Machine Learning: BreastCancer
David Heredia Sánchez
2024-03-02
Teoría
La función caret (Classification And Regression Training) es un paquete integral con una amplía variedad de algoritmos para el aprendizaje automático.
Obtención y entendimiento de los datos
Librerías
library(ggplot2) # Gráficas con mejor diseño
library(lattice) # Crear gráficos
library(caret) # Algoritmos de aprendizaje automático
library(datasets) # Usar la base de datos "Iris"
library(DataExplorer) # Análisis exploratorio de los datos
#install.packages("kernlab")
#install.packages("mlbench")
library(mlbench)
library(kernlab)
library(tidyverse)
library(kableExtra)
library(dplyr)Obetener la base de datos
data(BreastCancer)
df = data.frame(BreastCancer)
df_original = data.frame(BreastCancer) %>%
select(-Id)EDA
summary(df)## Id Cl.thickness Cell.size Cell.shape Marg.adhesion
## Length:699 1 :145 1 :384 1 :353 1 :407
## Class :character 5 :130 10 : 67 2 : 59 2 : 58
## Mode :character 3 :108 3 : 52 10 : 58 3 : 58
## 4 : 80 2 : 45 3 : 56 10 : 55
## 10 : 69 4 : 40 4 : 44 4 : 33
## 2 : 50 5 : 30 5 : 34 8 : 25
## (Other):117 (Other): 81 (Other): 95 (Other): 63
## Epith.c.size Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses
## 2 :386 1 :402 2 :166 1 :443 1 :579
## 3 : 72 10 :132 3 :165 10 : 61 2 : 35
## 4 : 48 2 : 30 1 :152 3 : 44 3 : 33
## 1 : 47 5 : 30 7 : 73 2 : 36 10 : 14
## 6 : 41 3 : 28 4 : 40 8 : 24 4 : 12
## 5 : 39 (Other): 61 5 : 34 6 : 22 7 : 9
## (Other): 66 NA's : 16 (Other): 69 (Other): 69 (Other): 17
## Class
## benign :458
## malignant:241
##
##
##
##
## # Con las estadísticas descriptiva de las variables explicativas se puede considerar que manejan una buena distribución, la variable que puede estar más afectada sería "Petal.Length" al tener los datos cargados hacía arriba del promedio.
str(df) # Confirmar que las variables vengan en el formato necesario.## 'data.frame': 699 obs. of 11 variables:
## $ Id : chr "1000025" "1002945" "1015425" "1016277" ...
## $ Cl.thickness : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 5 5 3 6 4 8 1 2 2 4 ...
## $ Cell.size : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 4 1 8 1 10 1 1 1 2 ...
## $ Cell.shape : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 4 1 8 1 10 1 2 1 1 ...
## $ Marg.adhesion : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 5 1 1 3 8 1 1 1 1 ...
## $ Epith.c.size : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 2 7 2 3 2 7 2 2 2 2 ...
## $ Bare.nuclei : Factor w/ 10 levels "1","2","3","4",..: 1 10 2 4 1 10 10 1 1 1 ...
## $ Bl.cromatin : Factor w/ 10 levels "1","2","3","4",..: 3 3 3 3 3 9 3 3 1 2 ...
## $ Normal.nucleoli: Factor w/ 10 levels "1","2","3","4",..: 1 2 1 7 1 7 1 1 1 1 ...
## $ Mitoses : Factor w/ 9 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 5 1 ...
## $ Class : Factor w/ 2 levels "benign","malignant": 1 1 1 1 1 2 1 1 1 1 ...#create_report(df)
plot_missing(df)
#Convertiremos los valor del df a continuos
df <- as.data.frame(lapply(df, as.numeric))
plot_boxplot(df, by = "Cell.size")## Warning: Removed 16 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
plot_histogram(df)
plot_bar(df)
plot_correlation(df)## Warning: Removed 20 rows containing missing values or values outside the scale range
## (`geom_text()`).
Uso de CARET
Partición de datos
df_original <- na.omit(df_original)
df_original <- df_original[complete.cases(df_original), ]
set.seed(123)
DataPart= createDataPartition(df_original$Class, p=0.8, list = FALSE)
train_set= df_original[DataPart,]
test_set = df_original[-DataPart,]Métodos para Modelar
Los métodos más utilizados para modelar aprendixaje automático son:
- SVM: Support Vector Machine o Máquina de
Vectores de soporte. Hay varios subtipos:
- Lineal (svmLinear)
- Radial (svmRadial)
- Polinómico (svmPoly)
- Lineal (svmLinear)
- Árbol de decisión: rpart
- Redes Neuronales: nnet
- Random Forests: rf
SVM Lineal
Entrenamiento de Modelo
modelo_svml = train(Class ~., data = train_set,
method = "svmLinear",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number =10),
tuneGrid = data.frame(C=1)
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei6## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.Resultados de Modelo
resultados_train_svml = predict(modelo_svml, train_set)
resultados_test_svml = predict(modelo_svml, test_set)Matriz de Confusión
##### ENTRENAMIENTO ######
MCRE_svml = confusionMatrix(resultados_train_svml, train_set$Class) # MCRE - Matriz de Confusión de Resultados de Entrenamiento.
#MCRE_svml
##### PRUEBA #####
resultados_test_svml <- factor(resultados_test_svml, levels = levels(train_set$Class))
MCRP_svml = confusionMatrix(resultados_test_svml, test_set$Class) # MCRE - Matriz de Confusión de Resultados de Prueba.
MCRP_svml## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 83 7
## malignant 5 40
##
## Accuracy : 0.9111
## 95% CI : (0.8499, 0.9532)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : 2.463e-12
##
## Kappa : 0.8022
##
## Mcnemar's Test P-Value : 0.7728
##
## Sensitivity : 0.9432
## Specificity : 0.8511
## Pos Pred Value : 0.9222
## Neg Pred Value : 0.8889
## Prevalence : 0.6519
## Detection Rate : 0.6148
## Detection Prevalence : 0.6667
## Balanced Accuracy : 0.8971
##
## 'Positive' Class : benign
## SVM Radial
Entrenamiento de Modelo
modelo_svmr = train(Class ~., data = train_set,
method = "svmRadial",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number =10),
tuneGrid = data.frame(sigma =1, C=1)
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Mitoses6## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei6## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.Resultados de Modelo
resultados_train_svmr = predict(modelo_svmr, train_set)
resultados_test_svmr = predict(modelo_svmr, test_set)Matriz de Confusión
##### ENTRENAMIENTO ######
MCRE_svmr = confusionMatrix(resultados_train_svmr, train_set$Class) # MCRE - Matriz de Confusión de Resultados de Entrenamiento.
#MCRE_svmr
##### PRUEBA #####
MCRP_svmr = confusionMatrix(resultados_test_svmr, test_set$Class) # MCRE - Matriz de Confusión de Resultados de Prueba.
MCRP_svmr## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 47 0
## malignant 41 47
##
## Accuracy : 0.6963
## 95% CI : (0.6113, 0.7724)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : 0.1602
##
## Kappa : 0.4439
##
## Mcnemar's Test P-Value : 4.185e-10
##
## Sensitivity : 0.5341
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.5341
## Prevalence : 0.6519
## Detection Rate : 0.3481
## Detection Prevalence : 0.3481
## Balanced Accuracy : 0.7670
##
## 'Positive' Class : benign
## SVM Polinómico
Entrenamiento de Modelo
modelo_svmp = train(Class ~., data = train_set,
method = "svmPoly",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number =10),
tuneGrid = data.frame(degree = 1, scale = 1, C= 1)
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei6## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.Resultados de Modelo
resultados_train_svmp = predict(modelo_svmp, train_set)
resultados_test_svmp = predict(modelo_svmp, test_set)Matriz de Confusión
##### ENTRENAMIENTO ######
MCRE_svmp = confusionMatrix(resultados_train_svmp, train_set$Class) # MCRE - Matriz de Confusión de Resultados de Entrenamiento.
#MCRE_svmp
##### PRUEBA #####
MCRP_svmp = confusionMatrix(resultados_test_svmp, test_set$Class) # MCRE - Matriz de Confusión de Resultados de Prueba.
MCRP_svmp## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 83 7
## malignant 5 40
##
## Accuracy : 0.9111
## 95% CI : (0.8499, 0.9532)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : 2.463e-12
##
## Kappa : 0.8022
##
## Mcnemar's Test P-Value : 0.7728
##
## Sensitivity : 0.9432
## Specificity : 0.8511
## Pos Pred Value : 0.9222
## Neg Pred Value : 0.8889
## Prevalence : 0.6519
## Detection Rate : 0.6148
## Detection Prevalence : 0.6667
## Balanced Accuracy : 0.8971
##
## 'Positive' Class : benign
## Árbol de decisión
Entrenamiento de Modelo
modelo_ad = train(Class ~., data = train_set,
method = "rpart",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number =10),
tuneLength = 10
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: Bare.nuclei6Resultados de Modelo
resultados_train_ad = predict(modelo_ad, train_set)
resultados_test_ad = predict(modelo_ad, test_set)Matriz de Confusión
##### ENTRENAMIENTO ######
MCRE_ad = confusionMatrix(resultados_train_ad, train_set$Class) # MCRE - Matriz de Confusión de Resultados de Entrenamiento.
#MCRE_ad
##### PRUEBA #####
MCRP_ad = confusionMatrix(resultados_test_ad, test_set$Class) # MCRE - Matriz de Confusión de Resultados de Prueba.
MCRP_ad## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 85 5
## malignant 3 42
##
## Accuracy : 0.9407
## 95% CI : (0.8866, 0.9741)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : 1.347e-15
##
## Kappa : 0.8681
##
## Mcnemar's Test P-Value : 0.7237
##
## Sensitivity : 0.9659
## Specificity : 0.8936
## Pos Pred Value : 0.9444
## Neg Pred Value : 0.9333
## Prevalence : 0.6519
## Detection Rate : 0.6296
## Detection Prevalence : 0.6667
## Balanced Accuracy : 0.9298
##
## 'Positive' Class : benign
## Redes Neuronales
Entrenamiento de Modelo
modelo_rn = train(Class ~., data = train_set,
method = "nnet",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number =10)
)## # weights: 83
## initial value 374.817257
## iter 10 value 40.277258
## iter 20 value 20.528253
## iter 30 value 19.231263
## iter 40 value 19.122754
## iter 50 value 19.114539
## iter 60 value 19.114088
## final value 19.113898
## converged
## # weights: 247
## initial value 324.313266
## iter 10 value 47.023454
## iter 20 value 34.501072
## iter 30 value 30.146446
## iter 40 value 29.471500
## iter 50 value 29.362689
## iter 60 value 28.692415
## iter 70 value 28.112819
## iter 80 value 28.042757
## iter 90 value 26.571126
## iter 100 value 26.559069
## final value 26.559069
## stopped after 100 iterations
## # weights: 411
## initial value 316.180536
## iter 10 value 13.913762
## iter 20 value 4.328973
## iter 30 value 2.552363
## iter 40 value 2.272730
## iter 50 value 1.922754
## iter 60 value 1.917273
## iter 70 value 1.911679
## iter 80 value 1.910533
## iter 90 value 1.909607
## iter 100 value 1.388215
## final value 1.388215
## stopped after 100 iterations
## # weights: 83
## initial value 316.912067
## iter 10 value 86.012268
## iter 20 value 59.597247
## iter 30 value 44.365711
## iter 40 value 33.669721
## iter 50 value 31.024497
## iter 60 value 26.401233
## iter 70 value 20.191166
## iter 80 value 18.258152
## iter 90 value 18.052746
## iter 100 value 18.042104
## final value 18.042104
## stopped after 100 iterations
## # weights: 247
## initial value 382.187522
## iter 10 value 103.663294
## iter 20 value 45.264551
## iter 30 value 27.392813
## iter 40 value 15.945273
## iter 50 value 10.439110
## iter 60 value 10.147304
## iter 70 value 10.070855
## iter 80 value 10.066895
## iter 90 value 10.066742
## iter 90 value 10.066742
## iter 90 value 10.066742
## final value 10.066742
## converged
## # weights: 411
## initial value 388.651583
## iter 10 value 81.178316
## iter 20 value 38.660423
## iter 30 value 22.159086
## iter 40 value 16.224888
## iter 50 value 13.883473
## iter 60 value 12.893250
## iter 70 value 10.005248
## iter 80 value 9.662003
## iter 90 value 9.454406
## iter 100 value 9.248063
## final value 9.248063
## stopped after 100 iterations
## # weights: 83
## initial value 297.409901
## iter 10 value 42.618141
## iter 20 value 26.253328
## iter 30 value 25.877399
## iter 40 value 25.874604
## iter 50 value 25.873501
## iter 60 value 25.868274
## iter 70 value 25.594741
## iter 80 value 22.368662
## iter 90 value 21.979915
## iter 100 value 21.974606
## final value 21.974606
## stopped after 100 iterations
## # weights: 247
## initial value 328.290262
## iter 10 value 42.653126
## iter 20 value 10.093193
## iter 30 value 7.568135
## iter 40 value 6.380110
## iter 50 value 6.342135
## iter 60 value 6.324702
## iter 70 value 6.307810
## iter 80 value 6.293637
## iter 90 value 4.841368
## iter 100 value 3.185512
## final value 3.185512
## stopped after 100 iterations
## # weights: 411
## initial value 321.689898
## iter 10 value 48.178152
## iter 20 value 17.682923
## iter 30 value 4.351793
## iter 40 value 3.602683
## iter 50 value 3.472430
## iter 60 value 3.423438
## iter 70 value 3.383050
## iter 80 value 3.353754
## iter 90 value 3.323043
## iter 100 value 3.166320
## final value 3.166320
## stopped after 100 iterations
## # weights: 83
## initial value 419.727174
## iter 10 value 74.663969
## iter 20 value 48.847083
## iter 30 value 45.476746
## iter 40 value 45.475792
## iter 50 value 45.475748
## final value 45.475721
## converged
## # weights: 247
## initial value 304.136827
## iter 10 value 25.425733
## iter 20 value 16.688368
## iter 30 value 15.524231
## iter 40 value 14.921821
## iter 50 value 14.900297
## iter 60 value 14.893053
## iter 70 value 14.889777
## iter 80 value 11.899490
## iter 90 value 11.491066
## iter 100 value 11.328002
## final value 11.328002
## stopped after 100 iterations
## # weights: 411
## initial value 291.136519
## iter 10 value 22.546166
## iter 20 value 5.471588
## iter 30 value 1.532441
## iter 40 value 1.399044
## iter 50 value 1.387959
## iter 60 value 1.386394
## iter 70 value 1.386336
## iter 80 value 1.386298
## final value 1.386295
## converged
## # weights: 83
## initial value 413.785797
## iter 10 value 102.460326
## iter 20 value 59.701876
## iter 30 value 53.757802
## iter 40 value 45.054277
## iter 50 value 28.961053
## iter 60 value 25.149845
## iter 70 value 21.716915
## iter 80 value 21.448553
## iter 90 value 21.444065
## final value 21.444012
## converged
## # weights: 247
## initial value 370.238660
## iter 10 value 62.186622
## iter 20 value 29.899986
## iter 30 value 21.399408
## iter 40 value 19.422549
## iter 50 value 17.939166
## iter 60 value 15.887346
## iter 70 value 14.034416
## iter 80 value 13.021269
## iter 90 value 12.430265
## iter 100 value 12.305054
## final value 12.305054
## stopped after 100 iterations
## # weights: 411
## initial value 366.834292
## iter 10 value 36.692684
## iter 20 value 18.481860
## iter 30 value 12.762517
## iter 40 value 11.980784
## iter 50 value 10.544337
## iter 60 value 10.219762
## iter 70 value 9.887662
## iter 80 value 9.855872
## iter 90 value 9.854914
## final value 9.854908
## converged
## # weights: 83
## initial value 320.843027
## iter 10 value 61.752303
## iter 20 value 51.177507
## iter 30 value 42.996377
## iter 40 value 42.590018
## iter 50 value 39.535969
## iter 60 value 39.531943
## iter 70 value 36.359822
## iter 80 value 36.355570
## iter 90 value 36.344210
## iter 100 value 33.029190
## final value 33.029190
## stopped after 100 iterations
## # weights: 247
## initial value 332.658071
## iter 10 value 45.061867
## iter 20 value 30.592706
## iter 30 value 24.292670
## iter 40 value 22.603491
## iter 50 value 17.610480
## iter 60 value 16.318104
## iter 70 value 16.282688
## iter 80 value 15.449802
## iter 90 value 15.204580
## iter 100 value 14.975435
## final value 14.975435
## stopped after 100 iterations
## # weights: 411
## initial value 337.430632
## iter 10 value 26.479043
## iter 20 value 19.485630
## iter 30 value 13.285569
## iter 40 value 8.671851
## iter 50 value 6.111552
## iter 60 value 5.431410
## iter 70 value 5.412742
## iter 80 value 4.959811
## iter 90 value 4.947497
## iter 100 value 4.319016
## final value 4.319016
## stopped after 100 iterations
## # weights: 83
## initial value 289.617667
## iter 10 value 47.158954
## iter 20 value 33.294405
## iter 30 value 29.526587
## iter 40 value 29.518883
## iter 50 value 29.517363
## iter 60 value 25.856120
## iter 70 value 25.849244
## iter 80 value 25.848727
## iter 90 value 25.848612
## iter 100 value 25.848458
## final value 25.848458
## stopped after 100 iterations
## # weights: 247
## initial value 315.908486
## iter 10 value 35.879313
## iter 20 value 25.360795
## iter 30 value 21.617253
## iter 40 value 20.441143
## iter 50 value 20.400365
## iter 60 value 18.869402
## iter 70 value 17.846714
## iter 80 value 17.491599
## iter 90 value 17.435652
## iter 100 value 17.427602
## final value 17.427602
## stopped after 100 iterations
## # weights: 411
## initial value 405.971636
## iter 10 value 33.589273
## iter 20 value 17.184931
## iter 30 value 7.680320
## iter 40 value 7.507280
## iter 50 value 7.496789
## iter 60 value 7.494826
## iter 70 value 6.179566
## iter 80 value 6.153885
## iter 90 value 4.804459
## iter 100 value 4.771136
## final value 4.771136
## stopped after 100 iterations
## # weights: 83
## initial value 380.174551
## iter 10 value 77.127696
## iter 20 value 37.400692
## iter 30 value 32.492526
## iter 40 value 30.577549
## iter 50 value 23.055923
## iter 60 value 22.466776
## iter 70 value 22.452999
## final value 22.452915
## converged
## # weights: 247
## initial value 431.310146
## iter 10 value 68.165421
## iter 20 value 40.578145
## iter 30 value 27.327389
## iter 40 value 20.378150
## iter 50 value 13.623395
## iter 60 value 12.383523
## iter 70 value 12.208413
## iter 80 value 12.156943
## iter 90 value 12.152864
## iter 100 value 12.152542
## final value 12.152542
## stopped after 100 iterations
## # weights: 411
## initial value 401.218439
## iter 10 value 52.937074
## iter 20 value 31.105349
## iter 30 value 16.667632
## iter 40 value 11.399958
## iter 50 value 10.135009
## iter 60 value 10.020440
## iter 70 value 9.984186
## iter 80 value 9.978552
## iter 90 value 9.977362
## iter 100 value 9.976882
## final value 9.976882
## stopped after 100 iterations
## # weights: 83
## initial value 317.890696
## iter 10 value 94.045981
## iter 20 value 85.559470
## iter 30 value 82.801458
## iter 40 value 65.548982
## iter 50 value 59.999511
## iter 60 value 59.089728
## iter 70 value 48.415218
## iter 80 value 48.406938
## iter 90 value 46.569118
## iter 100 value 45.542977
## final value 45.542977
## stopped after 100 iterations
## # weights: 247
## initial value 438.130620
## iter 10 value 30.052670
## iter 20 value 15.946457
## iter 30 value 9.704185
## iter 40 value 4.630931
## iter 50 value 3.775588
## iter 60 value 3.083640
## iter 70 value 2.855997
## iter 80 value 2.850302
## iter 90 value 2.844159
## iter 100 value 2.839119
## final value 2.839119
## stopped after 100 iterations
## # weights: 411
## initial value 311.704954
## iter 10 value 24.936060
## iter 20 value 18.865109
## iter 30 value 15.233004
## iter 40 value 12.816873
## iter 50 value 11.562671
## iter 60 value 10.592253
## iter 70 value 10.506194
## iter 80 value 10.394328
## iter 90 value 10.378309
## iter 100 value 10.348374
## final value 10.348374
## stopped after 100 iterations
## # weights: 83
## initial value 419.979920
## iter 10 value 133.821113
## iter 20 value 96.552511
## iter 30 value 67.513382
## iter 40 value 42.860836
## iter 50 value 33.739828
## iter 60 value 27.284994
## iter 70 value 23.052282
## iter 80 value 23.048354
## iter 90 value 23.047837
## iter 100 value 23.047539
## final value 23.047539
## stopped after 100 iterations
## # weights: 247
## initial value 361.645456
## iter 10 value 39.759559
## iter 20 value 23.011713
## iter 30 value 20.490660
## iter 40 value 15.024995
## iter 50 value 14.032866
## iter 60 value 10.551836
## iter 70 value 10.540033
## iter 80 value 10.532935
## iter 90 value 9.324989
## iter 100 value 9.283476
## final value 9.283476
## stopped after 100 iterations
## # weights: 411
## initial value 339.782310
## iter 10 value 20.786801
## iter 20 value 9.646829
## iter 30 value 6.891382
## iter 40 value 4.804879
## iter 50 value 4.784001
## iter 60 value 4.780069
## iter 70 value 3.035339
## iter 80 value 2.871294
## iter 90 value 2.870941
## iter 100 value 2.870156
## final value 2.870156
## stopped after 100 iterations
## # weights: 83
## initial value 345.726126
## iter 10 value 105.762598
## iter 20 value 45.849455
## iter 30 value 36.332657
## iter 40 value 31.082980
## iter 50 value 23.189272
## iter 60 value 21.976391
## iter 70 value 21.930180
## iter 80 value 21.927498
## final value 21.927477
## converged
## # weights: 247
## initial value 355.990955
## iter 10 value 89.762180
## iter 20 value 57.820168
## iter 30 value 40.116497
## iter 40 value 26.732173
## iter 50 value 17.792797
## iter 60 value 12.461963
## iter 70 value 11.348088
## iter 80 value 11.118615
## iter 90 value 10.770877
## iter 100 value 10.633591
## final value 10.633591
## stopped after 100 iterations
## # weights: 411
## initial value 495.278566
## iter 10 value 71.563946
## iter 20 value 28.112043
## iter 30 value 14.038774
## iter 40 value 10.130982
## iter 50 value 9.554676
## iter 60 value 9.486053
## iter 70 value 9.457476
## iter 80 value 9.420064
## iter 90 value 9.419817
## final value 9.419817
## converged
## # weights: 83
## initial value 342.487766
## iter 10 value 257.033401
## final value 256.758671
## converged
## # weights: 247
## initial value 492.098679
## iter 10 value 40.567340
## iter 20 value 30.530807
## iter 30 value 27.810847
## iter 40 value 27.762340
## iter 50 value 26.570957
## iter 60 value 26.543180
## iter 70 value 26.510429
## iter 80 value 26.443510
## iter 90 value 26.417175
## iter 100 value 25.446776
## final value 25.446776
## stopped after 100 iterations
## # weights: 411
## initial value 367.631331
## iter 10 value 25.657541
## iter 20 value 14.225380
## iter 30 value 10.225640
## iter 40 value 7.491544
## iter 50 value 4.866641
## iter 60 value 4.513469
## iter 70 value 4.147146
## iter 80 value 4.101431
## iter 90 value 4.076008
## iter 100 value 4.039355
## final value 4.039355
## stopped after 100 iterations
## # weights: 83
## initial value 354.419667
## iter 10 value 99.689731
## iter 20 value 98.378651
## final value 98.378414
## converged
## # weights: 247
## initial value 325.922554
## iter 10 value 51.473563
## iter 20 value 48.223332
## iter 30 value 42.504973
## iter 40 value 36.021855
## iter 50 value 35.978867
## iter 60 value 35.831277
## iter 70 value 35.253514
## iter 80 value 33.962166
## iter 90 value 28.256102
## iter 100 value 15.900058
## final value 15.900058
## stopped after 100 iterations
## # weights: 411
## initial value 414.781757
## iter 10 value 76.752961
## iter 20 value 21.828070
## iter 30 value 16.649624
## iter 40 value 13.610616
## iter 50 value 11.013184
## iter 60 value 10.887896
## iter 70 value 10.867303
## iter 80 value 10.864170
## iter 90 value 10.862884
## iter 100 value 10.862073
## final value 10.862073
## stopped after 100 iterations
## # weights: 83
## initial value 319.226075
## iter 10 value 238.866241
## iter 20 value 180.105906
## iter 30 value 119.341965
## iter 40 value 72.869791
## iter 50 value 61.401800
## iter 60 value 40.444304
## iter 70 value 32.939855
## iter 80 value 25.597118
## iter 90 value 21.820267
## iter 100 value 21.307368
## final value 21.307368
## stopped after 100 iterations
## # weights: 247
## initial value 513.625249
## iter 10 value 100.632572
## iter 20 value 55.916632
## iter 30 value 27.289044
## iter 40 value 19.706344
## iter 50 value 13.447144
## iter 60 value 12.344060
## iter 70 value 11.514262
## iter 80 value 11.154356
## iter 90 value 10.882485
## iter 100 value 10.558397
## final value 10.558397
## stopped after 100 iterations
## # weights: 411
## initial value 351.009442
## iter 10 value 47.911452
## iter 20 value 23.444193
## iter 30 value 14.685997
## iter 40 value 12.963928
## iter 50 value 11.724508
## iter 60 value 10.937526
## iter 70 value 10.577180
## iter 80 value 10.260275
## iter 90 value 10.179908
## iter 100 value 10.169759
## final value 10.169759
## stopped after 100 iterations
## # weights: 83
## initial value 421.364077
## iter 10 value 94.620012
## iter 20 value 54.857573
## iter 30 value 33.171241
## iter 40 value 33.118527
## iter 50 value 26.442386
## iter 60 value 26.411388
## iter 70 value 26.389198
## iter 80 value 22.894266
## iter 90 value 22.883768
## iter 100 value 22.868176
## final value 22.868176
## stopped after 100 iterations
## # weights: 247
## initial value 286.203235
## iter 10 value 26.882168
## iter 20 value 15.906937
## iter 30 value 15.272851
## iter 40 value 15.260041
## iter 50 value 15.252532
## iter 60 value 15.242600
## iter 70 value 15.079319
## iter 80 value 11.843697
## iter 90 value 0.698799
## iter 100 value 0.295308
## final value 0.295308
## stopped after 100 iterations
## # weights: 411
## initial value 311.773452
## iter 10 value 23.101437
## iter 20 value 6.975892
## iter 30 value 4.682807
## iter 40 value 4.191780
## iter 50 value 3.916041
## iter 60 value 3.524213
## iter 70 value 3.427861
## iter 80 value 3.357885
## iter 90 value 3.275867
## iter 100 value 3.142307
## final value 3.142307
## stopped after 100 iterations
## # weights: 83
## initial value 322.435701
## iter 10 value 26.729383
## iter 20 value 26.307331
## iter 30 value 26.285491
## iter 40 value 26.274633
## iter 50 value 26.273534
## iter 60 value 24.380001
## iter 70 value 22.802557
## iter 80 value 22.791102
## iter 90 value 19.119952
## iter 100 value 19.114531
## final value 19.114531
## stopped after 100 iterations
## # weights: 247
## initial value 455.309017
## iter 10 value 28.751759
## iter 20 value 21.876872
## iter 30 value 20.643283
## iter 40 value 19.872594
## iter 50 value 19.867350
## iter 60 value 15.058984
## iter 70 value 15.032997
## iter 80 value 15.025806
## iter 90 value 15.021036
## iter 100 value 15.009302
## final value 15.009302
## stopped after 100 iterations
## # weights: 411
## initial value 331.969586
## iter 10 value 38.836084
## iter 20 value 20.650971
## iter 30 value 18.913820
## iter 40 value 18.298981
## iter 50 value 9.695484
## iter 60 value 9.309546
## iter 70 value 9.296272
## iter 80 value 9.272530
## iter 90 value 8.923560
## iter 100 value 8.701402
## final value 8.701402
## stopped after 100 iterations
## # weights: 83
## initial value 388.344855
## iter 10 value 113.740894
## iter 20 value 61.705607
## iter 30 value 45.449012
## iter 40 value 32.075506
## iter 50 value 24.838923
## iter 60 value 24.390120
## iter 70 value 21.426762
## iter 80 value 20.903621
## iter 90 value 20.877099
## iter 100 value 20.876572
## final value 20.876572
## stopped after 100 iterations
## # weights: 247
## initial value 394.091643
## iter 10 value 80.590034
## iter 20 value 48.460155
## iter 30 value 27.518941
## iter 40 value 16.462677
## iter 50 value 11.273452
## iter 60 value 10.400797
## iter 70 value 10.305115
## iter 80 value 10.303060
## final value 10.303039
## converged
## # weights: 411
## initial value 351.749855
## iter 10 value 55.697025
## iter 20 value 22.350030
## iter 30 value 12.896713
## iter 40 value 10.176409
## iter 50 value 9.126635
## iter 60 value 8.718207
## iter 70 value 8.545336
## iter 80 value 8.502993
## iter 90 value 8.502663
## final value 8.502662
## converged
## # weights: 83
## initial value 336.414617
## iter 10 value 33.263736
## iter 20 value 22.848567
## iter 30 value 22.839049
## iter 40 value 22.838354
## iter 50 value 22.837767
## iter 60 value 22.837027
## iter 70 value 22.836404
## iter 80 value 22.835758
## iter 90 value 22.835447
## iter 100 value 22.835019
## final value 22.835019
## stopped after 100 iterations
## # weights: 247
## initial value 377.485777
## iter 10 value 34.310207
## iter 20 value 30.598283
## iter 30 value 30.545189
## iter 40 value 29.146090
## iter 50 value 29.123387
## iter 60 value 29.044300
## iter 70 value 28.923384
## iter 80 value 28.884158
## iter 90 value 28.844118
## iter 100 value 27.704976
## final value 27.704976
## stopped after 100 iterations
## # weights: 411
## initial value 411.709452
## iter 10 value 26.045387
## iter 20 value 14.185456
## iter 30 value 12.073522
## iter 40 value 8.012715
## iter 50 value 7.490718
## iter 60 value 7.161884
## iter 70 value 6.967063
## iter 80 value 6.953860
## iter 90 value 3.365022
## iter 100 value 3.131535
## final value 3.131535
## stopped after 100 iterations
## # weights: 83
## initial value 326.762731
## iter 10 value 52.822433
## iter 20 value 44.532594
## iter 30 value 36.432835
## iter 40 value 29.650558
## iter 50 value 29.591894
## iter 60 value 29.591514
## final value 29.591456
## converged
## # weights: 247
## initial value 305.385896
## iter 10 value 32.491765
## iter 20 value 28.687909
## iter 30 value 25.123022
## iter 40 value 21.582183
## iter 50 value 21.176793
## iter 60 value 21.169624
## iter 70 value 21.161173
## iter 80 value 20.455655
## iter 90 value 20.352051
## iter 100 value 19.714648
## final value 19.714648
## stopped after 100 iterations
## # weights: 411
## initial value 314.904128
## iter 10 value 11.495235
## iter 20 value 0.165978
## iter 30 value 0.010403
## iter 40 value 0.001818
## iter 50 value 0.000502
## iter 60 value 0.000159
## final value 0.000094
## converged
## # weights: 83
## initial value 321.992454
## iter 10 value 47.875165
## iter 20 value 39.178441
## iter 30 value 33.470784
## iter 40 value 26.510248
## iter 50 value 22.283769
## iter 60 value 22.153735
## iter 70 value 22.151905
## final value 22.151901
## converged
## # weights: 247
## initial value 403.045283
## iter 10 value 88.647068
## iter 20 value 43.340460
## iter 30 value 15.546890
## iter 40 value 11.606355
## iter 50 value 10.847702
## iter 60 value 10.655124
## iter 70 value 10.543752
## iter 80 value 10.452011
## iter 90 value 10.443473
## iter 100 value 10.443054
## final value 10.443054
## stopped after 100 iterations
## # weights: 411
## initial value 357.724047
## iter 10 value 78.113176
## iter 20 value 34.107580
## iter 30 value 15.284666
## iter 40 value 10.892756
## iter 50 value 9.772909
## iter 60 value 9.414567
## iter 70 value 9.261929
## iter 80 value 9.154964
## iter 90 value 9.142335
## iter 100 value 9.124472
## final value 9.124472
## stopped after 100 iterations
## # weights: 83
## initial value 401.208768
## iter 10 value 87.761132
## iter 20 value 64.594346
## iter 30 value 43.382110
## iter 40 value 36.810775
## iter 50 value 36.678376
## iter 60 value 36.674536
## iter 70 value 36.671595
## iter 80 value 36.667541
## iter 90 value 27.347022
## iter 100 value 27.324806
## final value 27.324806
## stopped after 100 iterations
## # weights: 247
## initial value 324.932125
## iter 10 value 44.817970
## iter 20 value 39.182862
## iter 30 value 37.213365
## iter 40 value 33.121727
## iter 50 value 31.632994
## iter 60 value 30.945094
## iter 70 value 28.346355
## iter 80 value 27.856603
## iter 90 value 25.945977
## iter 100 value 25.521140
## final value 25.521140
## stopped after 100 iterations
## # weights: 411
## initial value 324.920496
## iter 10 value 13.573040
## iter 20 value 9.420639
## iter 30 value 7.798172
## iter 40 value 4.015519
## iter 50 value 3.974850
## iter 60 value 3.960192
## iter 70 value 3.950770
## iter 80 value 3.930923
## iter 90 value 2.071583
## iter 100 value 2.048065
## final value 2.048065
## stopped after 100 iterations
## # weights: 83
## initial value 383.945027
## iter 10 value 61.262747
## iter 20 value 43.405468
## iter 30 value 36.312286
## iter 40 value 36.302712
## final value 36.302676
## converged
## # weights: 247
## initial value 344.609826
## iter 10 value 34.818943
## iter 20 value 10.884745
## iter 30 value 10.073753
## iter 40 value 10.005973
## iter 50 value 9.993395
## iter 60 value 9.987045
## iter 70 value 9.985209
## iter 80 value 9.980488
## iter 90 value 9.975887
## iter 100 value 9.973602
## final value 9.973602
## stopped after 100 iterations
## # weights: 411
## initial value 345.481847
## iter 10 value 40.339016
## iter 20 value 16.790916
## iter 30 value 16.048132
## iter 40 value 13.756416
## iter 50 value 13.722467
## iter 60 value 11.382532
## iter 70 value 11.284387
## iter 80 value 9.477437
## iter 90 value 6.922980
## iter 100 value 6.088043
## final value 6.088043
## stopped after 100 iterations
## # weights: 83
## initial value 350.487178
## iter 10 value 203.529124
## iter 20 value 123.523212
## iter 30 value 91.670398
## iter 40 value 70.725364
## iter 50 value 49.348123
## iter 60 value 38.426478
## iter 70 value 29.125238
## iter 80 value 21.949885
## iter 90 value 21.110607
## iter 100 value 21.068592
## final value 21.068592
## stopped after 100 iterations
## # weights: 247
## initial value 351.445527
## iter 10 value 41.584238
## iter 20 value 23.275974
## iter 30 value 16.972688
## iter 40 value 13.692530
## iter 50 value 11.761739
## iter 60 value 11.707056
## iter 70 value 11.705014
## iter 80 value 11.704961
## iter 80 value 11.704961
## iter 80 value 11.704961
## final value 11.704961
## converged
## # weights: 411
## initial value 272.614067
## iter 10 value 35.432430
## iter 20 value 14.561876
## iter 30 value 10.338165
## iter 40 value 9.311010
## iter 50 value 9.086595
## iter 60 value 9.052270
## iter 70 value 8.965391
## iter 80 value 8.886916
## iter 90 value 8.885054
## final value 8.885048
## converged
## # weights: 83
## initial value 335.319859
## iter 10 value 61.538804
## iter 20 value 36.382295
## iter 30 value 33.067065
## iter 40 value 33.060621
## iter 50 value 33.044561
## iter 60 value 33.036712
## iter 70 value 33.031922
## iter 80 value 33.021951
## iter 90 value 29.504528
## iter 100 value 25.879731
## final value 25.879731
## stopped after 100 iterations
## # weights: 247
## initial value 370.920549
## iter 10 value 26.062046
## iter 20 value 12.660154
## iter 30 value 8.415600
## iter 40 value 3.598680
## iter 50 value 1.666163
## iter 60 value 0.322298
## iter 70 value 0.229196
## iter 80 value 0.202559
## iter 90 value 0.190282
## iter 100 value 0.151807
## final value 0.151807
## stopped after 100 iterations
## # weights: 411
## initial value 390.652397
## iter 10 value 32.659016
## iter 20 value 19.924161
## iter 30 value 17.093988
## iter 40 value 16.078461
## iter 50 value 15.919828
## iter 60 value 14.743639
## iter 70 value 13.044147
## iter 80 value 10.404507
## iter 90 value 9.491522
## iter 100 value 8.992430
## final value 8.992430
## stopped after 100 iterations
## # weights: 83
## initial value 323.866630
## iter 10 value 242.796418
## iter 20 value 124.387128
## iter 30 value 91.543780
## iter 40 value 88.944513
## iter 50 value 88.831959
## iter 60 value 86.071753
## iter 70 value 86.070540
## iter 80 value 79.258909
## iter 90 value 68.227206
## iter 100 value 68.174449
## final value 68.174449
## stopped after 100 iterations
## # weights: 247
## initial value 418.060266
## iter 10 value 74.585597
## iter 20 value 24.498702
## iter 30 value 13.751412
## iter 40 value 3.789564
## iter 50 value 3.295703
## iter 60 value 3.157229
## iter 70 value 3.028643
## iter 80 value 3.023181
## iter 90 value 2.880953
## iter 100 value 2.876094
## final value 2.876094
## stopped after 100 iterations
## # weights: 411
## initial value 359.407168
## iter 10 value 28.817143
## iter 20 value 8.530030
## iter 30 value 6.358955
## iter 40 value 6.224511
## iter 50 value 6.204338
## iter 60 value 5.367965
## iter 70 value 4.785920
## iter 80 value 4.784189
## iter 90 value 4.782508
## iter 100 value 4.780933
## final value 4.780933
## stopped after 100 iterations
## # weights: 83
## initial value 361.801130
## iter 10 value 100.846163
## iter 20 value 68.653934
## iter 30 value 61.108285
## iter 40 value 54.144293
## iter 50 value 41.093110
## iter 60 value 36.260323
## iter 70 value 24.742730
## iter 80 value 22.235793
## iter 90 value 21.727434
## iter 100 value 21.650537
## final value 21.650537
## stopped after 100 iterations
## # weights: 247
## initial value 337.774398
## iter 10 value 89.614733
## iter 20 value 38.129368
## iter 30 value 20.926360
## iter 40 value 12.290090
## iter 50 value 11.397214
## iter 60 value 11.312696
## iter 70 value 11.307336
## iter 80 value 11.307243
## final value 11.307241
## converged
## # weights: 411
## initial value 377.705633
## iter 10 value 48.045188
## iter 20 value 26.397242
## iter 30 value 17.421726
## iter 40 value 12.201767
## iter 50 value 10.836739
## iter 60 value 10.200622
## iter 70 value 9.976117
## iter 80 value 9.825591
## iter 90 value 9.770976
## iter 100 value 9.762707
## final value 9.762707
## stopped after 100 iterations
## # weights: 83
## initial value 342.991901
## iter 10 value 75.526534
## iter 20 value 50.987318
## iter 30 value 46.474219
## iter 40 value 41.670675
## iter 50 value 38.423145
## iter 60 value 38.365522
## iter 70 value 38.361420
## iter 80 value 38.354202
## iter 90 value 38.347343
## iter 100 value 38.345319
## final value 38.345319
## stopped after 100 iterations
## # weights: 247
## initial value 327.856380
## iter 10 value 30.367253
## iter 20 value 25.592918
## iter 30 value 21.097637
## iter 40 value 20.046114
## iter 50 value 19.816784
## iter 60 value 15.068676
## iter 70 value 9.665097
## iter 80 value 9.183462
## iter 90 value 9.128038
## iter 100 value 8.927188
## final value 8.927188
## stopped after 100 iterations
## # weights: 411
## initial value 351.123164
## iter 10 value 25.977736
## iter 20 value 5.907212
## iter 30 value 2.630313
## iter 40 value 1.706659
## iter 50 value 0.394846
## iter 60 value 0.169993
## iter 70 value 0.141506
## iter 80 value 0.138905
## iter 90 value 0.134151
## iter 100 value 0.125704
## final value 0.125704
## stopped after 100 iterations
## # weights: 83
## initial value 318.880157
## iter 10 value 58.563511
## iter 20 value 47.196235
## iter 30 value 36.366908
## iter 40 value 36.306540
## iter 50 value 36.300610
## iter 60 value 36.136242
## iter 70 value 29.520299
## iter 80 value 29.517525
## iter 90 value 29.517040
## iter 100 value 29.516549
## final value 29.516549
## stopped after 100 iterations
## # weights: 247
## initial value 357.483818
## iter 10 value 89.915099
## iter 20 value 85.061469
## final value 85.061422
## converged
## # weights: 411
## initial value 333.629296
## iter 10 value 19.025901
## iter 20 value 12.964212
## iter 30 value 11.297203
## iter 40 value 9.954257
## iter 50 value 9.037558
## iter 60 value 8.973998
## iter 70 value 8.972601
## iter 80 value 8.324737
## iter 90 value 8.319041
## final value 8.317626
## converged
## # weights: 83
## initial value 392.915483
## iter 10 value 89.701773
## iter 20 value 68.413540
## iter 30 value 46.177130
## iter 40 value 35.438784
## iter 50 value 32.406963
## iter 60 value 26.340024
## iter 70 value 22.632118
## iter 80 value 21.689153
## iter 90 value 21.589338
## iter 100 value 21.580986
## final value 21.580986
## stopped after 100 iterations
## # weights: 247
## initial value 325.335884
## iter 10 value 69.473127
## iter 20 value 33.660683
## iter 30 value 21.240532
## iter 40 value 14.872928
## iter 50 value 13.797031
## iter 60 value 13.161008
## iter 70 value 12.528829
## iter 80 value 12.428804
## iter 90 value 12.401363
## iter 100 value 12.392418
## final value 12.392418
## stopped after 100 iterations
## # weights: 411
## initial value 353.835320
## iter 10 value 122.696207
## iter 20 value 89.558816
## iter 30 value 55.887725
## iter 40 value 28.341546
## iter 50 value 15.308638
## iter 60 value 12.562383
## iter 70 value 11.791538
## iter 80 value 11.512743
## iter 90 value 11.376500
## iter 100 value 11.190836
## final value 11.190836
## stopped after 100 iterations
## # weights: 83
## initial value 328.453160
## iter 10 value 74.848758
## iter 20 value 43.862520
## iter 30 value 33.091587
## iter 40 value 32.846212
## iter 50 value 32.842331
## iter 60 value 32.835865
## iter 70 value 32.827368
## iter 80 value 30.169689
## iter 90 value 29.647091
## iter 100 value 29.644096
## final value 29.644096
## stopped after 100 iterations
## # weights: 247
## initial value 483.674462
## iter 10 value 33.793108
## iter 20 value 19.208429
## iter 30 value 11.854500
## iter 40 value 9.717876
## iter 50 value 7.877034
## iter 60 value 7.704249
## iter 70 value 7.046863
## iter 80 value 7.034829
## iter 90 value 6.281851
## iter 100 value 5.585036
## final value 5.585036
## stopped after 100 iterations
## # weights: 411
## initial value 337.598349
## iter 10 value 47.603911
## iter 20 value 23.924252
## iter 30 value 19.537631
## iter 40 value 17.288119
## iter 50 value 15.515339
## iter 60 value 10.101210
## iter 70 value 4.198891
## iter 80 value 3.069069
## iter 90 value 2.690411
## iter 100 value 2.632615
## final value 2.632615
## stopped after 100 iterations
## # weights: 83
## initial value 440.007313
## iter 10 value 51.768164
## iter 20 value 47.345902
## iter 30 value 46.438616
## iter 40 value 40.472296
## iter 50 value 40.404240
## iter 60 value 40.399679
## iter 70 value 37.128505
## iter 80 value 37.127939
## iter 90 value 37.127831
## iter 90 value 37.127831
## iter 90 value 37.127831
## final value 37.127831
## convergedResultados de Modelo
resultados_train_rn = predict(modelo_rn, train_set)
resultados_test_rn = predict(modelo_rn, test_set)Matriz de Confusión
##### ENTRENAMIENTO ######
MCRE_rn = confusionMatrix(resultados_train_rn, train_set$Class) # MCRE - Matriz de Confusión de Resultados de Entrenamiento.
#MCRE_rn
##### PRUEBA #####
MCRP_rn = confusionMatrix(resultados_test_rn, test_set$Class) # MCRE - Matriz de Confusión de Resultados de Prueba.
MCRP_rn## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 84 3
## malignant 4 44
##
## Accuracy : 0.9481
## 95% CI : (0.8961, 0.9789)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8863
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9545
## Specificity : 0.9362
## Pos Pred Value : 0.9655
## Neg Pred Value : 0.9167
## Prevalence : 0.6519
## Detection Rate : 0.6222
## Detection Prevalence : 0.6444
## Balanced Accuracy : 0.9454
##
## 'Positive' Class : benign
## Random Forests
Entrenamiento de Modelo
modelo_rf = train(Class ~., data = train_set,
method = "rf",
preProcess = c("scale", "center"),
trControl = trainControl(method = "cv", number =10),
tuneGrid = expand.grid(mtry =c(2,4,6))
)Resultados de Modelo
resultados_train_rf = predict(modelo_rf, train_set)
resultados_test_rf = predict(modelo_rf, test_set)Matriz de Confusión
##### ENTRENAMIENTO ######
MCRE_rf = confusionMatrix(resultados_train_rf, train_set$Class) # MCRE - Matriz de Confusión de Resultados de Entrenamiento.
#MCRE_rf
##### PRUEBA #####
MCRP_rf = confusionMatrix(resultados_test_rf, test_set$Class) # MCRE - Matriz de Confusión de Resultados de Prueba.
MCRP_rf## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 85 3
## malignant 3 44
##
## Accuracy : 0.9556
## 95% CI : (0.9058, 0.9835)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9021
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9659
## Specificity : 0.9362
## Pos Pred Value : 0.9659
## Neg Pred Value : 0.9362
## Prevalence : 0.6519
## Detection Rate : 0.6296
## Detection Prevalence : 0.6519
## Balanced Accuracy : 0.9510
##
## 'Positive' Class : benign
## Comparativa entre Modelos
resultados = data.frame(
"svmLinear" = c(MCRE_svml$overall["Accuracy"],MCRP_svml$overall["Accuracy"]),
"svmRadial" = c(MCRE_svmr$overall["Accuracy"],MCRP_svmr$overall["Accuracy"]),
"svmPoly" = c(MCRE_svmp$overall["Accuracy"],MCRP_svmp$overall["Accuracy"]),
"Arboles" = c(MCRE_ad$overall["Accuracy"],MCRP_ad$overall["Accuracy"]),
"Redes" = c(MCRE_rn$overall["Accuracy"],MCRP_rn$overall["Accuracy"]),
"RandomForest" = c(MCRE_rf$overall["Accuracy"],MCRP_rf$overall["Accuracy"])
)
rownames(resultados) = c("Precisión de entrenamiento", "Precisión de prueba")
resultados## svmLinear svmRadial svmPoly Arboles Redes
## Precisión de entrenamiento 1.0000000 1.0000000 1.0000000 0.9580292 0.9854015
## Precisión de prueba 0.9111111 0.6962963 0.9111111 0.9407407 0.9481481
## RandomForest
## Precisión de entrenamiento 0.9963504
## Precisión de prueba 0.9555556Metodo<-c('SVM Linear','SVM Radial', 'SVM Poly', 'Árboles de decisión', 'Redes Neuronales', 'Random Forest')
ACC_Train<-c(MCRE_svml$overall["Accuracy"], MCRE_svmr$overall["Accuracy"], MCRE_svmp$overall["Accuracy"], MCRE_ad$overall["Accuracy"],MCRE_rn$overall["Accuracy"],MCRE_rf$overall["Accuracy"])
ACC_Test<-c(MCRP_svml$overall["Accuracy"], MCRP_svmr$overall["Accuracy"], MCRP_svmp$overall["Accuracy"], MCRP_ad$overall["Accuracy"],MCRP_rn$overall["Accuracy"],MCRP_rf$overall["Accuracy"])
RMSE_df<-data.frame(Metodo,ACC_Train, ACC_Test)
RMSE_df %>%
kbl() %>%
kable_styling()| Metodo | ACC_Train | ACC_Test |
|---|---|---|
| SVM Linear | 1.0000000 | 0.9111111 |
| SVM Radial | 1.0000000 | 0.6962963 |
| SVM Poly | 1.0000000 | 0.9111111 |
| Árboles de decisión | 0.9580292 | 0.9407407 |
| Redes Neuronales | 0.9854015 | 0.9481481 |
| Random Forest | 0.9963504 | 0.9555556 |