter 90 value 260.365082
## iter 100 value 260.344572
## final value 260.344572
## stopped after 100 iterations
## # weights: 46
## initial value 539.499371
## iter 10 value 253.943077
## iter 20 value 230.397124
## iter 30 value 196.524749
## iter 40 value 180.563418
## iter 50 value 167.473582
## iter 60 value 161.591162
## iter 70 value 161.534234
## final value 161.534146
## converged
## # weights: 76
## initial value 546.439235
## iter 10 value 225.767357
## iter 20 value 133.137586
## iter 30 value 89.903977
## iter 40 value 78.954091
## iter 50 value 74.667182
## iter 60 value 72.031344
## iter 70 value 71.685510
## iter 80 value 71.614649
## iter 90 value 71.600919
## iter 100 value 71.600276
## final value 71.600276
## stopped after 100 iterations
## # weights: 16
## initial value 511.644137
## iter 10 value 297.200282
## iter 20 value 277.125896
## iter 30 value 270.116750
## iter 40 value 268.528921
## iter 50 value 268.176524
## final value 268.069566
## converged
## # weights: 46
## initial value 554.859699
## iter 10 value 268.435519
## iter 20 value 248.161698
## iter 30 value 230.647061
## iter 40 value 222.198185
## iter 50 value 213.983142
## iter 60 value 208.260555
## iter 70 value 196.270609
## iter 80 value 187.950809
## iter 90 value 187.643066
## iter 100 value 187.634050
## final value 187.634050
## stopped after 100 iterations
## # weights: 76
## initial value 503.623932
## iter 10 value 229.842390
## iter 20 value 195.817915
## iter 30 value 177.194543
## iter 40 value 158.229761
## iter 50 value 148.283311
## iter 60 value 146.060155
## iter 70 value 143.536210
## iter 80 value 142.586259
## iter 90 value 142.436477
## iter 100 value 142.369645
## final value 142.369645
## stopped after 100 iterations
## # weights: 16
## initial value 522.254022
## iter 10 value 303.644732
## iter 20 value 270.507537
## iter 30 value 264.640196
## iter 40 value 258.419303
## iter 50 value 254.200396
## iter 60 value 239.046202
## iter 70 value 238.602475
## iter 80 value 238.447121
## iter 90 value 238.216956
## iter 100 value 237.581761
## final value 237.581761
## stopped after 100 iterations
## # weights: 46
## initial value 576.723361
## iter 10 value 263.920686
## iter 20 value 231.012088
## iter 30 value 182.611609
## iter 40 value 139.954051
## iter 50 value 119.686484
## iter 60 value 105.976739
## iter 70 value 99.730298
## iter 80 value 99.452154
## iter 90 value 98.970999
## iter 100 value 98.756869
## final value 98.756869
## stopped after 100 iterations
## # weights: 76
## initial value 495.172484
## iter 10 value 229.005093
## iter 20 value 152.976637
## iter 30 value 127.519141
## iter 40 value 116.875869
## iter 50 value 101.899053
## iter 60 value 99.376819
## iter 70 value 98.995740
## iter 80 value 98.901507
## iter 90 value 98.829214
## iter 100 value 97.635963
## final value 97.635963
## stopped after 100 iterations
## # weights: 16
## initial value 543.983251
## iter 10 value 266.307470
## iter 20 value 251.096992
## iter 30 value 247.554051
## iter 40 value 234.484205
## iter 50 value 230.754282
## iter 60 value 225.256800
## final value 225.243236
## converged
## # weights: 46
## initial value 551.132327
## iter 10 value 230.455229
## iter 20 value 193.091097
## iter 30 value 158.807623
## iter 40 value 139.913530
## iter 50 value 132.903544
## iter 60 value 129.828096
## iter 70 value 128.848115
## iter 80 value 128.814064
## iter 90 value 128.812567
## iter 100 value 128.795992
## final value 128.795992
## stopped after 100 iterations
## # weights: 76
## initial value 497.150525
## iter 10 value 220.001885
## iter 20 value 148.901729
## iter 30 value 93.807773
## iter 40 value 82.308049
## iter 50 value 79.031398
## iter 60 value 78.737673
## iter 70 value 78.685377
## iter 80 value 78.345343
## iter 90 value 78.343616
## iter 100 value 78.342788
## final value 78.342788
## stopped after 100 iterations
## # weights: 16
## initial value 551.109219
## iter 10 value 256.656049
## iter 20 value 254.916228
## iter 30 value 253.888205
## iter 40 value 252.966331
## iter 50 value 252.958586
## final value 252.957718
## converged
## # weights: 46
## initial value 557.933784
## iter 10 value 250.075456
## iter 20 value 236.291551
## iter 30 value 207.122344
## iter 40 value 190.488446
## iter 50 value 183.259744
## iter 60 value 181.685441
## iter 70 value 179.618598
## iter 80 value 174.152349
## iter 90 value 172.236861
## iter 100 value 171.978530
## final value 171.978530
## stopped after 100 iterations
## # weights: 76
## initial value 558.253557
## iter 10 value 229.010836
## iter 20 value 183.145827
## iter 30 value 159.272031
## iter 40 value 138.235749
## iter 50 value 130.572151
## iter 60 value 127.974596
## iter 70 value 126.887230
## iter 80 value 126.285918
## iter 90 value 126.145426
## iter 100 value 126.129686
## final value 126.129686
## stopped after 100 iterations
## # weights: 16
## initial value 519.911362
## iter 10 value 270.862989
## iter 20 value 253.583385
## iter 30 value 246.678301
## iter 40 value 244.779128
## iter 50 value 243.958990
## iter 60 value 232.452805
## iter 70 value 231.058633
## iter 80 value 230.787072
## iter 90 value 230.729866
## iter 100 value 230.726790
## final value 230.726790
## stopped after 100 iterations
## # weights: 46
## initial value 548.264527
## iter 10 value 287.441599
## iter 20 value 240.448758
## iter 30 value 220.711799
## iter 40 value 205.236325
## iter 50 value 197.032393
## iter 60 value 195.265178
## iter 70 value 194.129565
## iter 80 value 189.243415
## iter 90 value 177.436328
## iter 100 value 172.336703
## final value 172.336703
## stopped after 100 iterations
## # weights: 76
## initial value 506.974573
## iter 10 value 229.957764
## iter 20 value 156.876308
## iter 30 value 113.321178
## iter 40 value 101.580209
## iter 50 value 93.495688
## iter 60 value 88.735879
## iter 70 value 85.174025
## iter 80 value 84.580013
## iter 90 value 84.410808
## iter 100 value 84.329884
## final value 84.329884
## stopped after 100 iterations
## # weights: 16
## initial value 517.810707
## iter 10 value 286.498535
## iter 20 value 265.184593
## iter 30 value 252.513047
## iter 40 value 243.847606
## iter 50 value 243.336418
## iter 60 value 243.236431
## iter 70 value 243.050582
## iter 80 value 242.911123
## iter 90 value 242.870507
## iter 100 value 242.585447
## final value 242.585447
## stopped after 100 iterations
## # weights: 46
## initial value 518.066794
## iter 10 value 247.661628
## iter 20 value 179.320064
## iter 30 value 159.709029
## iter 40 value 141.775506
## iter 50 value 130.351919
## iter 60 value 127.887500
## iter 70 value 127.488501
## iter 80 value 127.442684
## iter 90 value 127.406318
## iter 100 value 127.382185
## final value 127.382185
## stopped after 100 iterations
## # weights: 76
## initial value 514.512405
## iter 10 value 231.252810
## iter 20 value 152.475769
## iter 30 value 109.860155
## iter 40 value 99.859629
## iter 50 value 93.159592
## iter 60 value 89.766759
## iter 70 value 85.068763
## iter 80 value 83.520101
## iter 90 value 74.137482
## iter 100 value 72.640768
## final value 72.640768
## stopped after 100 iterations
## # weights: 16
## initial value 511.395597
## iter 10 value 446.841021
## iter 20 value 320.542883
## iter 30 value 281.654171
## iter 40 value 270.935623
## iter 50 value 269.896165
## final value 269.896143
## converged
## # weights: 46
## initial value 526.582038
## iter 10 value 301.121993
## iter 20 value 237.208033
## iter 30 value 212.150097
## iter 40 value 202.621236
## iter 50 value 196.875020
## iter 60 value 194.965604
## iter 70 value 193.852746
## iter 80 value 193.478780
## iter 90 value 193.474843
## final value 193.474832
## converged
## # weights: 76
## initial value 539.799188
## iter 10 value 244.861468
## iter 20 value 192.120278
## iter 30 value 164.693727
## iter 40 value 136.974046
## iter 50 value 123.492266
## iter 60 value 112.777359
## iter 70 value 110.031025
## iter 80 value 107.845151
## iter 90 value 107.459364
## iter 100 value 107.318149
## final value 107.318149
## stopped after 100 iterations
## # weights: 16
## initial value 543.109625
## iter 10 value 275.098512
## iter 20 value 263.572903
## iter 30 value 250.709981
## iter 40 value 248.482471
## iter 50 value 248.249032
## iter 60 value 248.233709
## iter 70 value 248.216920
## iter 80 value 248.212551
## iter 90 value 248.205407
## iter 100 value 248.190062
## final value 248.190062
## stopped after 100 iterations
## # weights: 46
## initial value 605.733621
## iter 10 value 257.894282
## iter 20 value 205.265255
## iter 30 value 187.948752
## iter 40 value 177.134181
## iter 50 value 169.369418
## iter 60 value 164.853547
## iter 70 value 163.477881
## iter 80 value 161.667428
## iter 90 value 160.243486
## iter 100 value 159.717375
## final value 159.717375
## stopped after 100 iterations
## # weights: 76
## initial value 520.583193
## iter 10 value 210.652458
## iter 20 value 154.622899
## iter 30 value 111.768034
## iter 40 value 96.057720
## iter 50 value 92.862665
## iter 60 value 89.143708
## iter 70 value 87.709337
## iter 80 value 87.040188
## iter 90 value 86.577386
## iter 100 value 86.494952
## final value 86.494952
## stopped after 100 iterations
## # weights: 16
## initial value 523.017723
## iter 10 value 347.680638
## iter 20 value 273.472666
## iter 30 value 263.048722
## iter 40 value 254.303613
## iter 50 value 254.057635
## iter 60 value 254.011453
## iter 70 value 253.980386
## iter 80 value 253.972343
## iter 90 value 253.958500
## iter 100 value 253.885848
## final value 253.885848
## stopped after 100 iterations
## # weights: 46
## initial value 498.586989
## iter 10 value 252.293352
## iter 20 value 196.833685
## iter 30 value 167.383720
## iter 40 value 140.714188
## iter 50 value 131.313426
## iter 60 value 120.912767
## iter 70 value 119.919892
## iter 80 value 119.882366
## iter 90 value 119.873919
## iter 100 value 119.822893
## final value 119.822893
## stopped after 100 iterations
## # weights: 76
## initial value 517.401193
## iter 10 value 236.841136
## iter 20 value 165.780608
## iter 30 value 115.725248
## iter 40 value 92.622357
## iter 50 value 86.162448
## iter 60 value 80.504955
## iter 70 value 80.036969
## iter 80 value 79.959614
## iter 90 value 79.950062
## final value 79.949916
## converged
## # weights: 16
## initial value 531.934244
## iter 10 value 300.129820
## iter 20 value 272.528729
## iter 30 value 270.453509
## iter 40 value 270.096244
## iter 50 value 270.084908
## final value 270.084796
## converged
## # weights: 46
## initial value 534.710833
## iter 10 value 282.031503
## iter 20 value 242.156205
## iter 30 value 224.192932
## iter 40 value 207.359477
## iter 50 value 202.482633
## iter 60 value 199.953820
## iter 70 value 199.392582
## iter 80 value 199.202224
## iter 90 value 199.137994
## iter 100 value 199.049727
## final value 199.049727
## stopped after 100 iterations
## # weights: 76
## initial value 556.976522
## iter 10 value 259.694096
## iter 20 value 205.288529
## iter 30 value 186.819642
## iter 40 value 176.442234
## iter 50 value 160.467869
## iter 60 value 150.152898
## iter 70 value 141.682056
## iter 80 value 137.467942
## iter 90 value 134.943365
## iter 100 value 133.645655
## final value 133.645655
## stopped after 100 iterations
## # weights: 16
## initial value 510.094803
## iter 10 value 284.115036
## iter 20 value 271.427917
## iter 30 value 265.706556
## iter 40 value 257.711802
## iter 50 value 257.088293
## iter 60 value 252.211848
## iter 70 value 252.150974
## iter 80 value 252.150591
## final value 252.150588
## converged
## # weights: 46
## initial value 491.937270
## iter 10 value 242.227363
## iter 20 value 208.549141
## iter 30 value 195.652552
## iter 40 value 182.871626
## iter 50 value 175.448909
## iter 60 value 173.546635
## iter 70 value 173.182389
## iter 80 value 172.773138
## iter 90 value 172.676052
## iter 100 value 172.621077
## final value 172.621077
## stopped after 100 iterations
## # weights: 76
## initial value 514.613550
## iter 10 value 210.035997
## iter 20 value 155.177688
## iter 30 value 117.952893
## iter 40 value 99.610856
## iter 50 value 91.124477
## iter 60 value 88.801651
## iter 70 value 88.594406
## iter 80 value 88.326394
## iter 90 value 88.195356
## iter 100 value 88.117892
## final value 88.117892
## stopped after 100 iterations
## # weights: 16
## initial value 511.094900
## iter 10 value 278.921706
## iter 20 value 270.382317
## iter 30 value 264.302293
## iter 40 value 264.032206
## iter 50 value 263.925229
## iter 60 value 263.798965
## iter 70 value 263.759804
## iter 80 value 263.748013
## iter 90 value 263.737232
## iter 100 value 263.706411
## final value 263.706411
## stopped after 100 iterations
## # weights: 46
## initial value 518.840377
## iter 10 value 250.330504
## iter 20 value 215.721146
## iter 30 value 189.461573
## iter 40 value 174.770913
## iter 50 value 172.474548
## iter 60 value 172.450197
## iter 70 value 172.252389
## iter 80 value 171.324949
## final value 171.318463
## converged
## # weights: 76
## initial value 473.733547
## iter 10 value 217.422109
## iter 20 value 160.153220
## iter 30 value 110.862053
## iter 40 value 94.930733
## iter 50 value 87.588181
## iter 60 value 84.861344
## iter 70 value 84.458088
## iter 80 value 84.339695
## iter 90 value 84.022084
## iter 100 value 82.561677
## final value 82.561677
## stopped after 100 iterations
## # weights: 16
## initial value 527.978687
## iter 10 value 288.484896
## iter 20 value 274.309893
## iter 30 value 272.720199
## iter 40 value 272.341925
## iter 50 value 271.779314
## iter 60 value 271.519021
## final value 271.518801
## converged
## # weights: 46
## initial value 470.749471
## iter 10 value 252.792891
## iter 20 value 225.994422
## iter 30 value 192.035171
## iter 40 value 173.245495
## iter 50 value 169.396690
## iter 60 value 168.501247
## iter 70 value 168.404344
## final value 168.402135
## converged
## # weights: 76
## initial value 497.882547
## iter 10 value 233.022178
## iter 20 value 186.818033
## iter 30 value 158.119216
## iter 40 value 144.313371
## iter 50 value 136.208245
## iter 60 value 130.267288
## iter 70 value 124.451595
## iter 80 value 123.373517
## iter 90 value 123.214233
## iter 100 value 123.017390
## final value 123.017390
## stopped after 100 iterations
## # weights: 16
## initial value 540.225354
## iter 10 value 387.006208
## iter 20 value 300.051158
## iter 30 value 273.532871
## iter 40 value 267.537290
## iter 50 value 256.835107
## iter 60 value 255.804647
## iter 70 value 255.489243
## iter 80 value 255.444370
## iter 90 value 255.412110
## iter 100 value 255.390512
## final value 255.390512
## stopped after 100 iterations
## # weights: 46
## initial value 554.461926
## iter 10 value 298.033243
## iter 20 value 236.930990
## iter 30 value 185.595255
## iter 40 value 158.199200
## iter 50 value 139.146678
## iter 60 value 126.226434
## iter 70 value 123.521613
## iter 80 value 122.928262
## iter 90 value 122.824691
## iter 100 value 122.752368
## final value 122.752368
## stopped after 100 iterations
## # weights: 76
## initial value 567.635670
## iter 10 value 249.879593
## iter 20 value 188.210739
## iter 30 value 137.647918
## iter 40 value 108.915952
## iter 50 value 93.482638
## iter 60 value 80.958353
## iter 70 value 76.084749
## iter 80 value 72.775458
## iter 90 value 71.548197
## iter 100 value 70.695446
## final value 70.695446
## stopped after 100 iterations
## # weights: 16
## initial value 523.035040
## iter 10 value 300.950896
## iter 20 value 257.426976
## iter 30 value 254.340051
## iter 40 value 251.799688
## iter 50 value 242.877368
## iter 60 value 241.549799
## iter 70 value 237.782144
## iter 80 value 237.767577
## iter 90 value 237.766281
## final value 237.766277
## converged
## # weights: 46
## initial value 614.798795
## iter 10 value 292.814662
## iter 20 value 223.012131
## iter 30 value 166.442784
## iter 40 value 146.482220
## iter 50 value 128.558347
## iter 60 value 124.087808
## iter 70 value 123.941028
## iter 80 value 123.698871
## iter 90 value 122.973863
## iter 100 value 122.530107
## final value 122.530107
## stopped after 100 iterations
## # weights: 76
## initial value 529.591730
## iter 10 value 223.356964
## iter 20 value 153.433093
## iter 30 value 118.660796
## iter 40 value 109.897025
## iter 50 value 106.825770
## iter 60 value 106.519549
## iter 70 value 106.485811
## iter 80 value 106.482529
## final value 106.482516
## converged
## # weights: 16
## initial value 546.562483
## iter 10 value 326.771874
## iter 20 value 268.831121
## iter 30 value 261.364486
## iter 40 value 260.770866
## iter 50 value 260.738110
## final value 260.736974
## converged
## # weights: 46
## initial value 531.168903
## iter 10 value 230.682406
## iter 20 value 213.753824
## iter 30 value 206.996853
## iter 40 value 206.277173
## iter 50 value 201.423312
## iter 60 value 196.647900
## iter 70 value 183.046088
## iter 80 value 180.633701
## iter 90 value 179.378367
## iter 100 value 179.355823
## final value 179.355823
## stopped after 100 iterations
## # weights: 76
## initial value 502.069958
## iter 10 value 253.598526
## iter 20 value 208.834666
## iter 30 value 186.102276
## iter 40 value 171.905609
## iter 50 value 165.317454
## iter 60 value 157.191715
## iter 70 value 150.027595
## iter 80 value 142.183158
## iter 90 value 137.763490
## iter 100 value 135.344942
## final value 135.344942
## stopped after 100 iterations
## # weights: 16
## initial value 526.185610
## iter 10 value 338.341160
## iter 20 value 258.918740
## iter 30 value 254.409150
## iter 40 value 254.270189
## iter 50 value 254.150857
## iter 60 value 253.869504
## iter 70 value 253.850263
## final value 253.850255
## converged
## # weights: 46
## initial value 564.956355
## iter 10 value 250.683051
## iter 20 value 227.706155
## iter 30 value 182.973563
## iter 40 value 168.133176
## iter 50 value 157.117169
## iter 60 value 155.275165
## iter 70 value 154.311571
## iter 80 value 154.062051
## iter 90 value 154.034152
## iter 100 value 154.015949
## final value 154.015949
## stopped after 100 iterations
## # weights: 76
## initial value 598.798103
## iter 10 value 198.462887
## iter 20 value 127.814362
## iter 30 value 105.168907
## iter 40 value 93.118363
## iter 50 value 82.808987
## iter 60 value 73.669620
## iter 70 value 67.025718
## iter 80 value 65.980641
## iter 90 value 65.635539
## iter 100 value 65.462022
## final value 65.462022
## stopped after 100 iterations
## # weights: 16
## initial value 515.909807
## iter 10 value 336.748041
## iter 20 value 271.302669
## iter 30 value 257.310764
## iter 40 value 253.794526
## iter 50 value 243.135297
## iter 60 value 243.007735
## final value 243.007550
## converged
## # weights: 46
## initial value 722.656742
## iter 10 value 296.297709
## iter 20 value 208.477379
## iter 30 value 185.408483
## iter 40 value 174.068879
## iter 50 value 167.030303
## iter 60 value 165.581857
## iter 70 value 165.369973
## iter 80 value 165.361170
## iter 90 value 165.359073
## final value 165.357751
## converged
## # weights: 76
## initial value 574.558178
## iter 10 value 215.245786
## iter 20 value 135.471371
## iter 30 value 84.244945
## iter 40 value 56.425682
## iter 50 value 49.320981
## iter 60 value 48.428670
## iter 70 value 48.273637
## iter 80 value 48.080304
## iter 90 value 48.002889
## iter 100 value 47.869000
## final value 47.869000
## stopped after 100 iterations
## # weights: 16
## initial value 531.546927
## iter 10 value 293.551105
## iter 20 value 268.446471
## iter 30 value 264.401798
## iter 40 value 264.255169
## iter 40 value 264.255169
## iter 40 value 264.255169
## final value 264.255169
## converged
## # weights: 46
## initial value 546.985197
## iter 10 value 285.770180
## iter 20 value 241.627174
## iter 30 value 231.676044
## iter 40 value 215.414790
## iter 50 value 213.031955
## iter 60 value 212.831718
## iter 70 value 212.798028
## final value 212.797977
## converged
## # weights: 76
## initial value 633.219117
## iter 10 value 276.471692
## iter 20 value 213.389886
## iter 30 value 172.677121
## iter 40 value 159.315981
## iter 50 value 151.411031
## iter 60 value 143.564425
## iter 70 value 140.400166
## iter 80 value 134.968969
## iter 90 value 130.429502
## iter 100 value 128.768656
## final value 128.768656
## stopped after 100 iterations
## # weights: 16
## initial value 513.267829
## iter 10 value 269.916870
## iter 20 value 251.654118
## iter 30 value 245.875940
## iter 40 value 237.191354
## iter 50 value 236.806681
## iter 60 value 235.799266
## final value 235.789689
## converged
## # weights: 46
## initial value 514.903478
## iter 10 value 250.094174
## iter 20 value 211.799806
## iter 30 value 178.580042
## iter 40 value 154.574752
## iter 50 value 141.699527
## iter 60 value 140.523283
## iter 70 value 138.046916
## iter 80 value 137.231479
## iter 90 value 136.999819
## iter 100 value 136.935396
## final value 136.935396
## stopped after 100 iterations
## # weights: 76
## initial value 576.625775
## iter 10 value 215.212770
## iter 20 value 127.377889
## iter 30 value 96.553478
## iter 40 value 88.971853
## iter 50 value 86.644144
## iter 60 value 86.165898
## iter 70 value 84.372637
## iter 80 value 84.094609
## iter 90 value 83.884871
## iter 100 value 80.598906
## final value 80.598906
## stopped after 100 iterations
## # weights: 16
## initial value 577.633372
## iter 10 value 351.606547
## iter 20 value 269.882356
## iter 30 value 262.157503
## iter 40 value 259.048493
## iter 50 value 258.271640
## iter 60 value 257.055492
## iter 70 value 256.863479
## iter 80 value 256.854441
## iter 90 value 256.827023
## iter 100 value 256.736435
## final value 256.736435
## stopped after 100 iterations
## # weights: 46
## initial value 540.202959
## iter 10 value 282.815917
## iter 20 value 250.195443
## iter 30 value 223.346779
## iter 40 value 203.546374
## iter 50 value 198.014652
## iter 60 value 197.834548
## final value 197.833585
## converged
## # weights: 76
## initial value 502.147524
## iter 10 value 225.613166
## iter 20 value 160.224773
## iter 30 value 109.191275
## iter 40 value 89.642186
## iter 50 value 85.583140
## iter 60 value 83.541528
## iter 70 value 83.204470
## iter 80 value 83.186055
## iter 90 value 83.185722
## iter 90 value 83.185721
## iter 90 value 83.185721
## final value 83.185721
## converged
## # weights: 16
## initial value 515.619841
## iter 10 value 307.440000
## iter 20 value 270.166862
## iter 30 value 266.432738
## iter 40 value 266.355067
## iter 50 value 266.350435
## final value 266.349214
## converged
## # weights: 46
## initial value 506.548029
## iter 10 value 267.381687
## iter 20 value 236.902719
## iter 30 value 214.778785
## iter 40 value 206.490210
## iter 50 value 203.718473
## iter 60 value 202.664116
## iter 70 value 199.247954
## iter 80 value 198.681249
## iter 90 value 198.678126
## iter 90 value 198.678125
## iter 90 value 198.678125
## final value 198.678125
## converged
## # weights: 76
## initial value 536.112769
## iter 10 value 246.370681
## iter 20 value 192.658794
## iter 30 value 174.810356
## iter 40 value 164.734912
## iter 50 value 158.022772
## iter 60 value 153.885966
## iter 70 value 148.763402
## iter 80 value 136.501431
## iter 90 value 130.561813
## iter 100 value 127.847298
## final value 127.847298
## stopped after 100 iterations
## # weights: 16
## initial value 566.733019
## iter 10 value 306.712026
## iter 20 value 267.839969
## iter 30 value 259.580414
## iter 40 value 258.255207
## iter 50 value 257.913203
## iter 60 value 257.093078
## iter 70 value 256.964413
## iter 80 value 256.942466
## iter 90 value 256.877190
## iter 100 value 256.841597
## final value 256.841597
## stopped after 100 iterations
## # weights: 46
## initial value 624.216851
## iter 10 value 245.306959
## iter 20 value 206.828887
## iter 30 value 173.884617
## iter 40 value 161.746294
## iter 50 value 155.832567
## iter 60 value 149.577969
## iter 70 value 143.174982
## iter 80 value 141.646090
## iter 90 value 141.483794
## iter 100 value 138.275734
## final value 138.275734
## stopped after 100 iterations
## # weights: 76
## initial value 518.762469
## iter 10 value 257.550158
## iter 20 value 202.565643
## iter 30 value 149.256946
## iter 40 value 113.373905
## iter 50 value 98.801823
## iter 60 value 82.174152
## iter 70 value 78.400378
## iter 80 value 77.373371
## iter 90 value 76.998840
## iter 100 value 76.378277
## final value 76.378277
## stopped after 100 iterations
## # weights: 16
## initial value 559.096742
## iter 10 value 369.800126
## iter 20 value 344.783147
## iter 30 value 337.383639
## iter 40 value 332.522251
## iter 50 value 331.664606
## iter 60 value 327.847992
## iter 70 value 327.803363
## final value 327.803237
## converged
## # weights: 46
## initial value 543.850931
## iter 10 value 244.529590
## iter 20 value 204.084185
## iter 30 value 173.249662
## iter 40 value 160.002745
## iter 50 value 143.125825
## iter 60 value 142.284238
## iter 70 value 142.233539
## final value 142.232552
## converged
## # weights: 76
## initial value 502.556393
## iter 10 value 229.147717
## iter 20 value 147.499287
## iter 30 value 110.024520
## iter 40 value 99.807889
## iter 50 value 92.752324
## iter 60 value 89.974555
## iter 70 value 89.572409
## iter 80 value 89.470780
## iter 90 value 89.352961
## iter 100 value 89.259769
## final value 89.259769
## stopped after 100 iterations
## # weights: 16
## initial value 518.069970
## iter 10 value 286.442530
## iter 20 value 274.404217
## iter 30 value 269.219792
## iter 40 value 268.819955
## iter 50 value 268.716098
## final value 268.703625
## converged
## # weights: 46
## initial value 582.009179
## iter 10 value 270.861422
## iter 20 value 232.234958
## iter 30 value 217.742956
## iter 40 value 214.920260
## iter 50 value 214.367571
## iter 60 value 214.231415
## iter 70 value 210.620447
## iter 80 value 200.333517
## iter 90 value 197.308703
## iter 100 value 196.606986
## final value 196.606986
## stopped after 100 iterations
## # weights: 76
## initial value 530.932300
## iter 10 value 243.951265
## iter 20 value 196.230751
## iter 30 value 177.180549
## iter 40 value 159.642547
## iter 50 value 140.553457
## iter 60 value 132.618413
## iter 70 value 129.326546
## iter 80 value 124.452777
## iter 90 value 120.765627
## iter 100 value 120.232789
## final value 120.232789
## stopped after 100 iterations
## # weights: 16
## initial value 513.746806
## iter 10 value 268.924104
## iter 20 value 264.275618
## iter 30 value 261.729953
## iter 40 value 261.362500
## iter 50 value 261.279036
## iter 60 value 261.043895
## iter 70 value 260.962341
## iter 80 value 260.955843
## iter 90 value 260.943382
## iter 100 value 260.930525
## final value 260.930525
## stopped after 100 iterations
## # weights: 46
## initial value 524.882824
## iter 10 value 241.721132
## iter 20 value 216.918797
## iter 30 value 186.758670
## iter 40 value 166.535187
## iter 50 value 165.758067
## iter 60 value 165.601208
## iter 70 value 165.455919
## iter 80 value 165.296831
## iter 90 value 164.856070
## iter 100 value 164.750469
## final value 164.750469
## stopped after 100 iterations
## # weights: 76
## initial value 550.298627
## iter 10 value 263.163366
## iter 20 value 192.286713
## iter 30 value 112.433371
## iter 40 value 87.708667
## iter 50 value 82.130654
## iter 60 value 78.878375
## iter 70 value 76.451216
## iter 80 value 75.215191
## iter 90 value 73.980541
## iter 100 value 73.200062
## final value 73.200062
## stopped after 100 iterations
## # weights: 76
## initial value 562.653156
## iter 10 value 250.264508
## iter 20 value 206.842339
## iter 30 value 177.396601
## iter 40 value 152.857608
## iter 50 value 143.663315
## iter 60 value 140.198496
## iter 70 value 138.726731
## iter 80 value 138.234294
## iter 90 value 138.060163
## iter 100 value 138.051778
## final value 138.051778
## stopped after 100 iterationsresultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)
# Matriz de confusión
# Es una tabla de evaluación que desglosa el rendimietno del modelo de clasificación
#Matriz de Confusión del Resultado de Entrenamiento
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$target)
mcre6## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 396 8
## 1 4 413
##
## Accuracy : 0.9854
## 95% CI : (0.9746, 0.9924)
## No Information Rate : 0.5128
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9708
##
## Mcnemar's Test P-Value : 0.3865
##
## Sensitivity : 0.9900
## Specificity : 0.9810
## Pos Pred Value : 0.9802
## Neg Pred Value : 0.9904
## Prevalence : 0.4872
## Detection Rate : 0.4823
## Detection Prevalence : 0.4921
## Balanced Accuracy : 0.9855
##
## 'Positive' Class : 0
## #Matriz de confusión del Resultado de la Prueba
mcrp6 <- confusionMatrix (resultado_prueba6, prueba$target)
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 99 7
## 1 0 98
##
## Accuracy : 0.9657
## 95% CI : (0.9306, 0.9861)
## No Information Rate : 0.5147
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.9315
##
## Mcnemar's Test P-Value : 0.02334
##
## Sensitivity : 1.0000
## Specificity : 0.9333
## Pos Pred Value : 0.9340
## Neg Pred Value : 1.0000
## Prevalence : 0.4853
## Detection Rate : 0.4853
## Detection Prevalence : 0.5196
## Balanced Accuracy : 0.9667
##
## 'Positive' Class : 0
## Tabla de resultados
resultados <- data.frame(
svmLinear = unname(c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"])),
svmRadial = unname(c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"])),
svmPoly = unname(c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"])),
rpart = unname(c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"])),
rf = unname(c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"])),
nnet = unname(c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"]))
)
rownames(resultados) <- c("Exactitud del Entrenamiento", "Exactitud de la Prueba")
resultados## svmLinear svmRadial svmPoly rpart rf
## Exactitud del Entrenamiento 0.8343484 0.9220463 0.9853837 0.9244823 1
## Exactitud de la Prueba 0.8480392 0.9215686 0.9705882 0.8823529 1
## nnet
## Exactitud del Entrenamiento 0.9853837
## Exactitud de la Prueba 0.9656863Conclusión
En conclusión, el modelo de Bosques Aleatorios es el recomendado para la clasificación de pacientes con enfermedad cardiaca.