CARET
Ana Paula Alvear Cantú
2025-02-20
Teoría
El paquete caret (Classification And Regression Training) es una herramienta poderosa para la implementación de modelos de Machine Learning.
Instalar paquetes y llamar librerías
#install.packages("caret") #Algoritmos de aprendizaje automático
library(caret)
#install.packages("datasets") #Para usar la base de datos "Iris"
library(datasets)
library(ggplot2) #Gráficascon mejor diseñ
#install.packages("lattice") #Crear gráficos
library(lattice)
#install.packages("DataExplorer") #Análisis descriptivo
library(DataExplorer)
#install.packages("kernlab")
library(kernlab)
#install.packages("rpart")
library(rpart)Importar la base de datos
df <- data.frame(iris)Análisis Descriptivo
create_report(df)##
##
## processing file: report.rmd## | | | 0% | |. | 2% | |.. | 5% [global_options] | |... | 7% | |.... | 10% [introduce] | |.... | 12% | |..... | 14% [plot_intro]## | |...... | 17% | |....... | 19% [data_structure] | |........ | 21% | |......... | 24% [missing_profile]## | |.......... | 26% | |........... | 29% [univariate_distribution_header] | |........... | 31% | |............ | 33% [plot_histogram]## | |............. | 36% | |.............. | 38% [plot_density] | |............... | 40% | |................ | 43% [plot_frequency_bar]## | |................. | 45% | |.................. | 48% [plot_response_bar] | |.................. | 50% | |................... | 52% [plot_with_bar] | |.................... | 55% | |..................... | 57% [plot_normal_qq]## | |...................... | 60% | |....................... | 62% [plot_response_qq] | |........................ | 64% | |......................... | 67% [plot_by_qq] | |.......................... | 69% | |.......................... | 71% [correlation_analysis]## | |........................... | 74% | |............................ | 76% [principal_component_analysis]## | |............................. | 79% | |.............................. | 81% [bivariate_distribution_header] | |............................... | 83% | |................................ | 86% [plot_response_boxplot] | |................................. | 88% | |................................. | 90% [plot_by_boxplot] | |.................................. | 93% | |................................... | 95% [plot_response_scatterplot] | |.................................... | 98% | |.....................................| 100% [plot_by_scatterplot] ## output file: /Users/anapaualvear/Desktop/modulo 2 R/report.knit.md## /private/var/folders/gf/rh6n8hhj40l17_3n_7zgjzd80000gn/T/AppTranslocation/A1E2447B-2BD3-4050-877B-A0415BBE092C/d/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/pandoc +RTS -K512m -RTS '/Users/anapaualvear/Desktop/modulo 2 R/report.knit.md' --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output pandoc65a56c8ede3f.html --lua-filter /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 6 --template /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=yeti --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /var/folders/gf/rh6n8hhj40l17_3n_7zgjzd80000gn/T//RtmpHh7Hld/rmarkdown-str65a566287e96.html##
## Output created: report.htmlplot_missing(df)
plot_histogram(df)
plot_correlation(df)
NOTA: La variable que queremos predecir debe tener formato de FACTOR.
Partir los datos 80-20
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$Species, p=0.8, list=FALSE)
entrenamiento <- iris[renglones_entrenamiento, ]
prueba <- iris[-renglones_entrenamiento, ]Distintos tipos de métodos para modelar
Los métodos más utilizados para modelar aprnedizaje automático
son:
* SVM: Support Vector Machine o Máquina de
Vectores de Soporte. hay varios subtipos: Linear (svmLinear), radial
(svmRadial), polinómico (svmPoly), etc.
* Árbol de Decisión: rpart
* Redes Neuronales: nnet
* Random Forest o Bosques Aleatorios: rf
La validación cruzada (Cross Validation, CV) es una técnica para evaluar el rendimiento de un modelo, dividiendo los datos en múltiples subconjuntos, prermitiendo medir su capacidad de generlaización y evitar sobreajuste (Overfitting).
La Matriz de Confusión¨ permite analizar qué tan bien funciona un modelo y qué tipos de errores comete. Lo que hace es comparar las predicciones del modelo con los valores reales de la variable objetivo.
Si la precisión es muy alta en entrenamiento (95-100%), pero baja en prueba (60-70%), es una señal de sobreajuste (overfitting).
Modelo 1. SVM Linear
modelo1 <- train(Species ~ ., data = entrenamiento,
method = "svmLinear", #Cambiar
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(C=1) #Cambiar hiperparámetro
)
resultado_entrenamiento1 <- predict(modelo1,entrenamiento)
resultados_prueba1 <- predict(modelo1,prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$Species)
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 0.9938# Matriz de Confusión del Resultado de la Prueba
mcrp1 <- confusionMatrix(resultados_prueba1, prueba$Species)
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 1
## virginica 0 0 9
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.9000
## Specificity 1.0000 0.9500 1.0000
## Pos Pred Value 1.0000 0.9091 1.0000
## Neg Pred Value 1.0000 1.0000 0.9524
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3000
## Detection Prevalence 0.3333 0.3667 0.3000
## Balanced Accuracy 1.0000 0.9750 0.9500Modelo 2. SVM Radial
modelo2 <- train(Species ~ ., data = entrenamiento,
method = "svmRadial", #Cambiar
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(sigma=1, C=1) #Cambiar hiperparámetro
)
resultado_entrenamiento2 <- predict(modelo2,entrenamiento)
resultados_prueba2 <- predict(modelo2,prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$Species)
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 0
## virginica 0 1 40
##
## Overall Statistics
##
## Accuracy : 0.9917
## 95% CI : (0.9544, 0.9998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9875
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 1.0000
## Specificity 1.0000 1.0000 0.9875
## Pos Pred Value 1.0000 1.0000 0.9756
## Neg Pred Value 1.0000 0.9877 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3333
## Detection Prevalence 0.3333 0.3250 0.3417
## Balanced Accuracy 1.0000 0.9875 0.9938# Matriz de Confusión del Resultado de la Prueba
mcrp2 <- confusionMatrix(resultados_prueba2, prueba$Species)
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.9000Modelo 3. SVM Polinómico
modelo3 <- train(Species ~ ., data = entrenamiento,
method = "svmPoly", #Cambiar
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(degree=1, scale=1, C=1) #Cambiar hiperparámetro
)
resultado_entrenamiento3 <- predict(modelo3,entrenamiento)
resultados_prueba3 <- predict(modelo3,prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$Species)
mcre3## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 0
## virginica 0 1 40
##
## Overall Statistics
##
## Accuracy : 0.9917
## 95% CI : (0.9544, 0.9998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9875
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 1.0000
## Specificity 1.0000 1.0000 0.9875
## Pos Pred Value 1.0000 1.0000 0.9756
## Neg Pred Value 1.0000 0.9877 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3333
## Detection Prevalence 0.3333 0.3250 0.3417
## Balanced Accuracy 1.0000 0.9875 0.9938# Matriz de Confusión del Resultado de la Prueba
mcrp3 <- confusionMatrix(resultados_prueba3, prueba$Species)
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 1
## virginica 0 0 9
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.9000
## Specificity 1.0000 0.9500 1.0000
## Pos Pred Value 1.0000 0.9091 1.0000
## Neg Pred Value 1.0000 1.0000 0.9524
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3000
## Detection Prevalence 0.3333 0.3667 0.3000
## Balanced Accuracy 1.0000 0.9750 0.9500Modelo 4. Árbol de decisión
modelo4 <- train(Species ~ ., data = entrenamiento,
method = "rpart", #Cambiar
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneLength = 10 #Cambiar hiperparámetros
)
resultado_entrenamiento4 <- predict(modelo4,entrenamiento)
resultados_prueba4 <- predict(modelo4,prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$Species)
mcre4## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 39 3
## virginica 0 1 37
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.9169, 0.9908)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9750 0.9250
## Specificity 1.0000 0.9625 0.9875
## Pos Pred Value 1.0000 0.9286 0.9737
## Neg Pred Value 1.0000 0.9872 0.9634
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3250 0.3083
## Detection Prevalence 0.3333 0.3500 0.3167
## Balanced Accuracy 1.0000 0.9688 0.9563# Matriz de Confusión del Resultado de la Prueba
mcrp4 <- confusionMatrix(resultados_prueba4, prueba$Species)
mcrp4## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.9000Modelo 5. Redes Neuronales
modelo5 <- train(Species ~ ., data = entrenamiento,
method = "nnet", #Cambiar
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10),
trace=FALSE
)
resultado_entrenamiento5 <- predict(modelo5,entrenamiento)
resultados_prueba5 <- predict(modelo5,prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$Species)
mcre5## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 36 0
## virginica 0 4 40
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.9169, 0.9908)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9000 1.0000
## Specificity 1.0000 1.0000 0.9500
## Pos Pred Value 1.0000 1.0000 0.9091
## Neg Pred Value 1.0000 0.9524 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3000 0.3333
## Detection Prevalence 0.3333 0.3000 0.3667
## Balanced Accuracy 1.0000 0.9500 0.9750# Matriz de Confusión del Resultado de la Prueba
mcrp5 <- confusionMatrix(resultados_prueba5, prueba$Species)
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 9 0
## virginica 0 1 10
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9000 1.0000
## Specificity 1.0000 1.0000 0.9500
## Pos Pred Value 1.0000 1.0000 0.9091
## Neg Pred Value 1.0000 0.9524 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3000 0.3333
## Detection Prevalence 0.3333 0.3000 0.3667
## Balanced Accuracy 1.0000 0.9500 0.9750Modelo 6. Bosques Aleatorios
modelo6 <- train(Species ~ ., data = entrenamiento,
method = "rf", #Cambiar
preProcess=c("scale","center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = expand.grid(mtry = c(2,4,6))
)## Warning in randomForest.default(x, y, mtry = param$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$mtry, ...): invalid mtry:
## reset to within valid rangeresultado_entrenamiento6 <- predict(modelo6,entrenamiento)
resultados_prueba6 <- predict(modelo6,prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$Species)
mcre6## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 40 0
## virginica 0 0 40
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9697, 1)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 1.0000
## Specificity 1.0000 1.0000 1.0000
## Pos Pred Value 1.0000 1.0000 1.0000
## Neg Pred Value 1.0000 1.0000 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3333
## Detection Prevalence 0.3333 0.3333 0.3333
## Balanced Accuracy 1.0000 1.0000 1.0000# Matriz de Confusión del Resultado de la Prueba
mcrp6 <- confusionMatrix(resultados_prueba6, prueba$Species)
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 2
## virginica 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.9333
## 95% CI : (0.7793, 0.9918)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 8.747e-12
##
## Kappa : 0.9
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.8000
## Specificity 1.0000 0.9000 1.0000
## Pos Pred Value 1.0000 0.8333 1.0000
## Neg Pred Value 1.0000 1.0000 0.9091
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.2667
## Detection Prevalence 0.3333 0.4000 0.2667
## Balanced Accuracy 1.0000 0.9500 0.9000Resumen de Resultados
resultados <- data.frame(
"SVM Lineal" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
"SVM Radial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"SVM Polinómico" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"Árbol de Decisión" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"Redes Neuronales" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"Bosques Aleatorios" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisión de Entrenamiento", "Precisión de Prueba")
resultados## SVM.Lineal SVM.Radial SVM.Polinómico
## Precisión de Entrenamiento 0.9916667 0.9916667 0.9916667
## Precisión de Prueba 0.9666667 0.9333333 0.9666667
## Árbol.de.Decisión Redes.Neuronales
## Precisión de Entrenamiento 0.9666667 0.9666667
## Precisión de Prueba 0.9333333 0.9666667
## Bosques.Aleatorios
## Precisión de Entrenamiento 1.0000000
## Precisión de Prueba 0.9333333