M1
Mariana Garibay - A00839593
27/02/2026
Teoría
El paquete CARET (Classification And REgression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.
Instalar paquetes y llamar librerías
# install.packages("ggplot2") # Gráficas
library(ggplot2)
# install.packages("lattice") # Crear gráficos
library(lattice)
# install.packages("caret") # Algoritmos de aprendizaje automático
library(caret)
# install.packages("DataExplorer") # Análisis Exploratorio
library(DataExplorer)Crear la base de datos
# Leemos el archivo CSV.
# stringsAsFactors = TRUE convierte automáticamente los textos (como "Yes", "No", "Male", "Female") en factores.
df <- read.csv("C:\\Users\\maria\\OneDrive\\Desktop\\TEC SEMESTRE 6\\M1_data.csv", stringsAsFactors = TRUE)
# Aseguramos que nuestra variable predictora (Target) sea explícitamente un factor
df$m1_purchase <- as.factor(df$m1_purchase)
# Eliminamos cualquier valor nulo por seguridad
df <- na.omit(df)Entender la base de datos
summary(df)## trust_apple interest_computers age_computer user_pcmac appleproducts_count
## No : 19 Min. :2.000 Min. :0.000 Apple:86 Min. :0.000
## Yes:114 1st Qu.:3.000 1st Qu.:1.000 Hp : 1 1st Qu.:1.000
## Median :4.000 Median :3.000 Other: 1 Median :3.000
## Mean :3.812 Mean :2.827 PC :45 Mean :2.609
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :9.000 Max. :8.000
##
## familiarity_m1 f_batterylife f_price f_size f_multitasking
## No :75 Min. :1.000 Min. :1.000 Min. :1.000 Min. :2.00
## Yes:58 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:4.00
## Median :5.000 Median :4.000 Median :3.000 Median :4.00
## Mean :4.526 Mean :3.872 Mean :3.158 Mean :4.12
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:5.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
##
## f_noise f_performance f_neural f_synergy
## Min. :1.000 Min. :2.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:2.000 1st Qu.:3.000
## Median :4.000 Median :5.000 Median :3.000 Median :4.000
## Mean :3.729 Mean :4.398 Mean :3.165 Mean :3.466
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## f_performanceloss m1_consideration m1_purchase gender age_group
## Min. :1.000 Min. :1.000 No :45 Female:61 Min. : 1.00
## 1st Qu.:3.000 1st Qu.:3.000 Yes:88 Male :72 1st Qu.: 2.00
## Median :4.000 Median :4.000 Median : 2.00
## Mean :3.376 Mean :3.609 Mean : 2.97
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.: 3.00
## Max. :5.000 Max. :5.000 Max. :10.00
##
## income_group status domain
## Min. :1.00 Employed :41 IT & Technology:33
## 1st Qu.:1.00 Retired : 1 Marketing :21
## Median :2.00 Self-Employed : 5 Business :14
## Mean :2.97 Student :84 Engineering : 7
## 3rd Qu.:4.00 Student ant employed: 1 Finance : 7
## Max. :7.00 Unemployed : 1 Science : 7
## (Other) :44str(df)## 'data.frame': 133 obs. of 22 variables:
## $ trust_apple : Factor w/ 2 levels "No","Yes": 1 2 2 2 2 2 2 1 2 2 ...
## $ interest_computers : int 4 2 5 2 4 3 3 3 4 5 ...
## $ age_computer : int 8 4 6 6 4 1 2 0 2 0 ...
## $ user_pcmac : Factor w/ 4 levels "Apple","Hp","Other",..: 4 4 4 1 1 1 1 4 1 1 ...
## $ appleproducts_count: int 0 1 0 4 7 2 7 0 6 7 ...
## $ familiarity_m1 : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 1 1 2 2 ...
## $ f_batterylife : int 5 5 3 4 5 5 4 5 4 5 ...
## $ f_price : int 4 5 4 3 3 5 3 5 4 3 ...
## $ f_size : int 3 5 2 3 3 4 4 4 3 5 ...
## $ f_multitasking : int 4 3 4 4 4 4 5 4 4 5 ...
## $ f_noise : int 4 4 1 4 4 5 5 3 4 5 ...
## $ f_performance : int 2 5 4 4 5 5 5 3 4 5 ...
## $ f_neural : int 2 2 2 4 3 5 3 2 3 3 ...
## $ f_synergy : int 1 2 2 4 4 4 3 2 3 5 ...
## $ f_performanceloss : int 1 4 2 3 4 2 2 3 4 5 ...
## $ m1_consideration : int 1 2 4 2 4 2 3 1 5 5 ...
## $ m1_purchase : Factor w/ 2 levels "No","Yes": 2 1 2 1 2 1 2 1 2 2 ...
## $ gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 2 1 2 2 2 2 ...
## $ age_group : int 2 2 2 2 5 2 6 2 8 4 ...
## $ income_group : int 2 3 2 2 7 2 7 2 7 6 ...
## $ status : Factor w/ 6 levels "Employed","Retired",..: 4 1 4 4 1 4 1 4 1 1 ...
## $ domain : Factor w/ 22 levels "Administration & Public Services",..: 21 10 13 3 12 17 13 22 13 12 ...# create_report(df)
plot_missing(df)## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the DataExplorer package.
## Please report the issue at
## <https://github.com/boxuancui/DataExplorer/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# plot_histogram(df)
# plot_correlation(df)Partir la base de datos
# Normalmente 80-20
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$m1_purchase, p=0.8, list=FALSE)
entrenamiento <- df[renglones_entrenamiento, ]
prueba <- df[-renglones_entrenamiento, ]Distintos tipos de Métodos para Modelar
modelo1 <- train(m1_purchase ~ ., data=entrenamiento,
method = "svmLinear",
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(C=1)
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusStudent ant employed, statusUnemployed,
## domainCommunication , domainRealestate, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainConsulting , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$m1_purchase)
mcre1## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 30 4
## Yes 6 67
##
## Accuracy : 0.9065
## 95% CI : (0.8348, 0.9543)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 4.281e-09
##
## Kappa : 0.7878
##
## Mcnemar's Test P-Value : 0.7518
##
## Sensitivity : 0.8333
## Specificity : 0.9437
## Pos Pred Value : 0.8824
## Neg Pred Value : 0.9178
## Prevalence : 0.3364
## Detection Rate : 0.2804
## Detection Prevalence : 0.3178
## Balanced Accuracy : 0.8885
##
## 'Positive' Class : No
## # Matriz de Confusión del Resultado de la Prueba
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$m1_purchase)
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 3 6
## Yes 6 11
##
## Accuracy : 0.5385
## 95% CI : (0.3337, 0.7341)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.9231
##
## Kappa : -0.0196
##
## Mcnemar's Test P-Value : 1.0000
##
## Sensitivity : 0.3333
## Specificity : 0.6471
## Pos Pred Value : 0.3333
## Neg Pred Value : 0.6471
## Prevalence : 0.3462
## Detection Rate : 0.1154
## Detection Prevalence : 0.3462
## Balanced Accuracy : 0.4902
##
## 'Positive' Class : No
## Modelo 1. SVM Lineal
modelo2 <- train(m1_purchase ~ ., data=entrenamiento,
method = "svmRadial",
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(sigma=1, C=1)
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainAgriculture, domainCommunication ,
## domainLogistics, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRealestate, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainConsulting , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainEconomics, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$m1_purchase)
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 34 0
## Yes 2 71
##
## Accuracy : 0.9813
## 95% CI : (0.9341, 0.9977)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9576
##
## Mcnemar's Test P-Value : 0.4795
##
## Sensitivity : 0.9444
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.9726
## Prevalence : 0.3364
## Detection Rate : 0.3178
## Detection Prevalence : 0.3178
## Balanced Accuracy : 0.9722
##
## 'Positive' Class : No
## # Matriz de Confusión del Resultado de la Prueba
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$m1_purchase)
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 0 0
## Yes 9 17
##
## Accuracy : 0.6538
## 95% CI : (0.4433, 0.8279)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.589398
##
## Kappa : 0
##
## Mcnemar's Test P-Value : 0.007661
##
## Sensitivity : 0.0000
## Specificity : 1.0000
## Pos Pred Value : NaN
## Neg Pred Value : 0.6538
## Prevalence : 0.3462
## Detection Rate : 0.0000
## Detection Prevalence : 0.0000
## Balanced Accuracy : 0.5000
##
## 'Positive' Class : No
## Modelo 3. SVM Polinómico
modelo3 <- train(m1_purchase ~ ., data=entrenamiento,
method = "svmPoly",
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(degree=1, scale=1, C=1)
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusSelf-Employed, statusStudent ant employed, statusUnemployed,
## domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainConsulting , domainRetail,
## domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainLaw,
## domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$m1_purchase)
mcre3## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 30 4
## Yes 6 67
##
## Accuracy : 0.9065
## 95% CI : (0.8348, 0.9543)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 4.281e-09
##
## Kappa : 0.7878
##
## Mcnemar's Test P-Value : 0.7518
##
## Sensitivity : 0.8333
## Specificity : 0.9437
## Pos Pred Value : 0.8824
## Neg Pred Value : 0.9178
## Prevalence : 0.3364
## Detection Rate : 0.2804
## Detection Prevalence : 0.3178
## Balanced Accuracy : 0.8885
##
## 'Positive' Class : No
## # Matriz de Confusión del Resultado de la Prueba
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$m1_purchase)
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 3 6
## Yes 6 11
##
## Accuracy : 0.5385
## 95% CI : (0.3337, 0.7341)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.9231
##
## Kappa : -0.0196
##
## Mcnemar's Test P-Value : 1.0000
##
## Sensitivity : 0.3333
## Specificity : 0.6471
## Pos Pred Value : 0.3333
## Neg Pred Value : 0.6471
## Prevalence : 0.3462
## Detection Rate : 0.1154
## Detection Prevalence : 0.3462
## Balanced Accuracy : 0.4902
##
## 'Positive' Class : No
## Modelo 4. Árbol de Decisión
modelo4 <- train(m1_purchase ~ ., data=entrenamiento,
method = "rpart",
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneLength = 10
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusStudent ant employed, statusUnemployed,
## domainCommunication , domainRealestate, domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainLaw,
## domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainConsulting , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetiredresultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$m1_purchase)
mcre4## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 17 2
## Yes 19 69
##
## Accuracy : 0.8037
## 95% CI : (0.7158, 0.8742)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 0.0010139
##
## Kappa : 0.5025
##
## Mcnemar's Test P-Value : 0.0004803
##
## Sensitivity : 0.4722
## Specificity : 0.9718
## Pos Pred Value : 0.8947
## Neg Pred Value : 0.7841
## Prevalence : 0.3364
## Detection Rate : 0.1589
## Detection Prevalence : 0.1776
## Balanced Accuracy : 0.7220
##
## 'Positive' Class : No
## # Matriz de Confusión del Resultado de la Prueba
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$m1_purchase)
mcrp4## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 4 6
## Yes 5 11
##
## Accuracy : 0.5769
## 95% CI : (0.3692, 0.7665)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.8485
##
## Kappa : 0.0892
##
## Mcnemar's Test P-Value : 1.0000
##
## Sensitivity : 0.4444
## Specificity : 0.6471
## Pos Pred Value : 0.4000
## Neg Pred Value : 0.6875
## Prevalence : 0.3462
## Detection Rate : 0.1538
## Detection Prevalence : 0.3846
## Balanced Accuracy : 0.5458
##
## 'Positive' Class : No
## Modelo 5. Redes Neuronales
modelo5 <- train(m1_purchase ~ ., data=entrenamiento,
method = "nnet",
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10)
)## Warning in preProcess.default(method = c("scale", "center"), x = structure(c(1,
## : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 82.794609
## iter 10 value 32.523223
## iter 20 value 30.173940
## iter 30 value 28.539522
## iter 40 value 28.506990
## iter 50 value 27.074299
## iter 60 value 26.908957
## iter 70 value 25.867242
## iter 80 value 25.188107
## iter 90 value 25.180191
## iter 100 value 25.175239
## final value 25.175239
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 97.677847
## iter 10 value 16.219975
## iter 20 value 10.653356
## iter 30 value 7.796083
## iter 40 value 7.291744
## iter 50 value 7.256140
## iter 60 value 6.798688
## iter 70 value 6.604047
## iter 80 value 6.596572
## iter 90 value 6.594610
## iter 100 value 6.592594
## final value 6.592594
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 72.744037
## iter 10 value 19.372157
## iter 20 value 9.636929
## iter 30 value 7.136274
## iter 40 value 3.528353
## iter 50 value 2.915231
## iter 60 value 2.797221
## iter 70 value 2.776372
## iter 80 value 2.774198
## iter 90 value 2.773608
## iter 100 value 2.772933
## final value 2.772933
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 70.289855
## iter 10 value 35.933317
## iter 20 value 24.558417
## iter 30 value 24.244017
## iter 40 value 24.242078
## final value 24.242074
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 77.281580
## iter 10 value 30.010986
## iter 20 value 22.050017
## iter 30 value 18.424384
## iter 40 value 17.604812
## iter 50 value 16.857607
## iter 60 value 16.355402
## iter 70 value 16.332377
## iter 80 value 16.331379
## final value 16.331356
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 69.945990
## iter 10 value 24.243650
## iter 20 value 17.522007
## iter 30 value 16.322947
## iter 40 value 15.878325
## iter 50 value 15.597082
## iter 60 value 15.488776
## iter 70 value 15.446262
## iter 80 value 15.410025
## iter 90 value 15.402042
## iter 100 value 15.393000
## final value 15.393000
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 62.360472
## iter 10 value 28.658205
## iter 20 value 19.201108
## iter 30 value 19.083328
## iter 40 value 19.079268
## iter 50 value 19.078384
## iter 60 value 19.077813
## iter 70 value 19.077231
## iter 80 value 19.076897
## iter 90 value 19.076728
## iter 100 value 19.076605
## final value 19.076605
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 77.117099
## iter 10 value 25.472374
## iter 20 value 9.149204
## iter 30 value 7.753878
## iter 40 value 7.655239
## iter 50 value 7.636452
## iter 60 value 7.621506
## iter 70 value 7.451727
## iter 80 value 6.311720
## iter 90 value 6.251173
## iter 100 value 6.243870
## final value 6.243870
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 68.029470
## iter 10 value 12.172986
## iter 20 value 3.473130
## iter 30 value 3.100124
## iter 40 value 3.028963
## iter 50 value 2.993067
## iter 60 value 2.963699
## iter 70 value 2.949382
## iter 80 value 2.930823
## iter 90 value 2.923014
## iter 100 value 2.917919
## final value 2.917919
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 59.368741
## iter 10 value 26.718303
## iter 20 value 20.584750
## iter 30 value 19.306762
## iter 40 value 19.190102
## iter 50 value 19.183632
## iter 60 value 19.181910
## iter 70 value 14.260203
## iter 80 value 14.224927
## iter 90 value 14.224845
## final value 14.224722
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 67.993695
## iter 10 value 16.383947
## iter 20 value 7.276646
## iter 30 value 6.923741
## iter 40 value 4.943834
## iter 50 value 4.932038
## iter 60 value 4.927091
## iter 70 value 2.518276
## iter 80 value 1.932994
## iter 90 value 1.921476
## iter 100 value 1.917878
## final value 1.917878
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 98.387219
## iter 10 value 11.476411
## iter 20 value 5.547852
## iter 30 value 3.232565
## iter 40 value 2.781108
## iter 50 value 2.774195
## iter 60 value 2.773354
## iter 70 value 2.772740
## iter 80 value 2.772324
## iter 90 value 1.909664
## iter 100 value 1.909560
## final value 1.909560
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 68.791180
## iter 10 value 35.028759
## iter 20 value 27.907264
## iter 30 value 22.762805
## iter 40 value 22.112099
## iter 50 value 22.098946
## iter 60 value 22.098422
## final value 22.098418
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 83.712837
## iter 10 value 26.716450
## iter 20 value 17.587663
## iter 30 value 15.233041
## iter 40 value 14.310126
## iter 50 value 14.241002
## iter 60 value 14.236339
## iter 70 value 14.236237
## iter 70 value 14.236237
## iter 70 value 14.236237
## final value 14.236237
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 69.979147
## iter 10 value 24.376136
## iter 20 value 15.363840
## iter 30 value 14.323596
## iter 40 value 14.214524
## iter 50 value 14.089252
## iter 60 value 14.051703
## iter 70 value 14.047668
## iter 80 value 14.047613
## iter 80 value 14.047613
## iter 80 value 14.047613
## final value 14.047613
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 60.434429
## iter 10 value 33.749684
## iter 20 value 30.752206
## iter 30 value 25.402654
## iter 40 value 24.769739
## iter 50 value 24.760702
## iter 60 value 20.670778
## iter 70 value 18.213444
## iter 80 value 18.179513
## iter 90 value 18.176381
## iter 100 value 18.174123
## final value 18.174123
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 73.425392
## iter 10 value 20.617829
## iter 20 value 15.789569
## iter 30 value 14.489948
## iter 40 value 10.895604
## iter 50 value 10.618544
## iter 60 value 10.370854
## iter 70 value 10.289755
## iter 80 value 10.281710
## iter 90 value 10.110416
## iter 100 value 9.918664
## final value 9.918664
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 87.891678
## iter 10 value 19.390635
## iter 20 value 8.607673
## iter 30 value 5.944232
## iter 40 value 4.918315
## iter 50 value 3.933743
## iter 60 value 2.246445
## iter 70 value 2.145542
## iter 80 value 2.132707
## iter 90 value 2.124702
## iter 100 value 2.112569
## final value 2.112569
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired## # weights: 50
## initial value 71.976216
## iter 10 value 33.342980
## iter 20 value 21.662463
## iter 30 value 14.639662
## iter 40 value 12.946586
## iter 50 value 12.922052
## iter 60 value 12.920325
## iter 70 value 12.919702
## iter 80 value 12.919511
## iter 90 value 12.919486
## final value 12.919483
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired## # weights: 148
## initial value 78.216282
## iter 10 value 17.900698
## iter 20 value 3.843636
## iter 30 value 2.784637
## iter 40 value 2.773070
## iter 50 value 2.772667
## iter 60 value 2.772608
## final value 2.772589
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired## # weights: 246
## initial value 70.573297
## iter 10 value 11.056969
## iter 20 value 6.188884
## iter 30 value 5.745323
## iter 40 value 4.681470
## iter 50 value 2.795438
## iter 60 value 2.780735
## iter 70 value 2.777014
## iter 80 value 2.776401
## iter 90 value 2.773795
## iter 100 value 2.773227
## final value 2.773227
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired## # weights: 50
## initial value 83.300123
## iter 10 value 30.687234
## iter 20 value 25.559378
## iter 30 value 24.516171
## iter 40 value 24.089590
## iter 50 value 23.950815
## iter 60 value 23.702450
## iter 70 value 23.700113
## final value 23.700110
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired## # weights: 148
## initial value 94.372604
## iter 10 value 34.330543
## iter 20 value 20.242339
## iter 30 value 16.575234
## iter 40 value 16.080500
## iter 50 value 15.835419
## iter 60 value 15.737704
## iter 70 value 15.726623
## iter 80 value 15.726062
## iter 90 value 15.725993
## final value 15.725993
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired## # weights: 246
## initial value 68.477806
## iter 10 value 23.228993
## iter 20 value 16.404908
## iter 30 value 15.539001
## iter 40 value 15.026944
## iter 50 value 14.868771
## iter 60 value 14.848713
## iter 70 value 14.845767
## iter 80 value 14.844733
## iter 90 value 14.840800
## iter 100 value 14.837916
## final value 14.837916
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired## # weights: 50
## initial value 62.649672
## iter 10 value 31.665932
## iter 20 value 21.798895
## iter 30 value 13.205426
## iter 40 value 12.985333
## iter 50 value 12.982972
## iter 60 value 12.982058
## iter 70 value 12.981439
## iter 80 value 12.981136
## iter 90 value 12.980976
## iter 100 value 12.980773
## final value 12.980773
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired## # weights: 148
## initial value 61.516738
## iter 10 value 12.573174
## iter 20 value 4.389903
## iter 30 value 4.319975
## iter 40 value 4.294086
## iter 50 value 4.278291
## iter 60 value 3.225050
## iter 70 value 2.972311
## iter 80 value 2.920447
## iter 90 value 2.910318
## iter 100 value 2.905371
## final value 2.905371
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired## # weights: 246
## initial value 100.181376
## iter 10 value 22.784907
## iter 20 value 13.965855
## iter 30 value 11.145884
## iter 40 value 9.989035
## iter 50 value 9.004827
## iter 60 value 8.324247
## iter 70 value 8.310037
## iter 80 value 8.294750
## iter 90 value 8.191976
## iter 100 value 5.229373
## final value 5.229373
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## # weights: 50
## initial value 79.954652
## iter 10 value 39.234535
## iter 20 value 33.514689
## iter 30 value 33.140681
## iter 40 value 31.229409
## iter 50 value 25.219234
## iter 60 value 22.877642
## iter 70 value 20.591738
## iter 80 value 20.484456
## iter 90 value 20.479297
## iter 100 value 20.477962
## final value 20.477962
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## # weights: 148
## initial value 61.873646
## iter 10 value 10.930614
## iter 20 value 8.159923
## iter 30 value 7.642354
## iter 40 value 7.553842
## iter 50 value 6.785688
## iter 60 value 6.164015
## iter 70 value 6.145143
## iter 80 value 6.140889
## iter 90 value 6.139390
## iter 100 value 6.139081
## final value 6.139081
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## # weights: 246
## initial value 64.815571
## iter 10 value 10.981524
## iter 20 value 4.404612
## iter 30 value 2.800178
## iter 40 value 2.774528
## iter 50 value 2.773007
## iter 60 value 2.772744
## iter 70 value 2.772651
## iter 80 value 2.772596
## iter 90 value 2.772592
## iter 90 value 2.772592
## iter 90 value 2.772592
## final value 2.772592
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## # weights: 50
## initial value 65.118354
## iter 10 value 32.386986
## iter 20 value 25.716508
## iter 30 value 24.256204
## iter 40 value 23.892339
## iter 50 value 23.878284
## final value 23.878274
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## # weights: 148
## initial value 87.342192
## iter 10 value 26.955697
## iter 20 value 18.385422
## iter 30 value 17.587733
## iter 40 value 17.315309
## iter 50 value 17.199786
## iter 60 value 17.190483
## final value 17.190432
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## # weights: 246
## initial value 68.367557
## iter 10 value 23.902082
## iter 20 value 16.361040
## iter 30 value 15.301320
## iter 40 value 15.083982
## iter 50 value 15.059599
## iter 60 value 15.015962
## iter 70 value 14.980847
## iter 80 value 14.976009
## iter 90 value 14.974998
## iter 100 value 14.974702
## final value 14.974702
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## # weights: 50
## initial value 74.913060
## iter 10 value 29.342125
## iter 20 value 25.213536
## iter 30 value 23.271096
## iter 40 value 23.219913
## iter 50 value 23.212860
## iter 60 value 23.211414
## iter 70 value 23.210637
## iter 80 value 23.210225
## iter 90 value 23.209769
## iter 100 value 23.209394
## final value 23.209394
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## # weights: 148
## initial value 66.808724
## iter 10 value 15.854533
## iter 20 value 8.820257
## iter 30 value 3.112124
## iter 40 value 2.999179
## iter 50 value 2.958810
## iter 60 value 2.934274
## iter 70 value 2.907263
## iter 80 value 2.898741
## iter 90 value 2.892244
## iter 100 value 2.887944
## final value 2.887944
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetail, domainRetired## # weights: 246
## initial value 72.989823
## iter 10 value 5.924902
## iter 20 value 3.024461
## iter 30 value 2.989294
## iter 40 value 2.956873
## iter 50 value 2.925959
## iter 60 value 2.908988
## iter 70 value 2.895211
## iter 80 value 2.888331
## iter 90 value 2.881497
## iter 100 value 2.873979
## final value 2.873979
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 73.194708
## iter 10 value 29.257803
## iter 20 value 15.121911
## iter 30 value 10.831618
## iter 40 value 10.266672
## iter 50 value 10.254020
## iter 60 value 10.253940
## iter 70 value 10.253927
## final value 10.253925
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 65.546884
## iter 10 value 19.968681
## iter 20 value 9.875934
## iter 30 value 9.435251
## iter 40 value 9.244243
## iter 50 value 8.796685
## iter 60 value 7.372868
## iter 70 value 7.316372
## iter 80 value 4.754440
## iter 90 value 4.693311
## iter 100 value 4.170914
## final value 4.170914
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 75.182816
## iter 10 value 12.219245
## iter 20 value 3.138752
## iter 30 value 2.799542
## iter 40 value 2.776662
## iter 50 value 2.773339
## iter 60 value 2.772814
## iter 70 value 2.772618
## iter 80 value 2.772596
## iter 90 value 2.772591
## final value 2.772590
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 80.664198
## iter 10 value 31.769665
## iter 20 value 22.930277
## iter 30 value 20.995536
## iter 40 value 20.389782
## iter 50 value 20.382084
## final value 20.382049
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 72.140767
## iter 10 value 22.597729
## iter 20 value 15.754747
## iter 30 value 15.506772
## iter 40 value 15.443794
## iter 50 value 15.380771
## iter 60 value 15.379478
## iter 70 value 15.379430
## iter 70 value 15.379429
## iter 70 value 15.379429
## final value 15.379429
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 71.957486
## iter 10 value 22.531119
## iter 20 value 15.276009
## iter 30 value 14.859896
## iter 40 value 14.764417
## iter 50 value 14.720292
## iter 60 value 14.662859
## iter 70 value 14.657139
## iter 80 value 14.653565
## iter 90 value 14.653481
## final value 14.653480
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 69.279274
## iter 10 value 31.048343
## iter 20 value 27.271239
## iter 30 value 24.725026
## iter 40 value 21.862000
## iter 50 value 21.454322
## iter 60 value 21.448891
## iter 70 value 21.445487
## iter 80 value 18.558477
## iter 90 value 16.061342
## iter 100 value 16.010145
## final value 16.010145
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 80.018636
## iter 10 value 18.522263
## iter 20 value 9.026920
## iter 30 value 8.280354
## iter 40 value 7.883689
## iter 50 value 5.662657
## iter 60 value 5.426435
## iter 70 value 5.404630
## iter 80 value 5.384877
## iter 90 value 5.378448
## iter 100 value 5.191033
## final value 5.191033
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 85.974871
## iter 10 value 16.707814
## iter 20 value 7.228730
## iter 30 value 6.636602
## iter 40 value 6.293116
## iter 50 value 6.031013
## iter 60 value 5.943857
## iter 70 value 5.644049
## iter 80 value 5.525917
## iter 90 value 5.399625
## iter 100 value 4.851789
## final value 4.851789
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired## # weights: 50
## initial value 61.553814
## iter 10 value 35.277615
## iter 20 value 28.063575
## iter 30 value 25.104741
## iter 40 value 21.311828
## iter 50 value 21.197852
## iter 60 value 21.162542
## iter 70 value 21.153516
## iter 80 value 21.150601
## iter 90 value 21.150536
## final value 21.150523
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired## # weights: 148
## initial value 63.384780
## iter 10 value 14.283585
## iter 20 value 8.256076
## iter 30 value 6.371471
## iter 40 value 4.589857
## iter 50 value 3.921047
## iter 60 value 3.357869
## iter 70 value 3.319939
## iter 80 value 3.303442
## iter 90 value 3.299578
## iter 100 value 3.296512
## final value 3.296512
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired## # weights: 246
## initial value 64.043275
## iter 10 value 6.237517
## iter 20 value 2.002794
## iter 30 value 1.910590
## iter 40 value 1.909592
## iter 50 value 1.909550
## iter 60 value 1.909544
## iter 70 value 1.909543
## final value 1.909543
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired## # weights: 50
## initial value 62.316059
## iter 10 value 38.944154
## iter 20 value 26.380743
## iter 30 value 19.315276
## iter 40 value 18.989410
## iter 50 value 18.975903
## final value 18.975881
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired## # weights: 148
## initial value 66.640341
## iter 10 value 32.172447
## iter 20 value 19.778969
## iter 30 value 16.687003
## iter 40 value 14.737146
## iter 50 value 14.302814
## iter 60 value 14.208910
## iter 70 value 14.156938
## iter 80 value 14.133909
## iter 90 value 14.133432
## iter 100 value 14.133425
## final value 14.133425
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired## # weights: 246
## initial value 67.239292
## iter 10 value 22.220849
## iter 20 value 16.128021
## iter 30 value 15.037465
## iter 40 value 14.273030
## iter 50 value 14.035413
## iter 60 value 13.792054
## iter 70 value 13.522224
## iter 80 value 13.430113
## iter 90 value 13.396012
## iter 100 value 13.392577
## final value 13.392577
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired## # weights: 50
## initial value 62.466453
## iter 10 value 26.924936
## iter 20 value 25.103263
## iter 30 value 23.251517
## iter 40 value 23.209114
## iter 50 value 21.231767
## iter 60 value 21.217169
## iter 70 value 21.214984
## iter 80 value 21.212140
## iter 90 value 21.209688
## iter 100 value 21.207537
## final value 21.207537
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired## # weights: 148
## initial value 74.410303
## iter 10 value 20.148540
## iter 20 value 10.450807
## iter 30 value 5.722416
## iter 40 value 4.708645
## iter 50 value 4.664552
## iter 60 value 4.653850
## iter 70 value 4.642256
## iter 80 value 4.199105
## iter 90 value 2.427519
## iter 100 value 2.414477
## final value 2.414477
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired## # weights: 246
## initial value 81.626665
## iter 10 value 8.208893
## iter 20 value 2.100247
## iter 30 value 2.046130
## iter 40 value 2.035333
## iter 50 value 2.026906
## iter 60 value 2.013949
## iter 70 value 2.002988
## iter 80 value 1.996243
## iter 90 value 1.991121
## iter 100 value 1.985333
## final value 1.985333
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainConsulting ,
## domainRetired## # weights: 50
## initial value 66.957991
## iter 10 value 33.795560
## iter 20 value 30.764744
## iter 30 value 30.726799
## iter 40 value 29.432519
## iter 50 value 29.411648
## iter 60 value 28.055067
## iter 70 value 28.050877
## iter 80 value 26.606075
## iter 90 value 26.596372
## iter 100 value 26.594993
## final value 26.594993
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainConsulting ,
## domainRetired## # weights: 148
## initial value 66.380510
## iter 10 value 23.583858
## iter 20 value 9.287468
## iter 30 value 6.828115
## iter 40 value 4.956410
## iter 50 value 4.805013
## iter 60 value 4.788865
## iter 70 value 4.783917
## iter 80 value 4.782095
## iter 90 value 4.781036
## iter 100 value 4.764955
## final value 4.764955
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainConsulting ,
## domainRetired## # weights: 246
## initial value 72.009673
## iter 10 value 13.199890
## iter 20 value 3.188530
## iter 30 value 1.947583
## iter 40 value 1.912963
## iter 50 value 1.909636
## final value 1.909543
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainConsulting ,
## domainRetired## # weights: 50
## initial value 67.284119
## iter 10 value 36.036373
## iter 20 value 26.175441
## iter 30 value 22.693643
## iter 40 value 22.145809
## final value 22.138071
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainConsulting ,
## domainRetired## # weights: 148
## initial value 68.078681
## iter 10 value 27.024005
## iter 20 value 18.966043
## iter 30 value 16.901176
## iter 40 value 16.127766
## iter 50 value 15.740388
## iter 60 value 15.650303
## iter 70 value 15.641672
## iter 80 value 15.639938
## iter 90 value 15.638782
## final value 15.638780
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainConsulting ,
## domainRetired## # weights: 246
## initial value 69.285061
## iter 10 value 23.914361
## iter 20 value 15.333893
## iter 30 value 14.895605
## iter 40 value 14.827519
## iter 50 value 14.803767
## iter 60 value 14.773113
## iter 70 value 14.749670
## iter 80 value 14.748566
## iter 90 value 14.746799
## iter 100 value 14.746031
## final value 14.746031
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainConsulting ,
## domainRetired## # weights: 50
## initial value 64.336021
## iter 10 value 31.507096
## iter 20 value 29.993624
## iter 30 value 28.245755
## iter 40 value 28.239712
## iter 50 value 28.231201
## iter 60 value 28.223967
## iter 70 value 25.213225
## iter 80 value 25.210617
## iter 90 value 25.208764
## iter 100 value 25.206148
## final value 25.206148
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainConsulting ,
## domainRetired## # weights: 148
## initial value 66.814703
## iter 10 value 22.546948
## iter 20 value 15.381218
## iter 30 value 14.501346
## iter 40 value 14.379715
## iter 50 value 14.082980
## iter 60 value 14.055293
## iter 70 value 13.962366
## iter 80 value 13.943572
## iter 90 value 13.622021
## iter 100 value 12.187155
## final value 12.187155
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainConsulting ,
## domainRetired## # weights: 246
## initial value 84.919110
## iter 10 value 28.033163
## iter 20 value 10.210805
## iter 30 value 8.570518
## iter 40 value 7.763227
## iter 50 value 7.295027
## iter 60 value 7.031426
## iter 70 value 7.000249
## iter 80 value 6.955851
## iter 90 value 6.644369
## iter 100 value 6.600102
## final value 6.600102
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 86.324573
## iter 10 value 26.232086
## iter 20 value 25.069007
## iter 30 value 25.051006
## iter 40 value 24.034646
## iter 50 value 23.407801
## iter 60 value 23.406463
## final value 23.405936
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 63.872883
## iter 10 value 23.261756
## iter 20 value 16.996061
## iter 30 value 16.630222
## iter 40 value 16.249935
## iter 50 value 15.287087
## iter 60 value 15.233224
## iter 70 value 14.997925
## iter 80 value 14.988528
## iter 90 value 14.984635
## iter 100 value 14.980263
## final value 14.980263
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 74.742942
## iter 10 value 20.371087
## iter 20 value 7.286435
## iter 30 value 6.102180
## iter 40 value 5.549045
## iter 50 value 4.714455
## iter 60 value 4.685760
## iter 70 value 4.683824
## iter 80 value 4.683360
## iter 90 value 4.683130
## iter 100 value 4.682918
## final value 4.682918
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 69.378173
## iter 10 value 32.177127
## iter 20 value 24.233473
## iter 30 value 22.246803
## iter 40 value 22.071438
## iter 50 value 21.751972
## iter 60 value 21.618247
## iter 70 value 21.617007
## iter 70 value 21.617007
## iter 70 value 21.617007
## final value 21.617007
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 73.071981
## iter 10 value 25.773608
## iter 20 value 17.298459
## iter 30 value 15.742513
## iter 40 value 15.155733
## iter 50 value 15.001713
## iter 60 value 14.961105
## iter 70 value 14.949065
## iter 80 value 14.948699
## final value 14.948682
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 80.762229
## iter 10 value 24.417215
## iter 20 value 15.634746
## iter 30 value 14.536364
## iter 40 value 14.274673
## iter 50 value 14.238511
## iter 60 value 14.235964
## iter 70 value 14.235910
## iter 80 value 14.235907
## final value 14.235907
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 50
## initial value 73.433304
## iter 10 value 37.555125
## iter 20 value 20.700075
## iter 30 value 19.804623
## iter 40 value 19.795099
## iter 50 value 19.791943
## iter 60 value 19.788699
## iter 70 value 17.771500
## iter 80 value 17.715991
## iter 90 value 17.709412
## iter 100 value 15.475401
## final value 15.475401
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 73.677626
## iter 10 value 35.760074
## iter 20 value 22.990582
## iter 30 value 19.533515
## iter 40 value 17.992623
## iter 50 value 17.340637
## iter 60 value 16.571211
## iter 70 value 15.818469
## iter 80 value 15.806080
## iter 90 value 13.259448
## iter 100 value 12.401802
## final value 12.401802
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 246
## initial value 78.297731
## iter 10 value 15.539093
## iter 20 value 11.334613
## iter 30 value 11.055371
## iter 40 value 10.022467
## iter 50 value 9.500383
## iter 60 value 9.291604
## iter 70 value 9.179569
## iter 80 value 8.796615
## iter 90 value 8.760201
## iter 100 value 8.739276
## final value 8.739276
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## # weights: 50
## initial value 64.529412
## iter 10 value 25.651713
## iter 20 value 19.715909
## iter 30 value 16.664665
## iter 40 value 16.429905
## iter 50 value 16.398188
## iter 60 value 16.387949
## iter 70 value 16.382131
## iter 80 value 16.381226
## final value 16.381224
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## # weights: 148
## initial value 65.266712
## iter 10 value 23.215554
## iter 20 value 11.933435
## iter 30 value 4.208241
## iter 40 value 3.827260
## iter 50 value 2.838205
## iter 60 value 2.793879
## iter 70 value 2.780570
## iter 80 value 2.775860
## iter 90 value 2.774085
## iter 100 value 2.773028
## final value 2.773028
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## # weights: 246
## initial value 66.556822
## iter 10 value 11.482455
## iter 20 value 4.917838
## iter 30 value 4.702307
## iter 40 value 4.682600
## iter 50 value 4.682265
## iter 60 value 4.682131
## iter 60 value 4.682131
## iter 60 value 4.682131
## final value 4.682131
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## # weights: 50
## initial value 71.696143
## iter 10 value 27.939852
## iter 20 value 20.937513
## iter 30 value 20.541111
## iter 40 value 20.530253
## iter 40 value 20.530253
## iter 40 value 20.530253
## final value 20.530253
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## # weights: 148
## initial value 67.447831
## iter 10 value 22.730246
## iter 20 value 16.069012
## iter 30 value 14.975225
## iter 40 value 14.297662
## iter 50 value 14.254544
## iter 60 value 14.248011
## iter 70 value 14.247734
## iter 70 value 14.247734
## iter 70 value 14.247734
## final value 14.247734
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## # weights: 246
## initial value 65.335582
## iter 10 value 21.447596
## iter 20 value 15.454211
## iter 30 value 14.477365
## iter 40 value 13.873263
## iter 50 value 13.708738
## iter 60 value 13.665483
## iter 70 value 13.646620
## iter 80 value 13.643768
## iter 90 value 13.643436
## final value 13.643432
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## # weights: 50
## initial value 73.072488
## iter 10 value 29.805494
## iter 20 value 23.816631
## iter 30 value 21.213655
## iter 40 value 21.196828
## iter 50 value 21.193898
## iter 60 value 21.192865
## iter 70 value 21.056069
## iter 80 value 14.713420
## iter 90 value 14.705609
## iter 100 value 14.702258
## final value 14.702258
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## # weights: 148
## initial value 59.887673
## iter 10 value 25.606206
## iter 20 value 11.845724
## iter 30 value 10.400596
## iter 40 value 10.375751
## iter 50 value 10.312334
## iter 60 value 10.237429
## iter 70 value 10.196505
## iter 80 value 10.012968
## iter 90 value 9.808608
## iter 100 value 9.657355
## final value 9.657355
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLogistics, domainRetired## # weights: 246
## initial value 74.319562
## iter 10 value 10.168484
## iter 20 value 5.511297
## iter 30 value 5.402116
## iter 40 value 4.905955
## iter 50 value 4.340351
## iter 60 value 4.298331
## iter 70 value 4.291601
## iter 80 value 4.280951
## iter 90 value 4.260944
## iter 100 value 4.193815
## final value 4.193815
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainRetired## # weights: 50
## initial value 66.015095
## iter 10 value 31.097786
## iter 20 value 29.604179
## iter 30 value 27.080306
## iter 40 value 27.036491
## iter 50 value 24.287027
## iter 60 value 21.336392
## iter 70 value 21.156247
## iter 80 value 21.154844
## iter 90 value 21.153388
## iter 100 value 21.151713
## final value 21.151713
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainRetired## # weights: 148
## initial value 63.610273
## iter 10 value 24.104075
## iter 20 value 8.640125
## iter 30 value 2.981083
## iter 40 value 2.775571
## iter 50 value 2.772885
## iter 60 value 2.772731
## iter 70 value 2.772644
## iter 80 value 2.772633
## iter 90 value 2.772597
## iter 100 value 2.772590
## final value 2.772590
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainRetired## # weights: 246
## initial value 63.137518
## iter 10 value 21.063098
## iter 20 value 10.576914
## iter 30 value 9.067025
## iter 40 value 6.438300
## iter 50 value 5.072279
## iter 60 value 4.555081
## iter 70 value 2.797958
## iter 80 value 2.779677
## iter 90 value 2.777176
## iter 100 value 2.775329
## final value 2.775329
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainRetired## # weights: 50
## initial value 61.787783
## iter 10 value 28.573552
## iter 20 value 24.511573
## iter 30 value 24.033795
## iter 40 value 24.004832
## iter 50 value 24.004664
## final value 24.004663
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainRetired## # weights: 148
## initial value 73.072023
## iter 10 value 34.321151
## iter 20 value 19.607188
## iter 30 value 16.057632
## iter 40 value 15.221674
## iter 50 value 15.122504
## iter 60 value 15.094168
## iter 70 value 15.091944
## iter 80 value 15.091887
## final value 15.091884
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainRetired## # weights: 246
## initial value 76.151706
## iter 10 value 21.580490
## iter 20 value 15.677001
## iter 30 value 14.810781
## iter 40 value 14.661626
## iter 50 value 14.645167
## iter 60 value 14.643259
## iter 70 value 14.642952
## iter 80 value 14.642876
## iter 80 value 14.642876
## iter 80 value 14.642876
## final value 14.642876
## converged## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainRetired## # weights: 50
## initial value 91.328835
## iter 10 value 35.412042
## iter 20 value 24.634171
## iter 30 value 15.730665
## iter 40 value 15.335339
## iter 50 value 15.329432
## iter 60 value 15.324485
## iter 70 value 15.321931
## iter 80 value 15.319288
## iter 90 value 15.316559
## iter 100 value 15.315422
## final value 15.315422
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainRetired## # weights: 148
## initial value 60.051624
## iter 10 value 20.361561
## iter 20 value 11.136947
## iter 30 value 9.101344
## iter 40 value 8.572187
## iter 50 value 8.094703
## iter 60 value 8.075152
## iter 70 value 7.382035
## iter 80 value 7.352125
## iter 90 value 6.236481
## iter 100 value 3.028709
## final value 3.028709
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainRetired## # weights: 246
## initial value 73.261698
## iter 10 value 13.314776
## iter 20 value 7.553811
## iter 30 value 7.178838
## iter 40 value 7.133394
## iter 50 value 7.093329
## iter 60 value 7.061353
## iter 70 value 7.039266
## iter 80 value 7.029383
## iter 90 value 6.462540
## iter 100 value 5.126840
## final value 5.126840
## stopped after 100 iterations## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## # weights: 148
## initial value 95.467947
## iter 10 value 29.352737
## iter 20 value 18.658725
## iter 30 value 17.792349
## iter 40 value 17.742214
## iter 50 value 17.738211
## iter 60 value 17.737670
## final value 17.737641
## convergedresultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$m1_purchase)
mcre5## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 32 2
## Yes 4 69
##
## Accuracy : 0.9439
## 95% CI : (0.8819, 0.9791)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 3.021e-12
##
## Kappa : 0.8727
##
## Mcnemar's Test P-Value : 0.6831
##
## Sensitivity : 0.8889
## Specificity : 0.9718
## Pos Pred Value : 0.9412
## Neg Pred Value : 0.9452
## Prevalence : 0.3364
## Detection Rate : 0.2991
## Detection Prevalence : 0.3178
## Balanced Accuracy : 0.9304
##
## 'Positive' Class : No
## # Matriz de Confusión del Resultado de la Prueba
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$m1_purchase)
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 4 6
## Yes 5 11
##
## Accuracy : 0.5769
## 95% CI : (0.3692, 0.7665)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.8485
##
## Kappa : 0.0892
##
## Mcnemar's Test P-Value : 1.0000
##
## Sensitivity : 0.4444
## Specificity : 0.6471
## Pos Pred Value : 0.4000
## Neg Pred Value : 0.6875
## Prevalence : 0.3462
## Detection Rate : 0.1538
## Detection Prevalence : 0.3846
## Balanced Accuracy : 0.5458
##
## 'Positive' Class : No
## Modelo 6. Bosques Aleatorios
modelo6 <- train(m1_purchase ~ ., data=entrenamiento,
method = "rf",
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = expand.grid(mtry = c(2,4,6))
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusStudent ant employed, statusUnemployed, domainCommunication ,
## domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainAgriculture, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## statusRetired, statusUnemployed, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainConsulting , domainRetail,
## domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainConsulting , domainRetail,
## domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainConsulting , domainRetail,
## domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainLogistics,
## domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainLogistics,
## domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainLaw, domainLogistics,
## domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetiredresultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)
# Matriz de Confusión del Resultado del Entrenamiento
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$m1_purchase)
mcre6## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 34 0
## Yes 2 71
##
## Accuracy : 0.9813
## 95% CI : (0.9341, 0.9977)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9576
##
## Mcnemar's Test P-Value : 0.4795
##
## Sensitivity : 0.9444
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.9726
## Prevalence : 0.3364
## Detection Rate : 0.3178
## Detection Prevalence : 0.3178
## Balanced Accuracy : 0.9722
##
## 'Positive' Class : No
## # Matriz de Confusión del Resultado de la Prueba
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$m1_purchase)
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 4 3
## Yes 5 14
##
## Accuracy : 0.6923
## 95% CI : (0.4821, 0.8567)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.4267
##
## Kappa : 0.2828
##
## Mcnemar's Test P-Value : 0.7237
##
## Sensitivity : 0.4444
## Specificity : 0.8235
## Pos Pred Value : 0.5714
## Neg Pred Value : 0.7368
## Prevalence : 0.3462
## Detection Rate : 0.1538
## Detection Prevalence : 0.2692
## Balanced Accuracy : 0.6340
##
## 'Positive' Class : No
## Tabla de Resultados
resultados <- data.frame(
"svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
"svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"rf" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisión de entrenamiento", "Precisión de prueba")
resultados## svmLinear svmRadial svmPoly rpart nnet
## Precisión de entrenamiento 0.9065421 0.9813084 0.9065421 0.8037383 0.9439252
## Precisión de prueba 0.5384615 0.6538462 0.5384615 0.5769231 0.5769231
## rf
## Precisión de entrenamiento 0.9813084
## Precisión de prueba 0.6923077Conclusiones
A partir de la tabla anterior, podemos observar el desempeño de los distintos modelos de Machine Learning evaluando quién predeciría mejor si un cliente realizará la compra de una Mac M1. El mejor modelo es el que presenta la “Precisión de prueba” (Accuracy en el conjunto de prueba) más alta sin tener overfitting.
---
title: "M1"
author: "Mariana Garibay - A00839593"
date: "27/02/2026"
output: 
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
    theme: flatly
---
<center>
![](https://predikdata.com/es/wp-content/uploads/sites/2/2021/07/modelos-predictivos-series-de-tiempo.jpg)
</center>

# <span style="color: blue"> Teoría </span>
El paquete **CARET (Classification And REgression Training)** es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático.

# <span style="color: blue"> Instalar paquetes y llamar librerías </span>
```{r message=FALSE, warning=FALSE}
# install.packages("ggplot2") # Gráficas
library(ggplot2)
# install.packages("lattice") # Crear gráficos
library(lattice)
# install.packages("caret") # Algoritmos de aprendizaje automático
library(caret)
# install.packages("DataExplorer") # Análisis Exploratorio
library(DataExplorer)
```

# <span style="color: blue"> Crear la base de datos </span>
```{r message=FALSE, warning=FALSE}
# Leemos el archivo CSV. 
# stringsAsFactors = TRUE convierte automáticamente los textos (como "Yes", "No", "Male", "Female") en factores.
df <- read.csv("C:\\Users\\maria\\OneDrive\\Desktop\\TEC SEMESTRE 6\\M1_data.csv", stringsAsFactors = TRUE)

# Aseguramos que nuestra variable predictora (Target) sea explícitamente un factor
df$m1_purchase <- as.factor(df$m1_purchase)

# Eliminamos cualquier valor nulo por seguridad
df <- na.omit(df)
```

# <span style="color: blue"> Entender la base de datos </span>
```{r}
summary(df)
str(df)
# create_report(df)
plot_missing(df)
# plot_histogram(df)
# plot_correlation(df)
```

# <span style="color: blue"> Partir la base de datos </span>
```{r}
# Normalmente 80-20
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$m1_purchase, p=0.8, list=FALSE)
entrenamiento <- df[renglones_entrenamiento, ]
prueba <- df[-renglones_entrenamiento, ]
```

# <span style="color: blue"> Distintos tipos de Métodos para Modelar </span>
```{r}
modelo1 <- train(m1_purchase ~ ., data=entrenamiento,
                 method = "svmLinear", 
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10),
                 tuneGrid = data.frame(C=1) 
)
resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)

# Matriz de Confusión del Resultado del Entrenamiento
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$m1_purchase)
mcre1

# Matriz de Confusión del Resultado de la Prueba
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$m1_purchase)
mcrp1
```

# <span style="color: blue"> Modelo 1. SVM Lineal </span>
```{r}
modelo2 <- train(m1_purchase ~ ., data=entrenamiento,
                 method = "svmRadial", 
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10),
                 tuneGrid = data.frame(sigma=1, C=1) 
)
resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)

# Matriz de Confusión del Resultado del Entrenamiento
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$m1_purchase)
mcre2

# Matriz de Confusión del Resultado de la Prueba
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$m1_purchase)
mcrp2
```

# <span style="color: blue"> Modelo 3. SVM Polinómico </span>
```{r}
modelo3 <- train(m1_purchase ~ ., data=entrenamiento,
                 method = "svmPoly", 
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10),
                 tuneGrid = data.frame(degree=1, scale=1, C=1) 
)
resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)

# Matriz de Confusión del Resultado del Entrenamiento
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$m1_purchase)
mcre3

# Matriz de Confusión del Resultado de la Prueba
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$m1_purchase)
mcrp3
```

# <span style="color: blue"> Modelo 4. Árbol de Decisión </span>
```{r}
modelo4 <- train(m1_purchase ~ ., data=entrenamiento,
                 method = "rpart", 
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10),
                 tuneLength = 10 
)
resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)

# Matriz de Confusión del Resultado del Entrenamiento
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$m1_purchase)
mcre4

# Matriz de Confusión del Resultado de la Prueba
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$m1_purchase)
mcrp4
```

# <span style="color: blue"> Modelo 5. Redes Neuronales </span>
```{r}
modelo5 <- train(m1_purchase ~ ., data=entrenamiento,
                 method = "nnet", 
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10)
)
```

```{r}
resultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)

# Matriz de Confusión del Resultado del Entrenamiento
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$m1_purchase)
mcre5

# Matriz de Confusión del Resultado de la Prueba
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$m1_purchase)
mcrp5
```

# <span style="color: blue"> Modelo 6. Bosques Aleatorios </span>
```{r}
modelo6 <- train(m1_purchase ~ ., data=entrenamiento,
                 method = "rf", 
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10),
                 tuneGrid = expand.grid(mtry = c(2,4,6)) 
)
resultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)

# Matriz de Confusión del Resultado del Entrenamiento
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$m1_purchase)
mcre6

# Matriz de Confusión del Resultado de la Prueba
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$m1_purchase)
mcrp6
```

# <span style="color: blue"> Tabla de Resultados </span>
```{r}
resultados <- data.frame(
  "svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
  "svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
  "svmPoly"   = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
  "rpart"     = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
  "nnet"      = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
  "rf"        = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisión de entrenamiento", "Precisión de prueba")
resultados
```

# <span style="color: blue"> Conclusiones </span>

A partir de la tabla anterior, podemos observar el desempeño de los distintos modelos de Machine Learning evaluando quién predeciría mejor si un cliente realizará la compra de una Mac M1. El mejor modelo es el que presenta la "Precisión de prueba" (Accuracy en el conjunto de prueba) más alta sin tener overfitting.





