Machine Learning: Función CARET
Diego Perez a01275561
2/28/2024
Teoría
El paquete CARET (Clasification And REgression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.
Instalar paquetes y llamar librerías
#install.packages("ggplot2") #Gráficas con mejor diseño
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.3.2#install.packages("lattice") #Crear gráficos
library(lattice)## Warning: package 'lattice' was built under R version 4.3.2#install.packages("caret") #Algoritmos de aprendizaje automático
library(caret)## Warning: package 'caret' was built under R version 4.3.2#install.packages("datasets") #Usar la base de datos IRIS
library(datasets)
#install.packages("mlbench")
library(mlbench)## Warning: package 'mlbench' was built under R version 4.3.2#install.packages("dplyr")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#install.packages("DataExplorer")
library(DataExplorer)## Warning: package 'DataExplorer' was built under R version 4.3.2Crear base de datos
df <- data.frame(iris)Análisis exploratorio
summary(df)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## str(df)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...#create_report()plot_missing(df)
plot_histogram(df)
plot_correlation(df)
Nota: La variable que queremos predecir debe tener formato de FACTOR
Partir datos 80-20
set.seed(123)
renglones_entrenamento <- createDataPartition(df$Species, p=0.8, list=FALSE)
entrenamiento <- iris[renglones_entrenamento, ]
prueba <- iris[-renglones_entrenamento, ]Distintos tipos de Métodos para Modelar
Los métodos más utilizado para modelaraprendizaje automático son:
- SSM: Support Vector Machine o Máquina de
Vectores de Sporte Hay varios subtipos: Linear (svmLinear), Radial
(svmRadial), Polinómico (svmPoly), etc.
- Árbol de Decisión: rpart
- Redes Neuronales: nnet
- Random Forest o Bosques Aleatorios: rf
1.Modelo con el Método svmLinear
modelo1 <- train(Species ~ ., data=entrenamiento, method= "svmLinear", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(C=1) #Cuando es svmLinear
)
resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)
#Matriz de Confusión
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre1## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 0
## virginica 0 1 40
##
## Overall Statistics
##
## Accuracy : 0.9917
## 95% CI : (0.9544, 0.9998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9875
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 1.0000
## Specificity 1.0000 1.0000 0.9875
## Pos Pred Value 1.0000 1.0000 0.9756
## Neg Pred Value 1.0000 0.9877 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3333
## Detection Prevalence 0.3333 0.3250 0.3417
## Balanced Accuracy 1.0000 0.9875 0.9938mcrp1 <- confusionMatrix(resultado_prueba1, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 1
## virginica 0 0 9
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.9000
## Specificity 1.0000 0.9500 1.0000
## Pos Pred Value 1.0000 0.9091 1.0000
## Neg Pred Value 1.0000 1.0000 0.9524
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3000
## Detection Prevalence 0.3333 0.3667 0.3000
## Balanced Accuracy 1.0000 0.9750 0.95002.Modelo con el Método svmRadial
modelo2 <- train(Species ~ ., data=entrenamiento, method= "svmRadial", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(sigma=1, C=1) #Cambiar
)
resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)
#Matriz de Confusión
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 0
## virginica 0 1 40
##
## Overall Statistics
##
## Accuracy : 0.9917
## 95% CI : (0.9544, 0.9998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9875
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 1.0000
## Specificity 1.0000 1.0000 0.9875
## Pos Pred Value 1.0000 1.0000 0.9756
## Neg Pred Value 1.0000 0.9877 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3333
## Detection Prevalence 0.3333 0.3250 0.3417
## Balanced Accuracy 1.0000 0.9875 0.9938mcrp2 <- confusionMatrix(resultado_prueba2, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.90003.Modelo con el Método svmPoly
modelo3 <- train(Species ~ ., data=entrenamiento, method= "svmPoly", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(degree=1, scale=1, C=1) #Cambiar
)
resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)
#Matriz de Confusión
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre3## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 0
## virginica 0 1 40
##
## Overall Statistics
##
## Accuracy : 0.9917
## 95% CI : (0.9544, 0.9998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9875
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 1.0000
## Specificity 1.0000 1.0000 0.9875
## Pos Pred Value 1.0000 1.0000 0.9756
## Neg Pred Value 1.0000 0.9877 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3333
## Detection Prevalence 0.3333 0.3250 0.3417
## Balanced Accuracy 1.0000 0.9875 0.9938mcrp3 <- confusionMatrix(resultado_prueba3, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 1
## virginica 0 0 9
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.9000
## Specificity 1.0000 0.9500 1.0000
## Pos Pred Value 1.0000 0.9091 1.0000
## Neg Pred Value 1.0000 1.0000 0.9524
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3000
## Detection Prevalence 0.3333 0.3667 0.3000
## Balanced Accuracy 1.0000 0.9750 0.95004.Modelo con el Método rpart
modelo4 <- train(Species ~ ., data=entrenamiento, method= "rpart", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneLength=10 #Cambiar
)
resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)
#Matriz de Confusión
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre4## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 3
## virginica 0 1 37
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.9169, 0.9908)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 0.9250
## Specificity 1.0000 0.9625 0.9875
## Pos Pred Value 1.0000 0.9286 0.9737
## Neg Pred Value 1.0000 0.9872 0.9634
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3083
## Detection Prevalence 0.3333 0.3500 0.3167
## Balanced Accuracy 1.0000 0.9688 0.9563mcrp4 <- confusionMatrix(resultado_prueba4, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp4## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.90005.Modelo con el Método nnet
modelo5 <- train(Species ~ ., data=entrenamiento, method= "nnet", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10) #Cambiar
)## # weights: 11
## initial value 130.530132
## iter 10 value 50.031494
## iter 20 value 48.622939
## iter 30 value 46.051782
## iter 40 value 45.435982
## iter 50 value 45.023331
## iter 60 value 41.544443
## iter 70 value 18.376424
## iter 80 value 4.629967
## iter 90 value 3.675228
## iter 100 value 3.275824
## final value 3.275824
## stopped after 100 iterations
## # weights: 27
## initial value 132.517409
## iter 10 value 22.263231
## iter 20 value 2.574680
## iter 30 value 0.008513
## final value 0.000051
## converged
## # weights: 43
## initial value 136.160730
## iter 10 value 3.642258
## iter 20 value 0.051614
## iter 30 value 0.013220
## iter 40 value 0.001249
## final value 0.000086
## converged
## # weights: 11
## initial value 124.472165
## iter 10 value 57.985437
## iter 20 value 43.232595
## final value 43.170440
## converged
## # weights: 27
## initial value 118.611044
## iter 10 value 30.413305
## iter 20 value 21.077103
## iter 30 value 20.192922
## iter 40 value 20.153936
## final value 20.153924
## converged
## # weights: 43
## initial value 131.301286
## iter 10 value 26.646865
## iter 20 value 17.682102
## iter 30 value 17.633586
## iter 40 value 17.623573
## iter 50 value 17.364993
## iter 60 value 17.295129
## iter 70 value 17.290694
## final value 17.290666
## converged
## # weights: 11
## initial value 115.622911
## iter 10 value 33.350769
## iter 20 value 4.676969
## iter 30 value 3.131052
## iter 40 value 2.922591
## iter 50 value 2.825976
## iter 60 value 2.769974
## iter 70 value 2.741299
## iter 80 value 2.741136
## iter 90 value 2.739093
## final value 2.739035
## converged
## # weights: 27
## initial value 139.822975
## iter 10 value 37.447376
## iter 20 value 1.445699
## iter 30 value 0.316497
## iter 40 value 0.287713
## iter 50 value 0.260591
## iter 60 value 0.236249
## iter 70 value 0.224761
## iter 80 value 0.215415
## iter 90 value 0.194816
## iter 100 value 0.189471
## final value 0.189471
## stopped after 100 iterations
## # weights: 43
## initial value 123.298044
## iter 10 value 4.177632
## iter 20 value 0.257205
## iter 30 value 0.224601
## iter 40 value 0.200241
## iter 50 value 0.193031
## iter 60 value 0.182082
## iter 70 value 0.164800
## iter 80 value 0.149792
## iter 90 value 0.144373
## iter 100 value 0.142810
## final value 0.142810
## stopped after 100 iterations
## # weights: 11
## initial value 123.243079
## iter 10 value 49.923348
## iter 20 value 49.909994
## iter 30 value 49.907880
## final value 49.906719
## converged
## # weights: 27
## initial value 117.894759
## iter 10 value 9.481781
## iter 20 value 0.026637
## iter 30 value 0.001156
## final value 0.000052
## converged
## # weights: 43
## initial value 131.870976
## iter 10 value 17.010430
## iter 20 value 0.698814
## iter 30 value 0.001401
## final value 0.000067
## converged
## # weights: 11
## initial value 141.804121
## iter 10 value 63.315182
## iter 20 value 44.532148
## iter 30 value 42.998412
## final value 42.994034
## converged
## # weights: 27
## initial value 129.180442
## iter 10 value 44.217928
## iter 20 value 19.729677
## iter 30 value 18.527378
## iter 40 value 18.411074
## iter 50 value 18.393711
## iter 60 value 18.393129
## final value 18.393125
## converged
## # weights: 43
## initial value 143.533117
## iter 10 value 21.063126
## iter 20 value 17.843661
## iter 30 value 17.106737
## iter 40 value 16.985544
## iter 50 value 16.981278
## iter 60 value 16.980626
## final value 16.980585
## converged
## # weights: 11
## initial value 123.091645
## iter 10 value 49.148390
## iter 20 value 35.943210
## iter 30 value 10.736283
## iter 40 value 2.021433
## iter 50 value 1.687392
## iter 60 value 1.640809
## iter 70 value 1.636953
## iter 80 value 1.613389
## iter 90 value 1.611928
## iter 100 value 1.611137
## final value 1.611137
## stopped after 100 iterations
## # weights: 27
## initial value 113.416728
## iter 10 value 6.236444
## iter 20 value 0.187917
## iter 30 value 0.166748
## iter 40 value 0.155642
## iter 50 value 0.144249
## iter 60 value 0.141208
## iter 70 value 0.138463
## iter 80 value 0.136774
## iter 90 value 0.134567
## iter 100 value 0.132971
## final value 0.132971
## stopped after 100 iterations
## # weights: 43
## initial value 124.153763
## iter 10 value 6.673362
## iter 20 value 0.166533
## iter 30 value 0.154159
## iter 40 value 0.149227
## iter 50 value 0.136832
## iter 60 value 0.125718
## iter 70 value 0.121478
## iter 80 value 0.115540
## iter 90 value 0.113390
## iter 100 value 0.110992
## final value 0.110992
## stopped after 100 iterations
## # weights: 11
## initial value 128.347385
## iter 10 value 55.157651
## iter 20 value 47.800562
## iter 30 value 47.763719
## iter 40 value 47.763542
## iter 50 value 47.762534
## final value 47.762465
## converged
## # weights: 27
## initial value 115.590774
## iter 10 value 5.054265
## iter 20 value 1.048058
## iter 30 value 0.000979
## final value 0.000072
## converged
## # weights: 43
## initial value 123.951869
## iter 10 value 13.178443
## iter 20 value 0.965118
## iter 30 value 0.002392
## final value 0.000078
## converged
## # weights: 11
## initial value 123.195822
## iter 10 value 53.656490
## iter 20 value 43.803131
## iter 30 value 43.734766
## final value 43.734347
## converged
## # weights: 27
## initial value 123.651803
## iter 10 value 29.880588
## iter 20 value 19.921143
## iter 30 value 19.707388
## iter 40 value 19.705704
## final value 19.705624
## converged
## # weights: 43
## initial value 148.336280
## iter 10 value 27.474145
## iter 20 value 18.301737
## iter 30 value 18.138015
## iter 40 value 18.086240
## iter 50 value 18.084155
## iter 60 value 18.083934
## final value 18.083909
## converged
## # weights: 11
## initial value 122.563728
## iter 10 value 32.122176
## iter 20 value 10.269949
## iter 30 value 4.526292
## iter 40 value 3.900620
## iter 50 value 3.805816
## iter 60 value 3.743349
## iter 70 value 3.733207
## iter 80 value 3.721238
## iter 90 value 3.713938
## iter 100 value 3.705684
## final value 3.705684
## stopped after 100 iterations
## # weights: 27
## initial value 130.631378
## iter 10 value 4.944652
## iter 20 value 0.903581
## iter 30 value 0.602599
## iter 40 value 0.449328
## iter 50 value 0.416076
## iter 60 value 0.405323
## iter 70 value 0.397568
## iter 80 value 0.392801
## iter 90 value 0.386606
## iter 100 value 0.380965
## final value 0.380965
## stopped after 100 iterations
## # weights: 43
## initial value 152.884265
## iter 10 value 11.737646
## iter 20 value 1.402922
## iter 30 value 0.553654
## iter 40 value 0.456488
## iter 50 value 0.433353
## iter 60 value 0.391721
## iter 70 value 0.350673
## iter 80 value 0.322382
## iter 90 value 0.309362
## iter 100 value 0.302224
## final value 0.302224
## stopped after 100 iterations
## # weights: 11
## initial value 133.677265
## iter 10 value 49.425529
## iter 20 value 45.125104
## iter 30 value 24.714814
## iter 40 value 6.951374
## iter 50 value 3.962940
## iter 60 value 3.585057
## iter 70 value 2.556588
## iter 80 value 2.219301
## iter 90 value 2.033936
## iter 100 value 2.011517
## final value 2.011517
## stopped after 100 iterations
## # weights: 27
## initial value 120.219437
## iter 10 value 20.105178
## iter 20 value 0.691846
## iter 30 value 0.000424
## final value 0.000094
## converged
## # weights: 43
## initial value 130.013247
## iter 10 value 6.990719
## iter 20 value 0.117056
## final value 0.000078
## converged
## # weights: 11
## initial value 122.587894
## iter 10 value 55.646479
## iter 20 value 44.073616
## iter 30 value 44.056707
## final value 44.056649
## converged
## # weights: 27
## initial value 122.488484
## iter 10 value 30.042105
## iter 20 value 22.364237
## iter 30 value 21.402694
## iter 40 value 21.391770
## final value 21.391728
## converged
## # weights: 43
## initial value 151.848122
## iter 10 value 27.150882
## iter 20 value 20.889994
## iter 30 value 19.061592
## iter 40 value 18.857339
## iter 50 value 18.636402
## iter 60 value 18.597842
## iter 70 value 18.581420
## final value 18.581304
## converged
## # weights: 11
## initial value 125.447189
## iter 10 value 42.432302
## iter 20 value 14.708081
## iter 30 value 5.928158
## iter 40 value 4.717183
## iter 50 value 4.261072
## iter 60 value 3.990872
## iter 70 value 3.894028
## iter 80 value 3.877352
## iter 90 value 3.868846
## iter 100 value 3.865924
## final value 3.865924
## stopped after 100 iterations
## # weights: 27
## initial value 141.522247
## iter 10 value 19.693351
## iter 20 value 2.060082
## iter 30 value 0.713635
## iter 40 value 0.684010
## iter 50 value 0.651024
## iter 60 value 0.599068
## iter 70 value 0.534726
## iter 80 value 0.525302
## iter 90 value 0.477461
## iter 100 value 0.468104
## final value 0.468104
## stopped after 100 iterations
## # weights: 43
## initial value 117.492171
## iter 10 value 5.474776
## iter 20 value 0.633193
## iter 30 value 0.523049
## iter 40 value 0.506835
## iter 50 value 0.486677
## iter 60 value 0.470314
## iter 70 value 0.423468
## iter 80 value 0.413761
## iter 90 value 0.406423
## iter 100 value 0.383741
## final value 0.383741
## stopped after 100 iterations
## # weights: 11
## initial value 128.494859
## iter 10 value 67.868204
## iter 20 value 40.370984
## iter 30 value 8.030160
## iter 40 value 3.602779
## iter 50 value 3.354456
## iter 60 value 3.245703
## iter 70 value 3.148381
## iter 80 value 3.017232
## iter 90 value 2.916738
## iter 100 value 2.698927
## final value 2.698927
## stopped after 100 iterations
## # weights: 27
## initial value 121.387618
## iter 10 value 17.333188
## iter 20 value 6.562404
## iter 30 value 4.218606
## iter 40 value 0.023796
## iter 50 value 0.013835
## iter 60 value 0.007181
## iter 70 value 0.000265
## final value 0.000094
## converged
## # weights: 43
## initial value 131.764022
## iter 10 value 6.923964
## iter 20 value 0.585918
## iter 30 value 0.001510
## final value 0.000094
## converged
## # weights: 11
## initial value 117.924376
## iter 10 value 59.153858
## iter 20 value 45.980503
## iter 30 value 43.965813
## final value 43.965807
## converged
## # weights: 27
## initial value 122.524569
## iter 10 value 28.252379
## iter 20 value 20.308998
## iter 30 value 19.983255
## iter 40 value 19.969846
## final value 19.969845
## converged
## # weights: 43
## initial value 175.722543
## iter 10 value 24.152694
## iter 20 value 19.351652
## iter 30 value 18.570128
## iter 40 value 18.540253
## iter 50 value 18.531786
## iter 60 value 18.531273
## final value 18.531272
## converged
## # weights: 11
## initial value 125.626851
## iter 10 value 50.695359
## iter 20 value 28.615271
## iter 30 value 12.424432
## iter 40 value 5.029030
## iter 50 value 4.166888
## iter 60 value 3.979676
## iter 70 value 3.882211
## iter 80 value 3.873043
## iter 90 value 3.872674
## iter 100 value 3.871442
## final value 3.871442
## stopped after 100 iterations
## # weights: 27
## initial value 123.025871
## iter 10 value 27.020381
## iter 20 value 2.694706
## iter 30 value 1.092737
## iter 40 value 0.872715
## iter 50 value 0.758401
## iter 60 value 0.630276
## iter 70 value 0.571755
## iter 80 value 0.515264
## iter 90 value 0.475373
## iter 100 value 0.452080
## final value 0.452080
## stopped after 100 iterations
## # weights: 43
## initial value 134.385829
## iter 10 value 5.396493
## iter 20 value 1.952502
## iter 30 value 0.810078
## iter 40 value 0.740163
## iter 50 value 0.700944
## iter 60 value 0.648312
## iter 70 value 0.581811
## iter 80 value 0.540064
## iter 90 value 0.513923
## iter 100 value 0.483298
## final value 0.483298
## stopped after 100 iterations
## # weights: 11
## initial value 124.033991
## iter 10 value 53.598901
## iter 20 value 53.094417
## iter 30 value 51.710795
## iter 40 value 44.732729
## iter 50 value 17.281237
## iter 60 value 6.529030
## iter 70 value 3.473730
## iter 80 value 3.279187
## iter 90 value 3.156556
## iter 100 value 2.981555
## final value 2.981555
## stopped after 100 iterations
## # weights: 27
## initial value 126.207925
## iter 10 value 6.867316
## iter 20 value 0.342203
## iter 30 value 0.000889
## final value 0.000071
## converged
## # weights: 43
## initial value 146.268437
## iter 10 value 7.061711
## iter 20 value 1.073309
## iter 30 value 0.000467
## final value 0.000066
## converged
## # weights: 11
## initial value 120.866935
## iter 10 value 85.950877
## iter 20 value 60.671406
## iter 30 value 50.749580
## iter 40 value 43.846120
## final value 43.846095
## converged
## # weights: 27
## initial value 126.514320
## iter 10 value 46.451931
## iter 20 value 22.288378
## iter 30 value 21.611509
## iter 40 value 21.142364
## iter 50 value 20.374688
## iter 60 value 19.975509
## iter 70 value 19.860029
## final value 19.859991
## converged
## # weights: 43
## initial value 113.521981
## iter 10 value 27.307122
## iter 20 value 19.069629
## iter 30 value 18.496103
## iter 40 value 18.414947
## iter 50 value 18.412091
## iter 60 value 18.411932
## final value 18.411927
## converged
## # weights: 11
## initial value 119.931364
## iter 10 value 33.212563
## iter 20 value 6.825543
## iter 30 value 4.153607
## iter 40 value 3.996719
## iter 50 value 3.936301
## iter 60 value 3.900913
## iter 70 value 3.868653
## iter 80 value 3.868193
## iter 90 value 3.864798
## iter 100 value 3.860658
## final value 3.860658
## stopped after 100 iterations
## # weights: 27
## initial value 125.980953
## iter 10 value 3.828376
## iter 20 value 1.757039
## iter 30 value 1.084888
## iter 40 value 0.779504
## iter 50 value 0.534913
## iter 60 value 0.521705
## iter 70 value 0.515783
## iter 80 value 0.504124
## iter 90 value 0.485201
## iter 100 value 0.483827
## final value 0.483827
## stopped after 100 iterations
## # weights: 43
## initial value 143.013185
## iter 10 value 7.195354
## iter 20 value 1.984745
## iter 30 value 0.713672
## iter 40 value 0.552459
## iter 50 value 0.437450
## iter 60 value 0.403627
## iter 70 value 0.363382
## iter 80 value 0.356303
## iter 90 value 0.346628
## iter 100 value 0.337926
## final value 0.337926
## stopped after 100 iterations
## # weights: 11
## initial value 119.603843
## iter 10 value 66.519353
## iter 20 value 48.085237
## iter 30 value 10.691129
## iter 40 value 4.343493
## iter 50 value 3.486657
## iter 60 value 2.937962
## iter 70 value 2.185862
## iter 80 value 1.910157
## iter 90 value 1.802781
## iter 100 value 1.791736
## final value 1.791736
## stopped after 100 iterations
## # weights: 27
## initial value 120.493313
## iter 10 value 14.568437
## iter 20 value 1.413139
## iter 30 value 0.002421
## final value 0.000049
## converged
## # weights: 43
## initial value 131.990396
## iter 10 value 3.607345
## iter 20 value 0.869522
## iter 30 value 0.000776
## final value 0.000079
## converged
## # weights: 11
## initial value 127.213395
## iter 10 value 58.997762
## iter 20 value 44.424763
## final value 43.139243
## converged
## # weights: 27
## initial value 117.195869
## iter 10 value 28.619024
## iter 20 value 19.206476
## iter 30 value 18.621574
## iter 40 value 18.619068
## iter 40 value 18.619068
## iter 40 value 18.619068
## final value 18.619068
## converged
## # weights: 43
## initial value 165.598734
## iter 10 value 24.205649
## iter 20 value 17.629535
## iter 30 value 17.222776
## iter 40 value 17.168752
## iter 50 value 17.168464
## iter 60 value 17.168428
## iter 60 value 17.168428
## iter 60 value 17.168428
## final value 17.168428
## converged
## # weights: 11
## initial value 115.941037
## iter 10 value 48.705139
## iter 20 value 47.783092
## iter 30 value 43.562064
## iter 40 value 11.101593
## iter 50 value 4.031437
## iter 60 value 3.116711
## iter 70 value 3.019260
## iter 80 value 2.993105
## iter 90 value 2.981303
## iter 100 value 2.969047
## final value 2.969047
## stopped after 100 iterations
## # weights: 27
## initial value 132.813339
## iter 10 value 3.715700
## iter 20 value 1.056815
## iter 30 value 0.558748
## iter 40 value 0.530262
## iter 50 value 0.467614
## iter 60 value 0.445847
## iter 70 value 0.424130
## iter 80 value 0.373259
## iter 90 value 0.354379
## iter 100 value 0.342801
## final value 0.342801
## stopped after 100 iterations
## # weights: 43
## initial value 126.886256
## iter 10 value 3.942342
## iter 20 value 1.736816
## iter 30 value 0.630651
## iter 40 value 0.552680
## iter 50 value 0.489807
## iter 60 value 0.396264
## iter 70 value 0.356221
## iter 80 value 0.340605
## iter 90 value 0.328238
## iter 100 value 0.321359
## final value 0.321359
## stopped after 100 iterations
## # weights: 11
## initial value 128.489378
## iter 10 value 49.909576
## iter 20 value 49.876540
## iter 30 value 47.945970
## iter 40 value 39.847983
## iter 50 value 8.016537
## iter 60 value 4.619364
## iter 70 value 2.386452
## iter 80 value 1.338165
## iter 90 value 1.178344
## iter 100 value 1.100248
## final value 1.100248
## stopped after 100 iterations
## # weights: 27
## initial value 141.912242
## iter 10 value 7.102731
## iter 20 value 0.339738
## final value 0.000079
## converged
## # weights: 43
## initial value 128.771330
## iter 10 value 21.354630
## iter 20 value 2.784172
## iter 30 value 0.013786
## iter 40 value 0.000332
## final value 0.000076
## converged
## # weights: 11
## initial value 120.181179
## iter 10 value 46.347790
## iter 20 value 43.064428
## iter 30 value 43.054040
## final value 43.054021
## converged
## # weights: 27
## initial value 126.647230
## iter 10 value 25.682812
## iter 20 value 20.660342
## iter 30 value 19.500529
## iter 40 value 19.121600
## iter 50 value 19.088454
## iter 60 value 19.083697
## final value 19.083689
## converged
## # weights: 43
## initial value 132.234904
## iter 10 value 29.615687
## iter 20 value 19.279132
## iter 30 value 17.877712
## iter 40 value 17.806996
## iter 50 value 17.793960
## iter 60 value 17.793819
## final value 17.793686
## converged
## # weights: 11
## initial value 121.579687
## iter 10 value 49.472914
## iter 20 value 48.410085
## iter 30 value 45.340464
## iter 40 value 37.104905
## iter 50 value 8.129202
## iter 60 value 4.703745
## iter 70 value 4.278312
## iter 80 value 3.668066
## iter 90 value 3.605900
## iter 100 value 3.568123
## final value 3.568123
## stopped after 100 iterations
## # weights: 27
## initial value 135.360878
## iter 10 value 10.436945
## iter 20 value 2.222820
## iter 30 value 0.763058
## iter 40 value 0.725440
## iter 50 value 0.677966
## iter 60 value 0.570628
## iter 70 value 0.518380
## iter 80 value 0.502364
## iter 90 value 0.462332
## iter 100 value 0.455880
## final value 0.455880
## stopped after 100 iterations
## # weights: 43
## initial value 125.924213
## iter 10 value 3.865138
## iter 20 value 1.025246
## iter 30 value 0.422681
## iter 40 value 0.379135
## iter 50 value 0.353145
## iter 60 value 0.335865
## iter 70 value 0.319622
## iter 80 value 0.303895
## iter 90 value 0.289299
## iter 100 value 0.271561
## final value 0.271561
## stopped after 100 iterations
## # weights: 11
## initial value 114.925820
## iter 10 value 45.333263
## iter 20 value 21.250608
## iter 30 value 6.082611
## iter 40 value 4.448976
## iter 50 value 3.266614
## iter 60 value 1.880390
## iter 70 value 1.733764
## iter 80 value 1.089267
## iter 90 value 1.045776
## iter 100 value 0.950636
## final value 0.950636
## stopped after 100 iterations
## # weights: 27
## initial value 116.607224
## iter 10 value 6.159810
## iter 20 value 1.197702
## iter 30 value 0.000196
## final value 0.000057
## converged
## # weights: 43
## initial value 123.125697
## iter 10 value 4.793414
## iter 20 value 0.073094
## iter 30 value 0.000393
## final value 0.000088
## converged
## # weights: 11
## initial value 120.471214
## iter 10 value 45.420303
## iter 20 value 43.694661
## iter 30 value 43.690235
## final value 43.690202
## converged
## # weights: 27
## initial value 168.714249
## iter 10 value 28.073376
## iter 20 value 21.126580
## iter 30 value 20.968508
## iter 40 value 20.968134
## final value 20.968117
## converged
## # weights: 43
## initial value 134.057733
## iter 10 value 44.240823
## iter 20 value 19.621880
## iter 30 value 18.596469
## iter 40 value 18.220014
## iter 50 value 18.200869
## iter 60 value 18.194706
## final value 18.194547
## converged
## # weights: 11
## initial value 137.081572
## iter 10 value 53.546736
## iter 20 value 49.263649
## iter 30 value 49.116099
## iter 40 value 49.041348
## iter 50 value 48.683090
## iter 60 value 48.634845
## iter 70 value 48.489442
## iter 80 value 48.480790
## iter 90 value 48.451846
## iter 100 value 48.179024
## final value 48.179024
## stopped after 100 iterations
## # weights: 27
## initial value 143.490043
## iter 10 value 4.357251
## iter 20 value 1.321252
## iter 30 value 0.645280
## iter 40 value 0.616636
## iter 50 value 0.565996
## iter 60 value 0.521660
## iter 70 value 0.508617
## iter 80 value 0.487870
## iter 90 value 0.483152
## iter 100 value 0.479423
## final value 0.479423
## stopped after 100 iterations
## # weights: 43
## initial value 178.832632
## iter 10 value 8.121158
## iter 20 value 1.422046
## iter 30 value 0.568662
## iter 40 value 0.518952
## iter 50 value 0.434974
## iter 60 value 0.392568
## iter 70 value 0.345835
## iter 80 value 0.285289
## iter 90 value 0.268178
## iter 100 value 0.253675
## final value 0.253675
## stopped after 100 iterations
## # weights: 11
## initial value 123.307045
## iter 10 value 43.672929
## iter 20 value 8.049676
## iter 30 value 3.773651
## iter 40 value 3.173208
## iter 50 value 3.060201
## iter 60 value 2.971167
## iter 70 value 2.563371
## iter 80 value 2.471224
## iter 90 value 2.341221
## iter 100 value 2.320048
## final value 2.320048
## stopped after 100 iterations
## # weights: 27
## initial value 129.270569
## iter 10 value 10.575847
## iter 20 value 2.930770
## iter 30 value 1.689612
## iter 40 value 0.097359
## iter 50 value 0.000123
## iter 50 value 0.000057
## iter 50 value 0.000057
## final value 0.000057
## converged
## # weights: 43
## initial value 119.634242
## iter 10 value 6.310691
## iter 20 value 1.591412
## iter 30 value 0.028391
## iter 40 value 0.000902
## final value 0.000069
## converged
## # weights: 11
## initial value 120.069235
## iter 10 value 60.195069
## iter 20 value 51.394914
## iter 30 value 43.991436
## final value 43.991141
## converged
## # weights: 27
## initial value 152.809198
## iter 10 value 25.471737
## iter 20 value 21.511163
## iter 30 value 21.387357
## iter 40 value 21.386800
## final value 21.386800
## converged
## # weights: 43
## initial value 137.024287
## iter 10 value 22.447246
## iter 20 value 19.002967
## iter 30 value 18.519064
## iter 40 value 18.404215
## iter 50 value 18.397540
## iter 60 value 18.396716
## final value 18.396607
## converged
## # weights: 11
## initial value 121.726735
## iter 10 value 50.373336
## iter 20 value 50.105529
## iter 30 value 49.998791
## iter 40 value 49.958270
## iter 50 value 49.774790
## iter 60 value 48.541266
## iter 70 value 18.978222
## iter 80 value 6.742676
## iter 90 value 4.056469
## iter 100 value 3.922763
## final value 3.922763
## stopped after 100 iterations
## # weights: 27
## initial value 146.633351
## iter 10 value 6.579898
## iter 20 value 0.624311
## iter 30 value 0.562510
## iter 40 value 0.514462
## iter 50 value 0.457198
## iter 60 value 0.403961
## iter 70 value 0.382785
## iter 80 value 0.371306
## iter 90 value 0.358751
## iter 100 value 0.317469
## final value 0.317469
## stopped after 100 iterations
## # weights: 43
## initial value 127.981900
## iter 10 value 7.369546
## iter 20 value 0.839917
## iter 30 value 0.675447
## iter 40 value 0.617273
## iter 50 value 0.540482
## iter 60 value 0.477520
## iter 70 value 0.443309
## iter 80 value 0.359346
## iter 90 value 0.308424
## iter 100 value 0.292198
## final value 0.292198
## stopped after 100 iterations
## # weights: 11
## initial value 133.510869
## iter 10 value 66.279276
## iter 20 value 49.065891
## iter 30 value 46.607987
## final value 46.598156
## convergedresultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
#Matriz de Confusión
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre5## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 36 0
## virginica 0 4 40
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.9169, 0.9908)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9000 1.0000
## Specificity 1.0000 1.0000 0.9500
## Pos Pred Value 1.0000 1.0000 0.9091
## Neg Pred Value 1.0000 0.9524 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3000 0.3333
## Detection Prevalence 0.3333 0.3000 0.3667
## Balanced Accuracy 1.0000 0.9500 0.9750mcrp5 <- confusionMatrix(resultado_prueba5, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 9 0
## virginica 0 1 10
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9000 1.0000
## Specificity 1.0000 1.0000 0.9500
## Pos Pred Value 1.0000 1.0000 0.9091
## Neg Pred Value 1.0000 0.9524 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3000 0.3333
## Detection Prevalence 0.3333 0.3000 0.3667
## Balanced Accuracy 1.0000 0.9500 0.97506.Modelo con el Método rf
modelo6 <- train(Species ~ ., data=entrenamiento, method= "rf", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= expand.grid(mtry=c(2,4,6)) #Cambiar
)## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid rangeresultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)
#Matriz de Confusión
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre6## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 40 0
## virginica 0 0 40
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9697, 1)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 1.0000
## Specificity 1.0000 1.0000 1.0000
## Pos Pred Value 1.0000 1.0000 1.0000
## Neg Pred Value 1.0000 1.0000 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3333
## Detection Prevalence 0.3333 0.3333 0.3333
## Balanced Accuracy 1.0000 1.0000 1.0000mcrp6 <- confusionMatrix(resultado_prueba6, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.9000Resumen de resultados
resultados <- data.frame(
"1. svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
"2. svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"3. svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"4. rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"5. nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"6. rf" = c (mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisión de entrenamiento", "Precisión de prueba")
resultados## X1..svmLinear X2..svmRadial X3..svmPoly X4..rpart
## Precisión de entrenamiento 0.9916667 0.9916667 0.9916667 0.9666667
## Precisión de prueba 0.9666667 0.9333333 0.9666667 0.9333333
## X5..nnet X6..rf
## Precisión de entrenamiento 0.9666667 1.0000000
## Precisión de prueba 0.9666667 0.9333333Conclusiones
El modelo con el método de bosques aleatorios presenta sobreajuste, ya que tiene una alta precisión en entrenamiento pero baja en prueba.
Acorde al resumen de resultados, el mejor modelo es el de Máquina de Vectores de Soporte Lineal.
EJERCICIO “Cáncer de mama”
Crear base de datos
data("BreastCancer")
df <- data.frame(BreastCancer)
#Quitar columna ID'S
df <- df %>% select(-Id)
#QUITAR NA'S
df <- na.omit(df)Análisis exploratorio
summary(df)## Cl.thickness Cell.size Cell.shape Marg.adhesion Epith.c.size
## 1 :139 1 :373 1 :346 1 :393 2 :376
## 5 :128 10 : 67 2 : 58 2 : 58 3 : 71
## 3 :104 3 : 52 10 : 58 3 : 58 4 : 48
## 4 : 79 2 : 45 3 : 53 10 : 55 1 : 44
## 10 : 69 4 : 38 4 : 43 4 : 33 6 : 40
## 2 : 50 5 : 30 5 : 32 8 : 25 5 : 39
## (Other):114 (Other): 78 (Other): 93 (Other): 61 (Other): 65
## Bare.nuclei Bl.cromatin Normal.nucleoli Mitoses Class
## 1 :402 3 :161 1 :432 1 :563 benign :444
## 10 :132 2 :160 10 : 60 2 : 35 malignant:239
## 2 : 30 1 :150 3 : 42 3 : 33
## 5 : 30 7 : 71 2 : 36 10 : 14
## 3 : 28 4 : 39 8 : 23 4 : 12
## 8 : 21 5 : 34 6 : 22 7 : 9
## (Other): 40 (Other): 68 (Other): 68 (Other): 17str(df)## 'data.frame': 683 obs. of 10 variables:
## $ 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 ...
## - attr(*, "na.action")= 'omit' Named int [1:16] 24 41 140 146 159 165 236 250 276 293 ...
## ..- attr(*, "names")= chr [1:16] "24" "41" "140" "146" ...#create_report()plot_missing(df)
plot_correlation(df)
Cambiar las variables para que sean númericas
df$Cl.thickness <- as.numeric(as.character(df$Cl.thickness))
df$Cell.size <- as.numeric(as.character(df$Cell.size))
df$Cell.shape <- as.numeric(as.character(df$Cell.shape))
df$Marg.adhesion <- as.numeric(as.character(df$Marg.adhesion))
df$Epith.c.size <- as.numeric(as.character(df$Epith.c.size))
df$Bare.nuclei <- as.numeric(as.character(df$Bare.nuclei))
df$Bl.cromatin <- as.numeric(as.character(df$Bl.cromatin))
df$Normal.nucleoli <- as.numeric(as.character(df$Normal.nucleoli))
df$Mitoses <- as.numeric(as.character(df$Mitoses))Nota: La variable que queremos predecir debe tener formato de FACTOR
Partir datos 80-20
set.seed(123)
renglones_entrenamentoBC <- createDataPartition(df$Class, p=0.8, list=FALSE)
entrenamientoBC <- df[renglones_entrenamentoBC, ]
pruebaBC <- df[-renglones_entrenamentoBC, ]Distintos tipos de Métodos para Modelar
Los métodos más utilizado para modelaraprendizaje automático son:
- SSM: Support Vector Machine o Máquina de
Vectores de Sporte Hay varios subtipos: Linear (svmLinear), Radial
(svmRadial), Polinómico (svmPoly), etc.
- Árbol de Decisión: rpart
- Redes Neuronales: nnet
- Random Forest o Bosques Aleatorios: rf
1.Modelo con el Método svmLinear
modeloBC1 <- train(Class ~ ., data=entrenamientoBC, method= "svmLinear", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(C=1) #Cuando es svmLinear
)
resultado_entrenamientoBC1 <- predict(modeloBC1, entrenamientoBC)
resultado_pruebaBC1 <- predict(modeloBC1, pruebaBC)
#Matriz de Confusión
mcre1 <- confusionMatrix(resultado_entrenamientoBC1, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre1## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 347 7
## malignant 9 185
##
## Accuracy : 0.9708
## 95% CI : (0.953, 0.9832)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.936
##
## Mcnemar's Test P-Value : 0.8026
##
## Sensitivity : 0.9747
## Specificity : 0.9635
## Pos Pred Value : 0.9802
## Neg Pred Value : 0.9536
## Prevalence : 0.6496
## Detection Rate : 0.6332
## Detection Prevalence : 0.6460
## Balanced Accuracy : 0.9691
##
## 'Positive' Class : benign
## mcrp1 <- confusionMatrix(resultado_pruebaBC1, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 87 2
## malignant 1 45
##
## Accuracy : 0.9778
## 95% CI : (0.9364, 0.9954)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9508
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9886
## Specificity : 0.9574
## Pos Pred Value : 0.9775
## Neg Pred Value : 0.9783
## Prevalence : 0.6519
## Detection Rate : 0.6444
## Detection Prevalence : 0.6593
## Balanced Accuracy : 0.9730
##
## 'Positive' Class : benign
## 2.Modelo con el Método svmRadial
modeloBC2 <- train(Class ~ ., data=entrenamientoBC, method= "svmRadial", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(sigma=1, C=1) #Cambiar
)
resultado_entrenamientoBC2 <- predict(modeloBC2, entrenamientoBC)
resultado_pruebaBC2 <- predict(modeloBC2, pruebaBC)
#Matriz de Confusión
mcre2 <- confusionMatrix(resultado_entrenamientoBC2, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 354 0
## malignant 2 192
##
## Accuracy : 0.9964
## 95% CI : (0.9869, 0.9996)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.992
##
## Mcnemar's Test P-Value : 0.4795
##
## Sensitivity : 0.9944
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.9897
## Prevalence : 0.6496
## Detection Rate : 0.6460
## Detection Prevalence : 0.6460
## Balanced Accuracy : 0.9972
##
## 'Positive' Class : benign
## mcrp2 <- confusionMatrix(resultado_pruebaBC2, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 82 0
## malignant 6 47
##
## Accuracy : 0.9556
## 95% CI : (0.9058, 0.9835)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : < 2e-16
##
## Kappa : 0.9049
##
## Mcnemar's Test P-Value : 0.04123
##
## Sensitivity : 0.9318
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.8868
## Prevalence : 0.6519
## Detection Rate : 0.6074
## Detection Prevalence : 0.6074
## Balanced Accuracy : 0.9659
##
## 'Positive' Class : benign
## 3.Modelo con el Método svmPoly
modeloBC3 <- train(Class ~ ., data=entrenamientoBC, method= "svmPoly", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(degree=1, scale=1, C=1) #Cambiar
)
resultado_entrenamientoBC3 <- predict(modeloBC3, entrenamientoBC)
resultado_pruebaBC3 <- predict(modeloBC3, pruebaBC)
#Matriz de Confusión
mcre3 <- confusionMatrix(resultado_entrenamientoBC3, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre3## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 347 7
## malignant 9 185
##
## Accuracy : 0.9708
## 95% CI : (0.953, 0.9832)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.936
##
## Mcnemar's Test P-Value : 0.8026
##
## Sensitivity : 0.9747
## Specificity : 0.9635
## Pos Pred Value : 0.9802
## Neg Pred Value : 0.9536
## Prevalence : 0.6496
## Detection Rate : 0.6332
## Detection Prevalence : 0.6460
## Balanced Accuracy : 0.9691
##
## 'Positive' Class : benign
## mcrp3 <- confusionMatrix(resultado_pruebaBC3, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 87 2
## malignant 1 45
##
## Accuracy : 0.9778
## 95% CI : (0.9364, 0.9954)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9508
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9886
## Specificity : 0.9574
## Pos Pred Value : 0.9775
## Neg Pred Value : 0.9783
## Prevalence : 0.6519
## Detection Rate : 0.6444
## Detection Prevalence : 0.6593
## Balanced Accuracy : 0.9730
##
## 'Positive' Class : benign
## 4.Modelo con el Método rpart
modeloBC4 <- train(Class ~ ., data=entrenamientoBC, method= "rpart", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneLength=10 #Cambiar
)
resultado_entrenamientoBC4 <- predict(modeloBC4, entrenamientoBC)
resultado_pruebaBC4 <- predict(modeloBC4, pruebaBC)
#Matriz de Confusión
mcre4 <- confusionMatrix(resultado_entrenamientoBC4, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre4## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 345 9
## malignant 11 183
##
## Accuracy : 0.9635
## 95% CI : (0.9442, 0.9776)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.92
##
## Mcnemar's Test P-Value : 0.8231
##
## Sensitivity : 0.9691
## Specificity : 0.9531
## Pos Pred Value : 0.9746
## Neg Pred Value : 0.9433
## Prevalence : 0.6496
## Detection Rate : 0.6296
## Detection Prevalence : 0.6460
## Balanced Accuracy : 0.9611
##
## 'Positive' Class : benign
## mcrp4 <- confusionMatrix(resultado_pruebaBC4, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp4## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 87 5
## malignant 1 42
##
## Accuracy : 0.9556
## 95% CI : (0.9058, 0.9835)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9001
##
## Mcnemar's Test P-Value : 0.2207
##
## Sensitivity : 0.9886
## Specificity : 0.8936
## Pos Pred Value : 0.9457
## Neg Pred Value : 0.9767
## Prevalence : 0.6519
## Detection Rate : 0.6444
## Detection Prevalence : 0.6815
## Balanced Accuracy : 0.9411
##
## 'Positive' Class : benign
## 5.Modelo con el Método nnet
modeloBC5 <- train(Class ~ ., data=entrenamientoBC, method= "nnet", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10) #Cambiar
)## # weights: 12
## initial value 376.512397
## iter 10 value 43.109567
## iter 20 value 37.816789
## iter 30 value 37.393307
## iter 40 value 37.202710
## iter 50 value 36.691152
## iter 60 value 36.645519
## iter 70 value 36.203034
## iter 80 value 36.068645
## iter 90 value 36.066816
## iter 100 value 35.990862
## final value 35.990862
## stopped after 100 iterations
## # weights: 34
## initial value 398.785839
## iter 10 value 40.921332
## iter 20 value 32.226888
## iter 30 value 28.947400
## iter 40 value 27.690881
## iter 50 value 27.258233
## iter 60 value 27.084473
## iter 70 value 26.862289
## iter 80 value 26.362282
## iter 90 value 25.443885
## iter 100 value 24.871772
## final value 24.871772
## stopped after 100 iterations
## # weights: 56
## initial value 392.518725
## iter 10 value 32.515498
## iter 20 value 12.874502
## iter 30 value 7.985029
## iter 40 value 6.194120
## iter 50 value 5.774182
## iter 60 value 5.718611
## iter 70 value 5.680335
## iter 80 value 5.663894
## iter 90 value 5.652273
## iter 100 value 5.644661
## final value 5.644661
## stopped after 100 iterations
## # weights: 12
## initial value 350.285052
## iter 10 value 63.176600
## iter 20 value 49.960565
## iter 30 value 49.668121
## final value 49.667643
## converged
## # weights: 34
## initial value 349.498531
## iter 10 value 115.292299
## iter 20 value 47.167744
## iter 30 value 40.701373
## iter 40 value 39.136713
## iter 50 value 37.993613
## iter 60 value 37.722960
## iter 70 value 37.705185
## iter 80 value 37.705094
## final value 37.705093
## converged
## # weights: 56
## initial value 385.790168
## iter 10 value 41.568917
## iter 20 value 37.839293
## iter 30 value 37.366085
## iter 40 value 37.244380
## iter 50 value 37.184247
## iter 60 value 37.148147
## iter 70 value 37.147200
## final value 37.147103
## converged
## # weights: 12
## initial value 411.197079
## iter 10 value 126.101033
## iter 20 value 64.916942
## iter 30 value 62.477071
## iter 40 value 62.434341
## iter 50 value 62.420463
## iter 60 value 60.564690
## iter 70 value 43.188354
## iter 80 value 40.597525
## iter 90 value 37.959741
## iter 100 value 37.386914
## final value 37.386914
## stopped after 100 iterations
## # weights: 34
## initial value 396.809103
## iter 10 value 36.803808
## iter 20 value 28.222616
## iter 30 value 24.692421
## iter 40 value 21.283836
## iter 50 value 20.693931
## iter 60 value 20.509633
## iter 70 value 20.285770
## iter 80 value 20.104851
## iter 90 value 20.012950
## iter 100 value 19.939195
## final value 19.939195
## stopped after 100 iterations
## # weights: 56
## initial value 470.495322
## iter 10 value 37.226297
## iter 20 value 28.536698
## iter 30 value 24.831327
## iter 40 value 18.218404
## iter 50 value 16.208980
## iter 60 value 15.441775
## iter 70 value 15.044430
## iter 80 value 14.851409
## iter 90 value 14.766905
## iter 100 value 14.663793
## final value 14.663793
## stopped after 100 iterations
## # weights: 12
## initial value 355.459360
## iter 10 value 52.710948
## iter 20 value 50.543680
## iter 30 value 42.778082
## iter 40 value 42.575005
## iter 50 value 41.896595
## iter 60 value 39.558006
## iter 70 value 39.540621
## iter 80 value 39.527535
## iter 90 value 39.516676
## iter 100 value 39.510296
## final value 39.510296
## stopped after 100 iterations
## # weights: 34
## initial value 345.094693
## iter 10 value 38.864054
## iter 20 value 33.098589
## iter 30 value 28.931958
## iter 40 value 28.548481
## iter 50 value 28.498623
## iter 60 value 28.425274
## iter 70 value 28.379917
## iter 80 value 28.347552
## iter 90 value 28.340689
## iter 100 value 28.338448
## final value 28.338448
## stopped after 100 iterations
## # weights: 56
## initial value 318.277821
## iter 10 value 39.852981
## iter 20 value 20.644864
## iter 30 value 11.484980
## iter 40 value 10.229586
## iter 50 value 9.343021
## iter 60 value 9.253538
## iter 70 value 9.140344
## iter 80 value 3.152197
## iter 90 value 2.548607
## iter 100 value 2.510563
## final value 2.510563
## stopped after 100 iterations
## # weights: 12
## initial value 383.439080
## iter 10 value 68.936910
## iter 20 value 55.011950
## iter 30 value 53.826306
## iter 40 value 53.551184
## final value 53.550992
## converged
## # weights: 34
## initial value 437.903342
## iter 10 value 53.347573
## iter 20 value 46.839645
## iter 30 value 42.789234
## iter 40 value 42.318303
## iter 50 value 41.863757
## iter 60 value 41.778420
## iter 70 value 41.771466
## iter 70 value 41.771466
## final value 41.771466
## converged
## # weights: 56
## initial value 432.922085
## iter 10 value 58.515744
## iter 20 value 43.453015
## iter 30 value 40.439290
## iter 40 value 40.215683
## iter 50 value 40.108368
## iter 60 value 40.091683
## iter 70 value 40.089514
## final value 40.089511
## converged
## # weights: 12
## initial value 370.554674
## iter 10 value 53.682403
## iter 20 value 46.277541
## iter 30 value 43.347649
## iter 40 value 39.935096
## iter 50 value 39.654913
## iter 60 value 39.643184
## iter 70 value 39.640909
## iter 80 value 39.638620
## iter 90 value 39.638417
## iter 100 value 39.638344
## final value 39.638344
## stopped after 100 iterations
## # weights: 34
## initial value 371.288609
## iter 10 value 41.762077
## iter 20 value 36.441925
## iter 30 value 35.026523
## iter 40 value 34.445840
## iter 50 value 31.489351
## iter 60 value 31.411757
## iter 70 value 31.378090
## iter 80 value 31.223502
## iter 90 value 30.318428
## iter 100 value 27.959773
## final value 27.959773
## stopped after 100 iterations
## # weights: 56
## initial value 324.078656
## iter 10 value 35.203759
## iter 20 value 18.713852
## iter 30 value 11.486088
## iter 40 value 11.093492
## iter 50 value 10.415557
## iter 60 value 10.268160
## iter 70 value 10.037759
## iter 80 value 9.978048
## iter 90 value 9.758822
## iter 100 value 9.561419
## final value 9.561419
## stopped after 100 iterations
## # weights: 12
## initial value 376.872469
## iter 10 value 55.754543
## iter 20 value 48.254280
## iter 30 value 45.399366
## iter 40 value 43.383439
## iter 50 value 39.502387
## iter 60 value 39.486663
## iter 70 value 39.485217
## iter 80 value 39.482703
## iter 90 value 39.482220
## iter 100 value 39.479492
## final value 39.479492
## stopped after 100 iterations
## # weights: 34
## initial value 461.270341
## iter 10 value 36.971158
## iter 20 value 29.190367
## iter 30 value 19.680057
## iter 40 value 18.250577
## iter 50 value 18.155907
## iter 60 value 18.103269
## iter 70 value 18.056877
## iter 80 value 17.989942
## iter 90 value 17.793585
## iter 100 value 17.718128
## final value 17.718128
## stopped after 100 iterations
## # weights: 56
## initial value 291.158632
## iter 10 value 32.488217
## iter 20 value 26.334718
## iter 30 value 23.977332
## iter 40 value 18.600272
## iter 50 value 17.936452
## iter 60 value 17.807247
## iter 70 value 17.734333
## iter 80 value 17.560476
## iter 90 value 17.500397
## iter 100 value 17.390077
## final value 17.390077
## stopped after 100 iterations
## # weights: 12
## initial value 350.758871
## iter 10 value 61.913337
## iter 20 value 52.799784
## iter 30 value 52.782791
## final value 52.782280
## converged
## # weights: 34
## initial value 325.534606
## iter 10 value 53.573710
## iter 20 value 47.224232
## iter 30 value 45.210505
## iter 40 value 42.197093
## iter 50 value 41.624769
## iter 60 value 41.618364
## iter 70 value 41.576205
## iter 80 value 41.564176
## final value 41.564175
## converged
## # weights: 56
## initial value 290.753871
## iter 10 value 82.927340
## iter 20 value 50.542865
## iter 30 value 44.382185
## iter 40 value 42.431680
## iter 50 value 41.058181
## iter 60 value 39.768515
## iter 70 value 38.984534
## iter 80 value 38.964683
## iter 90 value 38.963632
## final value 38.963626
## converged
## # weights: 12
## initial value 429.336451
## iter 10 value 47.883049
## iter 20 value 43.499831
## iter 30 value 41.880976
## iter 40 value 39.366043
## iter 50 value 39.341667
## iter 60 value 39.316204
## iter 70 value 39.290956
## iter 80 value 39.284362
## iter 90 value 39.281555
## iter 100 value 39.275839
## final value 39.275839
## stopped after 100 iterations
## # weights: 34
## initial value 476.298677
## iter 10 value 42.819519
## iter 20 value 31.517941
## iter 30 value 27.595750
## iter 40 value 26.682247
## iter 50 value 24.366580
## iter 60 value 23.065664
## iter 70 value 22.701349
## iter 80 value 22.590481
## iter 90 value 22.495564
## iter 100 value 22.349801
## final value 22.349801
## stopped after 100 iterations
## # weights: 56
## initial value 378.896960
## iter 10 value 36.842685
## iter 20 value 18.639488
## iter 30 value 7.801945
## iter 40 value 7.569890
## iter 50 value 7.337884
## iter 60 value 7.167823
## iter 70 value 7.036394
## iter 80 value 6.917578
## iter 90 value 6.759553
## iter 100 value 6.626162
## final value 6.626162
## stopped after 100 iterations
## # weights: 12
## initial value 344.820755
## iter 10 value 55.774708
## iter 20 value 51.813829
## iter 30 value 48.483388
## iter 40 value 48.360408
## iter 50 value 45.090612
## iter 60 value 44.999071
## iter 70 value 44.808114
## iter 80 value 44.788065
## iter 90 value 44.781254
## iter 100 value 44.775761
## final value 44.775761
## stopped after 100 iterations
## # weights: 34
## initial value 324.434349
## iter 10 value 35.073614
## iter 20 value 28.309883
## iter 30 value 24.379088
## iter 40 value 19.858114
## iter 50 value 18.851557
## iter 60 value 18.478818
## iter 70 value 18.354701
## iter 80 value 18.329160
## iter 90 value 18.317284
## iter 100 value 18.315578
## final value 18.315578
## stopped after 100 iterations
## # weights: 56
## initial value 301.471018
## iter 10 value 31.048488
## iter 20 value 26.683856
## iter 30 value 22.148084
## iter 40 value 19.147597
## iter 50 value 17.815825
## iter 60 value 17.614277
## iter 70 value 17.408252
## iter 80 value 17.337243
## iter 90 value 17.136867
## iter 100 value 17.034596
## final value 17.034596
## stopped after 100 iterations
## # weights: 12
## initial value 441.591031
## iter 10 value 52.286962
## iter 20 value 47.424877
## iter 30 value 46.692740
## final value 46.692073
## converged
## # weights: 34
## initial value 400.902943
## iter 10 value 38.230663
## iter 20 value 36.476461
## iter 30 value 36.435445
## iter 40 value 36.381102
## iter 50 value 36.366204
## final value 36.366111
## converged
## # weights: 56
## initial value 354.704046
## iter 10 value 40.110429
## iter 20 value 35.734147
## iter 30 value 34.752683
## iter 40 value 34.670163
## iter 50 value 34.663302
## final value 34.662961
## converged
## # weights: 12
## initial value 382.292257
## iter 10 value 51.368504
## iter 20 value 40.145722
## iter 30 value 36.402339
## iter 40 value 36.363779
## iter 50 value 36.353899
## iter 60 value 36.352843
## iter 70 value 36.351267
## iter 80 value 36.350961
## iter 90 value 36.350758
## iter 100 value 36.350596
## final value 36.350596
## stopped after 100 iterations
## # weights: 34
## initial value 384.939391
## iter 10 value 62.462506
## iter 20 value 26.735137
## iter 30 value 22.711353
## iter 40 value 19.620734
## iter 50 value 18.236719
## iter 60 value 17.387882
## iter 70 value 17.273983
## iter 80 value 16.910083
## iter 90 value 16.867087
## iter 100 value 16.833337
## final value 16.833337
## stopped after 100 iterations
## # weights: 56
## initial value 517.139303
## iter 10 value 208.864116
## iter 20 value 21.380448
## iter 30 value 19.541237
## iter 40 value 18.126321
## iter 50 value 17.710978
## iter 60 value 17.540169
## iter 70 value 17.450041
## iter 80 value 17.277234
## iter 90 value 17.091978
## iter 100 value 16.952427
## final value 16.952427
## stopped after 100 iterations
## # weights: 12
## initial value 292.888398
## iter 10 value 51.333516
## iter 20 value 47.789397
## iter 30 value 42.795220
## iter 40 value 42.495016
## iter 50 value 42.488166
## iter 60 value 42.486191
## iter 70 value 42.482677
## iter 80 value 42.481495
## iter 90 value 42.480611
## iter 100 value 42.479688
## final value 42.479688
## stopped after 100 iterations
## # weights: 34
## initial value 370.309048
## iter 10 value 38.906729
## iter 20 value 33.553344
## iter 30 value 27.802055
## iter 40 value 18.987398
## iter 50 value 17.329191
## iter 60 value 17.253432
## iter 70 value 17.235331
## iter 80 value 17.229974
## iter 90 value 17.216502
## iter 100 value 17.198910
## final value 17.198910
## stopped after 100 iterations
## # weights: 56
## initial value 330.962845
## iter 10 value 41.726571
## iter 20 value 25.590695
## iter 30 value 18.909810
## iter 40 value 15.860228
## iter 50 value 14.256631
## iter 60 value 13.820281
## iter 70 value 13.049490
## iter 80 value 12.975588
## iter 90 value 12.926055
## iter 100 value 12.904974
## final value 12.904974
## stopped after 100 iterations
## # weights: 12
## initial value 445.887679
## iter 10 value 50.646446
## iter 20 value 49.089459
## iter 30 value 48.772241
## iter 30 value 48.772241
## iter 30 value 48.772241
## final value 48.772241
## converged
## # weights: 34
## initial value 539.285830
## iter 10 value 72.414357
## iter 20 value 44.581740
## iter 30 value 40.041652
## iter 40 value 39.144273
## iter 50 value 38.875064
## iter 60 value 38.797750
## iter 70 value 38.788990
## iter 80 value 38.780930
## final value 38.780763
## converged
## # weights: 56
## initial value 300.532220
## iter 10 value 93.758304
## iter 20 value 46.410806
## iter 30 value 38.930739
## iter 40 value 37.801074
## iter 50 value 37.469783
## iter 60 value 37.373706
## iter 70 value 37.369824
## final value 37.369791
## converged
## # weights: 12
## initial value 342.181028
## iter 10 value 40.288158
## iter 20 value 37.523293
## iter 30 value 37.249012
## iter 40 value 37.119649
## iter 50 value 36.475494
## iter 60 value 36.287119
## iter 70 value 35.941428
## iter 80 value 35.813799
## iter 90 value 35.812278
## iter 100 value 35.809003
## final value 35.809003
## stopped after 100 iterations
## # weights: 34
## initial value 379.266270
## iter 10 value 43.021067
## iter 20 value 38.833754
## iter 30 value 35.832750
## iter 40 value 32.652108
## iter 50 value 32.441961
## iter 60 value 32.066898
## iter 70 value 31.783041
## iter 80 value 31.566594
## iter 90 value 31.469011
## iter 100 value 31.424603
## final value 31.424603
## stopped after 100 iterations
## # weights: 56
## initial value 369.470832
## iter 10 value 35.821117
## iter 20 value 15.908660
## iter 30 value 8.328573
## iter 40 value 3.877744
## iter 50 value 1.903260
## iter 60 value 1.023840
## iter 70 value 0.898991
## iter 80 value 0.836879
## iter 90 value 0.751292
## iter 100 value 0.685882
## final value 0.685882
## stopped after 100 iterations
## # weights: 12
## initial value 321.325968
## iter 10 value 41.748205
## iter 20 value 36.690287
## iter 30 value 35.466382
## iter 40 value 34.924324
## iter 50 value 34.879107
## iter 60 value 34.873452
## iter 70 value 34.871806
## iter 80 value 34.870474
## iter 90 value 34.869605
## iter 100 value 34.868966
## final value 34.868966
## stopped after 100 iterations
## # weights: 34
## initial value 389.665964
## iter 10 value 29.388989
## iter 20 value 20.570303
## iter 30 value 11.034016
## iter 40 value 9.374116
## iter 50 value 9.301317
## iter 60 value 9.298310
## final value 9.298307
## converged
## # weights: 56
## initial value 266.877699
## iter 10 value 23.487453
## iter 20 value 12.065476
## iter 30 value 9.874584
## iter 40 value 7.267801
## iter 50 value 7.086451
## iter 60 value 7.028914
## iter 70 value 7.005241
## iter 80 value 6.991960
## iter 90 value 6.987269
## iter 100 value 6.982161
## final value 6.982161
## stopped after 100 iterations
## # weights: 12
## initial value 320.534221
## iter 10 value 63.421444
## iter 20 value 45.728887
## iter 30 value 44.538101
## iter 40 value 44.535136
## iter 40 value 44.535136
## iter 40 value 44.535136
## final value 44.535136
## converged
## # weights: 34
## initial value 407.770635
## iter 10 value 54.542195
## iter 20 value 34.350959
## iter 30 value 34.057233
## iter 40 value 34.053718
## final value 34.053086
## converged
## # weights: 56
## initial value 454.249533
## iter 10 value 70.206337
## iter 20 value 34.001490
## iter 30 value 31.528974
## iter 40 value 31.231718
## iter 50 value 30.920129
## iter 60 value 30.750788
## iter 70 value 30.678612
## iter 80 value 30.673839
## final value 30.673837
## converged
## # weights: 12
## initial value 400.898661
## iter 10 value 33.253193
## iter 20 value 32.914174
## iter 30 value 32.889662
## iter 40 value 32.884246
## iter 50 value 32.879964
## iter 60 value 32.874967
## iter 70 value 32.873948
## iter 80 value 32.873462
## iter 90 value 32.873192
## iter 100 value 32.873123
## final value 32.873123
## stopped after 100 iterations
## # weights: 34
## initial value 382.556717
## iter 10 value 36.280934
## iter 20 value 17.930640
## iter 30 value 15.813059
## iter 40 value 13.913675
## iter 50 value 6.643322
## iter 60 value 6.165713
## iter 70 value 6.140107
## iter 80 value 6.128952
## iter 90 value 6.096947
## iter 100 value 6.079607
## final value 6.079607
## stopped after 100 iterations
## # weights: 56
## initial value 297.748314
## iter 10 value 28.861229
## iter 20 value 14.571782
## iter 30 value 9.282334
## iter 40 value 7.902590
## iter 50 value 7.783095
## iter 60 value 7.749937
## iter 70 value 7.659873
## iter 80 value 6.613719
## iter 90 value 6.584836
## iter 100 value 6.571720
## final value 6.571720
## stopped after 100 iterations
## # weights: 12
## initial value 339.317539
## iter 10 value 183.974334
## iter 20 value 53.549126
## iter 30 value 46.256768
## iter 40 value 42.709297
## iter 50 value 42.570728
## iter 60 value 42.551765
## iter 70 value 42.541596
## iter 80 value 42.539767
## iter 90 value 42.538251
## iter 100 value 42.535639
## final value 42.535639
## stopped after 100 iterations
## # weights: 34
## initial value 396.418010
## iter 10 value 42.095411
## iter 20 value 33.884891
## iter 30 value 27.376367
## iter 40 value 25.038883
## iter 50 value 21.657024
## iter 60 value 20.737880
## iter 70 value 17.361819
## iter 80 value 14.592033
## iter 90 value 12.617083
## iter 100 value 12.189366
## final value 12.189366
## stopped after 100 iterations
## # weights: 56
## initial value 325.357705
## iter 10 value 34.777626
## iter 20 value 22.528115
## iter 30 value 15.106759
## iter 40 value 13.386605
## iter 50 value 13.367140
## iter 60 value 13.366071
## final value 13.366069
## converged
## # weights: 12
## initial value 328.046979
## iter 10 value 63.438203
## iter 20 value 49.793236
## iter 30 value 49.132896
## final value 49.110519
## converged
## # weights: 34
## initial value 323.935299
## iter 10 value 46.209800
## iter 20 value 40.752360
## iter 30 value 39.742886
## iter 40 value 39.518575
## iter 50 value 39.442077
## final value 39.442069
## converged
## # weights: 56
## initial value 449.071180
## iter 10 value 81.019611
## iter 20 value 47.380764
## iter 30 value 43.872064
## iter 40 value 41.699791
## iter 50 value 41.065785
## iter 60 value 40.939268
## iter 70 value 40.900264
## iter 80 value 40.896946
## iter 90 value 40.896722
## iter 100 value 40.896104
## final value 40.896104
## stopped after 100 iterations
## # weights: 12
## initial value 326.760562
## iter 10 value 43.955248
## iter 20 value 36.831036
## iter 30 value 33.451374
## iter 40 value 33.386855
## iter 50 value 33.379406
## iter 60 value 33.378513
## iter 70 value 33.377686
## iter 80 value 33.376411
## iter 90 value 33.375892
## iter 100 value 33.375764
## final value 33.375764
## stopped after 100 iterations
## # weights: 34
## initial value 428.856325
## iter 10 value 44.885332
## iter 20 value 28.619876
## iter 30 value 25.215948
## iter 40 value 24.038104
## iter 50 value 23.847601
## iter 60 value 23.800001
## iter 70 value 23.777096
## iter 80 value 23.742404
## iter 90 value 23.683407
## iter 100 value 23.646538
## final value 23.646538
## stopped after 100 iterations
## # weights: 56
## initial value 408.831512
## iter 10 value 35.150293
## iter 20 value 19.962417
## iter 30 value 6.999703
## iter 40 value 6.410355
## iter 50 value 6.254994
## iter 60 value 6.161725
## iter 70 value 6.108996
## iter 80 value 6.081319
## iter 90 value 6.067144
## iter 100 value 6.056036
## final value 6.056036
## stopped after 100 iterations
## # weights: 12
## initial value 321.701873
## iter 10 value 43.680934
## iter 20 value 36.648538
## iter 30 value 36.456665
## iter 40 value 36.332539
## iter 50 value 36.318616
## iter 60 value 36.310392
## iter 70 value 36.306801
## iter 80 value 36.305603
## iter 90 value 36.305059
## final value 36.304997
## converged
## # weights: 34
## initial value 300.159570
## iter 10 value 39.055026
## iter 20 value 33.840220
## iter 30 value 30.603859
## iter 40 value 28.952140
## iter 50 value 27.795766
## iter 60 value 26.925566
## iter 70 value 25.607480
## iter 80 value 22.875297
## iter 90 value 21.140866
## iter 100 value 19.660687
## final value 19.660687
## stopped after 100 iterations
## # weights: 56
## initial value 282.174286
## iter 10 value 32.145654
## iter 20 value 18.082127
## iter 30 value 12.459379
## iter 40 value 10.960882
## iter 50 value 10.652498
## iter 60 value 10.601143
## iter 70 value 10.584815
## iter 80 value 10.582475
## iter 90 value 10.574824
## iter 100 value 10.545102
## final value 10.545102
## stopped after 100 iterations
## # weights: 12
## initial value 338.491261
## iter 10 value 57.656398
## iter 20 value 48.425059
## iter 30 value 46.955982
## iter 40 value 46.919184
## iter 40 value 46.919184
## iter 40 value 46.919184
## final value 46.919184
## converged
## # weights: 34
## initial value 388.872597
## iter 10 value 64.351406
## iter 20 value 47.266682
## iter 30 value 41.781574
## iter 40 value 38.213982
## iter 50 value 37.343387
## iter 60 value 37.030934
## iter 70 value 36.890786
## iter 80 value 36.846767
## iter 90 value 36.845837
## final value 36.845835
## converged
## # weights: 56
## initial value 314.139149
## iter 10 value 39.705645
## iter 20 value 37.016868
## iter 30 value 36.067629
## iter 40 value 35.824429
## iter 50 value 35.783243
## iter 60 value 35.587943
## iter 70 value 34.866619
## iter 80 value 34.810830
## final value 34.809798
## converged
## # weights: 12
## initial value 317.804136
## iter 10 value 55.228707
## iter 20 value 42.605093
## iter 30 value 41.648684
## iter 40 value 36.608355
## iter 50 value 36.485480
## iter 60 value 36.471912
## iter 70 value 36.464600
## iter 80 value 36.461373
## iter 90 value 36.458796
## iter 100 value 36.457700
## final value 36.457700
## stopped after 100 iterations
## # weights: 34
## initial value 315.846299
## iter 10 value 38.473415
## iter 20 value 26.848986
## iter 30 value 20.270260
## iter 40 value 18.296945
## iter 50 value 18.100182
## iter 60 value 17.907682
## iter 70 value 17.876446
## iter 80 value 17.846866
## iter 90 value 17.822412
## iter 100 value 17.767413
## final value 17.767413
## stopped after 100 iterations
## # weights: 56
## initial value 335.972417
## iter 10 value 29.936780
## iter 20 value 19.271285
## iter 30 value 13.712456
## iter 40 value 12.629135
## iter 50 value 12.481253
## iter 60 value 12.426979
## iter 70 value 12.145665
## iter 80 value 11.385391
## iter 90 value 11.080061
## iter 100 value 10.601434
## final value 10.601434
## stopped after 100 iterations
## # weights: 12
## initial value 373.437219
## iter 10 value 46.335578
## iter 20 value 44.162371
## iter 30 value 43.714313
## iter 40 value 42.541834
## iter 50 value 42.525357
## final value 42.525324
## converged
## # weights: 34
## initial value 303.287895
## iter 10 value 39.238993
## iter 20 value 36.067627
## iter 30 value 32.632827
## iter 40 value 31.610994
## iter 50 value 30.855448
## iter 60 value 30.341468
## iter 70 value 30.135316
## iter 80 value 30.035435
## iter 90 value 29.930592
## iter 100 value 29.770385
## final value 29.770385
## stopped after 100 iterations
## # weights: 56
## initial value 297.621298
## iter 10 value 34.972015
## iter 20 value 18.943944
## iter 30 value 11.586677
## iter 40 value 11.282534
## iter 50 value 11.021814
## iter 60 value 10.410503
## iter 70 value 9.605171
## iter 80 value 9.522675
## iter 90 value 8.214682
## iter 100 value 8.175033
## final value 8.175033
## stopped after 100 iterations
## # weights: 12
## initial value 323.345105
## iter 10 value 58.209433
## iter 20 value 54.315007
## iter 30 value 54.270182
## iter 30 value 54.270182
## iter 30 value 54.270182
## final value 54.270182
## converged
## # weights: 34
## initial value 422.076971
## iter 10 value 46.170339
## iter 20 value 42.350684
## iter 30 value 41.441216
## iter 40 value 41.012147
## iter 50 value 40.896813
## iter 60 value 40.881822
## iter 70 value 40.881225
## iter 80 value 40.875106
## final value 40.874913
## converged
## # weights: 56
## initial value 306.668049
## iter 10 value 60.690236
## iter 20 value 46.284930
## iter 30 value 42.735875
## iter 40 value 41.007019
## iter 50 value 40.491250
## iter 60 value 39.621355
## iter 70 value 39.060881
## iter 80 value 39.033623
## iter 90 value 39.032187
## final value 39.032074
## converged
## # weights: 12
## initial value 302.761041
## iter 10 value 83.560537
## iter 20 value 62.563210
## iter 30 value 49.839441
## iter 40 value 46.705630
## iter 50 value 46.581315
## iter 60 value 46.530917
## iter 70 value 46.373652
## iter 80 value 44.651749
## iter 90 value 43.965901
## iter 100 value 43.729956
## final value 43.729956
## stopped after 100 iterations
## # weights: 34
## initial value 357.720110
## iter 10 value 48.561801
## iter 20 value 42.404880
## iter 30 value 41.543935
## iter 40 value 39.408957
## iter 50 value 35.645368
## iter 60 value 35.145612
## iter 70 value 35.068424
## iter 80 value 34.979160
## iter 90 value 34.924724
## iter 100 value 33.507282
## final value 33.507282
## stopped after 100 iterations
## # weights: 56
## initial value 401.677397
## iter 10 value 35.902627
## iter 20 value 23.675744
## iter 30 value 11.245062
## iter 40 value 10.976112
## iter 50 value 10.822860
## iter 60 value 9.424841
## iter 70 value 8.921258
## iter 80 value 8.775222
## iter 90 value 8.483119
## iter 100 value 7.500117
## final value 7.500117
## stopped after 100 iterations
## # weights: 12
## initial value 341.489758
## iter 10 value 48.563817
## iter 20 value 39.788874
## iter 30 value 39.579703
## iter 40 value 39.551215
## iter 50 value 39.529592
## iter 60 value 39.507280
## iter 70 value 39.499314
## iter 80 value 39.496387
## iter 90 value 39.486742
## iter 100 value 39.483188
## final value 39.483188
## stopped after 100 iterations
## # weights: 34
## initial value 330.979350
## iter 10 value 33.960451
## iter 20 value 22.919974
## iter 30 value 18.884964
## iter 40 value 18.551028
## iter 50 value 18.550439
## final value 18.550299
## converged
## # weights: 56
## initial value 366.530519
## iter 10 value 31.983994
## iter 20 value 19.072783
## iter 30 value 12.356932
## iter 40 value 12.069920
## iter 50 value 11.858794
## iter 60 value 11.801794
## iter 70 value 11.561430
## iter 80 value 11.512375
## iter 90 value 11.508395
## iter 100 value 11.504982
## final value 11.504982
## stopped after 100 iterations
## # weights: 12
## initial value 360.748343
## iter 10 value 83.858557
## iter 20 value 57.325467
## iter 30 value 50.454849
## iter 40 value 48.691569
## final value 48.690256
## converged
## # weights: 34
## initial value 421.251608
## iter 10 value 79.608387
## iter 20 value 41.007630
## iter 30 value 38.956423
## iter 40 value 37.784770
## iter 50 value 37.553882
## iter 60 value 36.905560
## iter 70 value 36.859086
## final value 36.859032
## converged
## # weights: 56
## initial value 303.982587
## iter 10 value 43.509859
## iter 20 value 37.730531
## iter 30 value 36.815942
## iter 40 value 36.664768
## iter 50 value 36.515796
## iter 60 value 36.370700
## iter 70 value 36.303726
## iter 80 value 36.296592
## final value 36.296559
## converged
## # weights: 12
## initial value 328.637795
## iter 10 value 47.299042
## iter 20 value 39.924365
## iter 30 value 39.651773
## iter 40 value 39.632332
## iter 50 value 39.622021
## iter 60 value 39.619574
## iter 70 value 39.618946
## iter 80 value 39.617697
## iter 90 value 39.616645
## iter 100 value 39.616475
## final value 39.616475
## stopped after 100 iterations
## # weights: 34
## initial value 420.846713
## iter 10 value 41.417072
## iter 20 value 33.677250
## iter 30 value 29.984111
## iter 40 value 29.832642
## iter 50 value 29.811944
## iter 60 value 29.799675
## iter 70 value 29.786340
## iter 80 value 29.779071
## iter 90 value 29.773013
## iter 100 value 29.756576
## final value 29.756576
## stopped after 100 iterations
## # weights: 56
## initial value 305.656107
## iter 10 value 33.063694
## iter 20 value 28.876505
## iter 30 value 24.388957
## iter 40 value 20.162492
## iter 50 value 19.530642
## iter 60 value 19.093866
## iter 70 value 18.709888
## iter 80 value 18.638073
## iter 90 value 18.591180
## iter 100 value 18.416488
## final value 18.416488
## stopped after 100 iterations
## # weights: 12
## initial value 362.923629
## iter 10 value 45.663543
## iter 20 value 44.513763
## iter 30 value 43.151358
## iter 40 value 41.514797
## iter 50 value 39.813640
## iter 60 value 39.808652
## iter 70 value 39.808294
## final value 39.808266
## convergedresultado_entrenamientoBC5 <- predict(modeloBC5, entrenamientoBC)
resultado_pruebaBC5 <- predict(modeloBC5, pruebaBC)
#Matriz de Confusión
mcre5 <- confusionMatrix(resultado_entrenamientoBC5, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre5## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 346 0
## malignant 10 192
##
## Accuracy : 0.9818
## 95% CI : (0.9667, 0.9912)
## No Information Rate : 0.6496
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9604
##
## Mcnemar's Test P-Value : 0.004427
##
## Sensitivity : 0.9719
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.9505
## Prevalence : 0.6496
## Detection Rate : 0.6314
## Detection Prevalence : 0.6314
## Balanced Accuracy : 0.9860
##
## 'Positive' Class : benign
## mcrp5 <- confusionMatrix(resultado_pruebaBC5, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 85 1
## malignant 3 46
##
## Accuracy : 0.9704
## 95% CI : (0.9259, 0.9919)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9354
##
## Mcnemar's Test P-Value : 0.6171
##
## Sensitivity : 0.9659
## Specificity : 0.9787
## Pos Pred Value : 0.9884
## Neg Pred Value : 0.9388
## Prevalence : 0.6519
## Detection Rate : 0.6296
## Detection Prevalence : 0.6370
## Balanced Accuracy : 0.9723
##
## 'Positive' Class : benign
## 6.Modelo con el Método rf
modeloBC6 <- train(Class ~ ., data=entrenamientoBC, method= "rf", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= expand.grid(mtry=c(2,4,6)) #Cambiar
)
resultado_entrenamientoBC6 <- predict(modeloBC6, entrenamientoBC)
resultado_pruebaBC6 <- predict(modeloBC6, pruebaBC)
#Matriz de Confusión
mcre6 <- confusionMatrix(resultado_entrenamientoBC6, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre6## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 356 0
## malignant 0 192
##
## Accuracy : 1
## 95% CI : (0.9933, 1)
## No Information Rate : 0.6496
## 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 : 0.6496
## Detection Rate : 0.6496
## Detection Prevalence : 0.6496
## Balanced Accuracy : 1.0000
##
## 'Positive' Class : benign
## mcrp6 <- confusionMatrix(resultado_pruebaBC6, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction benign malignant
## benign 85 1
## malignant 3 46
##
## Accuracy : 0.9704
## 95% CI : (0.9259, 0.9919)
## No Information Rate : 0.6519
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9354
##
## Mcnemar's Test P-Value : 0.6171
##
## Sensitivity : 0.9659
## Specificity : 0.9787
## Pos Pred Value : 0.9884
## Neg Pred Value : 0.9388
## Prevalence : 0.6519
## Detection Rate : 0.6296
## Detection Prevalence : 0.6370
## Balanced Accuracy : 0.9723
##
## 'Positive' Class : benign
## Resumen de resultados
resultados <- data.frame(
"1. svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
"2. svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"3. svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"4. rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"5. nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"6. rf" = c (mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisión de entrenamiento", "Precisión de prueba")
resultados## X1..svmLinear X2..svmRadial X3..svmPoly X4..rpart
## Precisión de entrenamiento 0.9708029 0.9963504 0.9708029 0.9635036
## Precisión de prueba 0.9777778 0.9555556 0.9777778 0.9555556
## X5..nnet X6..rf
## Precisión de entrenamiento 0.9817518 1.0000000
## Precisión de prueba 0.9703704 0.9703704Conclusiones
El modelo con el método de bosques aleatorios presenta sobreajuste, ya que tiene una alta precisión en entrenamiento pero baja en prueba.
Acorde al resumen de resultados, el mejor modelo es el de Máquina de Vectores de Soporte Lineal.
---
title: 'Machine Learning: Función CARET'
author: "Diego Perez a01275561"
date: "2/28/2024"
output: 
  html_document:
    toc: true
    toc_float: true
    code_download: true 
---

![](C:\\Users\\Diego Pérez\\Downloads\\iris.png)

# Teoría
El paquete *CARET (Clasification And REgression Training)* es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.

# Instalar paquetes y llamar librerías
```{r}
#install.packages("ggplot2") #Gráficas con mejor diseño
library(ggplot2)
#install.packages("lattice") #Crear gráficos
library(lattice)
#install.packages("caret") #Algoritmos de aprendizaje automático
library(caret)
#install.packages("datasets") #Usar la base de datos IRIS
library(datasets)
#install.packages("mlbench")
library(mlbench)
#install.packages("dplyr")
library(dplyr)
#install.packages("DataExplorer")
library(DataExplorer)
```

## Crear base de datos
```{r}
df <- data.frame(iris)
```

## Análisis exploratorio
```{r}
summary(df)
```

```{r}
str(df)
#create_report()
```

```{r}
plot_missing(df)
```

```{r}
plot_histogram(df)
```

```{r}
plot_correlation(df)
```

**Nota: La variable que queremos predecir debe tener formato de FACTOR**

## Partir datos 80-20
```{r}
set.seed(123)
renglones_entrenamento <- createDataPartition(df$Species, p=0.8, list=FALSE)
entrenamiento <- iris[renglones_entrenamento, ]
prueba <- iris[-renglones_entrenamento, ]
```

## Distintos tipos de Métodos para Modelar
Los métodos más utilizado para modelaraprendizaje automático son:  

* **SSM**: *Support Vector Machine* o Máquina de Vectores de Sporte Hay varios subtipos: Linear (svmLinear), Radial (svmRadial), Polinómico (svmPoly), etc.  
* **Árbol de Decisión**: rpart  
* **Redes Neuronales**: nnet
* **Random Forest** o Bosques Aleatorios: rf 

### 1.Modelo con el Método svmLinear
```{r}
modelo1 <- train(Species ~ ., data=entrenamiento, method= "svmLinear", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(C=1) #Cuando es svmLinear
                )

resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)

#Matriz de Confusión
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre1
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp1
```

### 2.Modelo con el Método svmRadial
```{r}
modelo2 <- train(Species ~ ., data=entrenamiento, method= "svmRadial", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(sigma=1, C=1) #Cambiar
                )
 
resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)

#Matriz de Confusión
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre2
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp2
```

### 3.Modelo con el Método svmPoly
```{r}
modelo3 <- train(Species ~ ., data=entrenamiento, method= "svmPoly", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(degree=1, scale=1, C=1) #Cambiar
                )
 
resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)

#Matriz de Confusión
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre3
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp3
```

### 4.Modelo con el Método rpart
```{r}
modelo4 <- train(Species ~ ., data=entrenamiento, method= "rpart", 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
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre4
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp4
```

### 5.Modelo con el Método nnet
```{r}
modelo5 <- train(Species ~ ., data=entrenamiento, method= "nnet", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10) #Cambiar
                )
 
resultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)

#Matriz de Confusión
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre5
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp5
```

### 6.Modelo con el Método rf
```{r}
modelo6 <- train(Species ~ ., data=entrenamiento, method= "rf", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= expand.grid(mtry=c(2,4,6)) #Cambiar
                )
 
resultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)

#Matriz de Confusión
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$Species) #matriz de confusión del resultado del entrenamiento
mcre6
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$Species) #matriz de confusión del resultado de la prueba
mcrp6
```

## Resumen de resultados
```{r}
resultados <- data.frame(
  "1. svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
  "2. svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
  "3. svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
  "4. rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
  "5. nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
  "6. rf" = c (mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisión de entrenamiento", "Precisión de prueba")
resultados
```

## Conclusiones
El modelo con el método de bosques aleatorios presenta sobreajuste, ya que tiene una alta precisión en entrenamiento pero baja en prueba.  

Acorde al resumen de resultados, el mejor modelo es el de **Máquina de Vectores de Soporte Lineal**.

# EJERCICIO "Cáncer de mama"

## Crear base de datos
```{r}
data("BreastCancer")
df <- data.frame(BreastCancer)
#Quitar columna ID'S
df <- df %>% select(-Id)
#QUITAR NA'S
df <- na.omit(df)
```

## Análisis exploratorio
```{r}
summary(df)
str(df)
#create_report()
```

```{r}
plot_missing(df)
```

```{r}
plot_correlation(df)
```


## Cambiar las variables para que sean númericas
```{r}
df$Cl.thickness <- as.numeric(as.character(df$Cl.thickness))
df$Cell.size <- as.numeric(as.character(df$Cell.size))
df$Cell.shape <- as.numeric(as.character(df$Cell.shape))
df$Marg.adhesion <- as.numeric(as.character(df$Marg.adhesion))
df$Epith.c.size <- as.numeric(as.character(df$Epith.c.size))
df$Bare.nuclei <- as.numeric(as.character(df$Bare.nuclei))
df$Bl.cromatin <- as.numeric(as.character(df$Bl.cromatin))
df$Normal.nucleoli <- as.numeric(as.character(df$Normal.nucleoli))
df$Mitoses <- as.numeric(as.character(df$Mitoses))
```


**Nota: La variable que queremos predecir debe tener formato de FACTOR**

## Partir datos 80-20
```{r}
set.seed(123)
renglones_entrenamentoBC <- createDataPartition(df$Class, p=0.8, list=FALSE)
entrenamientoBC <- df[renglones_entrenamentoBC, ]
pruebaBC <- df[-renglones_entrenamentoBC, ]
```

## Distintos tipos de Métodos para Modelar
Los métodos más utilizado para modelaraprendizaje automático son:  

* **SSM**: *Support Vector Machine* o Máquina de Vectores de Sporte Hay varios subtipos: Linear (svmLinear), Radial (svmRadial), Polinómico (svmPoly), etc.  
* **Árbol de Decisión**: rpart  
* **Redes Neuronales**: nnet
* **Random Forest** o Bosques Aleatorios: rf 

### 1.Modelo con el Método svmLinear
```{r}
modeloBC1 <- train(Class ~ ., data=entrenamientoBC, method= "svmLinear", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(C=1) #Cuando es svmLinear
                )

resultado_entrenamientoBC1 <- predict(modeloBC1, entrenamientoBC)
resultado_pruebaBC1 <- predict(modeloBC1, pruebaBC)

#Matriz de Confusión
mcre1 <- confusionMatrix(resultado_entrenamientoBC1, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre1
mcrp1 <- confusionMatrix(resultado_pruebaBC1, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp1
```

### 2.Modelo con el Método svmRadial
```{r}
modeloBC2 <- train(Class ~ ., data=entrenamientoBC, method= "svmRadial", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(sigma=1, C=1) #Cambiar
                )
 
resultado_entrenamientoBC2 <- predict(modeloBC2, entrenamientoBC)
resultado_pruebaBC2 <- predict(modeloBC2, pruebaBC)

#Matriz de Confusión
mcre2 <- confusionMatrix(resultado_entrenamientoBC2, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre2
mcrp2 <- confusionMatrix(resultado_pruebaBC2, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp2
```

### 3.Modelo con el Método svmPoly
```{r}
modeloBC3 <- train(Class ~ ., data=entrenamientoBC, method= "svmPoly", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= data.frame(degree=1, scale=1, C=1) #Cambiar
                )
 
resultado_entrenamientoBC3 <- predict(modeloBC3, entrenamientoBC)
resultado_pruebaBC3 <- predict(modeloBC3, pruebaBC)

#Matriz de Confusión
mcre3 <- confusionMatrix(resultado_entrenamientoBC3, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre3
mcrp3 <- confusionMatrix(resultado_pruebaBC3, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp3
```

### 4.Modelo con el Método rpart
```{r}
modeloBC4 <- train(Class ~ ., data=entrenamientoBC, method= "rpart", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneLength=10 #Cambiar
                )
 
resultado_entrenamientoBC4 <- predict(modeloBC4, entrenamientoBC)
resultado_pruebaBC4 <- predict(modeloBC4, pruebaBC)

#Matriz de Confusión
mcre4 <- confusionMatrix(resultado_entrenamientoBC4, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre4
mcrp4 <- confusionMatrix(resultado_pruebaBC4, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp4
```

### 5.Modelo con el Método nnet
```{r}
modeloBC5 <- train(Class ~ ., data=entrenamientoBC, method= "nnet", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10) #Cambiar
                )
 
resultado_entrenamientoBC5 <- predict(modeloBC5, entrenamientoBC)
resultado_pruebaBC5 <- predict(modeloBC5, pruebaBC)

#Matriz de Confusión
mcre5 <- confusionMatrix(resultado_entrenamientoBC5, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre5
mcrp5 <- confusionMatrix(resultado_pruebaBC5, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp5
```

### 6.Modelo con el Método rf
```{r}
modeloBC6 <- train(Class ~ ., data=entrenamientoBC, method= "rf", preProcess=c("scale", "center"), trControl = trainControl(method = "cv", number=10), tuneGrid= expand.grid(mtry=c(2,4,6)) #Cambiar
                )
 
resultado_entrenamientoBC6 <- predict(modeloBC6, entrenamientoBC)
resultado_pruebaBC6 <- predict(modeloBC6, pruebaBC)

#Matriz de Confusión
mcre6 <- confusionMatrix(resultado_entrenamientoBC6, entrenamientoBC$Class) #matriz de confusión del resultado del entrenamiento
mcre6
mcrp6 <- confusionMatrix(resultado_pruebaBC6, pruebaBC$Class) #matriz de confusión del resultado de la prueba
mcrp6
```

## Resumen de resultados
```{r}
resultados <- data.frame(
  "1. svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
  "2. svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
  "3. svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
  "4. rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
  "5. nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
  "6. rf" = c (mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisión de entrenamiento", "Precisión de prueba")
resultados
```

## Conclusiones
El modelo con el método de bosques aleatorios presenta sobreajuste, ya que tiene una alta precisión en entrenamiento pero baja en prueba.  

Acorde al resumen de resultados, el mejor modelo es el de **Máquina de Vectores de Soporte Lineal**.