SVM M1
Ricardo Canive Valencia A01236496
2026-03-02
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)## Warning: package 'ggplot2' was built under R version 4.5.2#install.packages("lattice") # Crear gráficos
library(lattice)## Warning: package 'lattice' was built under R version 4.5.2#install.packages("caret") # Algoritmos de aprendizaje automático
library(caret)## Warning: package 'caret' was built under R version 4.5.2#install.packages("DataExplorer") # Análisis Exploratorio
library(DataExplorer)## Warning: package 'DataExplorer' was built under R version 4.5.2Crear la base de datos
df <- read.csv("C:\\Users\\sams\\Downloads\\M1_data.csv")
# Convertir columnas de texto a factor
# NOTA: La variable que queremos predecir debe tener formato de FACTOR
df$trust_apple <- as.factor(df$trust_apple)
df$user_pcmac <- as.factor(df$user_pcmac)
df$familiarity_m1 <- as.factor(df$familiarity_m1)
df$m1_consideration <- as.factor(df$m1_consideration)
df$m1_purchase <- as.factor(df$m1_purchase)
df$gender <- as.factor(df$gender)
df$status <- as.factor(df$status)
df$domain <- as.factor(df$domain)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 1:10 No :45 Female:61 Min. : 1.00
## 1st Qu.:3.000 2:15 Yes:88 Male :72 1st Qu.: 2.00
## Median :4.000 3:33 Median : 2.00
## Mean :3.376 4:34 Mean : 2.97
## 3rd Qu.:4.000 5:41 3rd Qu.: 3.00
## 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 : Factor w/ 5 levels "1","2","3","4",..: 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 every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
plot_histogram(df)
plot_correlation(df)## 1 features with more than 20 categories ignored!
## domain: 22 categories
NOTA: La variable que queremos predecir debe tener formato de
FACTOR # 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
Los métodos más utilizados para modelar aprendizaje automático son: * SVM: Support Vector Machine o Máquina de Vectores de Soporte. Hay varios subtipos: Lineal (svmLinear), Radial (svmRadial), Polinómico (svmPoly), etc. * Árbol de Decisión: rpart * Redes Neuronales: nnet * Random Forest o Bosques Aleatorios: rf # Modelo 1. SVM Lineal
modelo1 <- train(m1_purchase ~ ., data=entrenamiento,
method = "svmLinear", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(C=1) #Cambiar
)## 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)
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$m1_purchase)
mcre1## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 33 4
## Yes 3 67
##
## Accuracy : 0.9346
## 95% CI : (0.8698, 0.9733)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 2.262e-11
##
## Kappa : 0.8545
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 0.9167
## Specificity : 0.9437
## Pos Pred Value : 0.8919
## Neg Pred Value : 0.9571
## Prevalence : 0.3364
## Detection Rate : 0.3084
## Detection Prevalence : 0.3458
## Balanced Accuracy : 0.9302
##
## 'Positive' Class : No
## mcrp1 <- confusionMatrix(resultado_prueba1, prueba$m1_purchase)
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 4 7
## Yes 5 10
##
## Accuracy : 0.5385
## 95% CI : (0.3337, 0.7341)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.9231
##
## Kappa : 0.0311
##
## Mcnemar's Test P-Value : 0.7728
##
## Sensitivity : 0.4444
## Specificity : 0.5882
## Pos Pred Value : 0.3636
## Neg Pred Value : 0.6667
## Prevalence : 0.3462
## Detection Rate : 0.1538
## Detection Prevalence : 0.4231
## Balanced Accuracy : 0.5163
##
## 'Positive' Class : No
## Modelo 2. SVM Radial
modelo2 <- train(m1_purchase ~ ., data=entrenamiento,
method = "svmRadial", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(sigma=1, C=1) #Cambiar
)## 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 .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_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_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_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 , 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 , 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 , 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 , 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 , 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 , 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,
## 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,
## 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 , 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 , 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 , 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 , 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)
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$m1_purchase)
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 26 2
## Yes 10 69
##
## Accuracy : 0.8879
## 95% CI : (0.8123, 0.9407)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 8.209e-08
##
## Kappa : 0.7343
##
## Mcnemar's Test P-Value : 0.04331
##
## Sensitivity : 0.7222
## Specificity : 0.9718
## Pos Pred Value : 0.9286
## Neg Pred Value : 0.8734
## Prevalence : 0.3364
## Detection Rate : 0.2430
## Detection Prevalence : 0.2617
## Balanced Accuracy : 0.8470
##
## 'Positive' Class : No
## mcrp2 <- confusionMatrix(resultado_prueba2, prueba$m1_purchase)
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 2 4
## Yes 7 13
##
## Accuracy : 0.5769
## 95% CI : (0.3692, 0.7665)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.8485
##
## Kappa : -0.0142
##
## Mcnemar's Test P-Value : 0.5465
##
## Sensitivity : 0.22222
## Specificity : 0.76471
## Pos Pred Value : 0.33333
## Neg Pred Value : 0.65000
## Prevalence : 0.34615
## Detection Rate : 0.07692
## Detection Prevalence : 0.23077
## Balanced Accuracy : 0.49346
##
## 'Positive' Class : No
## Modelo 3. SVM Polinómico
modelo3 <- train(m1_purchase ~ ., data=entrenamiento,
method = "svmPoly", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(degree=1, scale=1, C=1) #Cambiar
)## 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_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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_pcmacOther, statusRetired,
## 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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , domainLaw, 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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 , 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)
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$m1_purchase)
mcre3## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 28 5
## Yes 8 66
##
## Accuracy : 0.8785
## 95% CI : (0.8012, 0.9337)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 3.134e-07
##
## Kappa : 0.7222
##
## Mcnemar's Test P-Value : 0.5791
##
## Sensitivity : 0.7778
## Specificity : 0.9296
## Pos Pred Value : 0.8485
## Neg Pred Value : 0.8919
## Prevalence : 0.3364
## Detection Rate : 0.2617
## Detection Prevalence : 0.3084
## Balanced Accuracy : 0.8537
##
## 'Positive' Class : No
## mcrp3 <- confusionMatrix(resultado_prueba3, prueba$m1_purchase)
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 4 7
## Yes 5 10
##
## Accuracy : 0.5385
## 95% CI : (0.3337, 0.7341)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.9231
##
## Kappa : 0.0311
##
## Mcnemar's Test P-Value : 0.7728
##
## Sensitivity : 0.4444
## Specificity : 0.5882
## Pos Pred Value : 0.3636
## Neg Pred Value : 0.6667
## Prevalence : 0.3462
## Detection Rate : 0.1538
## Detection Prevalence : 0.4231
## Balanced Accuracy : 0.5163
##
## 'Positive' Class : No
## Modelo 4. Árbol de Decisión
modelo4 <- train(m1_purchase ~ ., data=entrenamiento,
method = "rpart", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneLength = 10 #Cambiar
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRealestate, 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, statusUnemployed, 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, 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 , 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,
## statusStudent ant employed, 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 , 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)
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
## 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", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10)
#Cambiar
)## # weights: 53
## initial value 69.862622
## iter 10 value 37.803730
## iter 20 value 35.545836
## iter 30 value 35.543455
## iter 40 value 35.542602
## iter 50 value 35.542046
## iter 60 value 35.539749
## iter 70 value 32.912314
## iter 80 value 32.876199
## iter 90 value 32.875143
## iter 100 value 31.449407
## final value 31.449407
## stopped after 100 iterations## # weights: 157
## initial value 79.246265
## iter 10 value 8.452983
## iter 20 value 4.823318
## iter 30 value 4.781108
## iter 40 value 4.780377
## final value 4.780357
## converged## # weights: 261
## initial value 66.004496
## iter 10 value 18.971590
## iter 20 value 7.480696
## iter 30 value 4.968881
## iter 40 value 4.618943
## iter 50 value 2.822858
## iter 60 value 2.784046
## iter 70 value 2.774633
## iter 80 value 2.773429
## iter 90 value 2.772963
## iter 100 value 2.772778
## final value 2.772778
## stopped after 100 iterations## # weights: 53
## initial value 82.684334
## iter 10 value 47.348764
## iter 20 value 38.147368
## iter 30 value 30.289173
## iter 40 value 24.950882
## iter 50 value 23.536765
## iter 60 value 23.005965
## iter 70 value 22.944098
## final value 22.944021
## converged## # weights: 157
## initial value 89.827315
## iter 10 value 29.601078
## iter 20 value 16.960614
## iter 30 value 16.056531
## iter 40 value 15.861716
## iter 50 value 15.819874
## iter 60 value 15.814456
## iter 70 value 15.812814
## iter 80 value 15.812623
## final value 15.812616
## converged## # weights: 261
## initial value 65.723030
## iter 10 value 21.898658
## iter 20 value 15.424914
## iter 30 value 14.671095
## iter 40 value 14.535925
## iter 50 value 14.499040
## iter 60 value 14.398657
## iter 70 value 14.270857
## iter 80 value 14.267179
## final value 14.267127
## converged## # weights: 53
## initial value 91.815911
## iter 10 value 44.966684
## iter 20 value 26.278428
## iter 30 value 16.924140
## iter 40 value 16.570329
## iter 50 value 16.549997
## iter 60 value 16.542125
## iter 70 value 16.539691
## iter 80 value 16.537246
## iter 90 value 16.534884
## iter 100 value 16.533782
## final value 16.533782
## stopped after 100 iterations## # weights: 157
## initial value 60.407019
## iter 10 value 22.964018
## iter 20 value 9.906558
## iter 30 value 8.173630
## iter 40 value 6.763646
## iter 50 value 3.724141
## iter 60 value 3.046121
## iter 70 value 2.963973
## iter 80 value 2.956029
## iter 90 value 2.950768
## iter 100 value 2.940767
## final value 2.940767
## stopped after 100 iterations## # weights: 261
## initial value 82.110476
## iter 10 value 27.396733
## iter 20 value 7.074251
## iter 30 value 3.720853
## iter 40 value 3.045025
## iter 50 value 2.998582
## iter 60 value 2.973388
## iter 70 value 2.959466
## iter 80 value 2.942489
## iter 90 value 2.927573
## iter 100 value 2.910025
## final value 2.910025
## stopped after 100 iterations## # weights: 53
## initial value 61.763357
## iter 10 value 30.065263
## iter 20 value 27.086157
## iter 30 value 26.699065
## iter 40 value 26.685931
## iter 50 value 26.681518
## iter 60 value 26.681390
## iter 70 value 26.681255
## final value 26.681221
## converged## # weights: 157
## initial value 61.770719
## iter 10 value 24.259901
## iter 20 value 16.856376
## iter 30 value 12.991659
## iter 40 value 11.986577
## iter 50 value 11.916230
## iter 60 value 11.634676
## iter 70 value 11.051374
## iter 80 value 10.925983
## iter 90 value 10.925041
## iter 100 value 10.924210
## final value 10.924210
## stopped after 100 iterations## # weights: 261
## initial value 63.000680
## iter 10 value 15.132696
## iter 20 value 9.467275
## iter 30 value 9.070733
## iter 40 value 8.839593
## iter 50 value 8.775592
## iter 60 value 8.522434
## iter 70 value 8.204194
## iter 80 value 7.995326
## iter 90 value 7.989563
## iter 100 value 7.989042
## final value 7.989042
## stopped after 100 iterations## # weights: 53
## initial value 78.268467
## iter 10 value 36.368519
## iter 20 value 33.161832
## iter 30 value 31.261387
## iter 40 value 29.932846
## iter 50 value 27.626855
## iter 60 value 23.167870
## iter 70 value 22.902149
## iter 80 value 22.901419
## iter 80 value 22.901419
## iter 80 value 22.901419
## final value 22.901419
## converged## # weights: 157
## initial value 66.290225
## iter 10 value 28.700155
## iter 20 value 18.863529
## iter 30 value 16.554546
## iter 40 value 16.172931
## iter 50 value 16.101122
## iter 60 value 16.083023
## iter 70 value 16.079448
## iter 80 value 16.064312
## final value 16.064117
## converged## # weights: 261
## initial value 77.584385
## iter 10 value 24.331159
## iter 20 value 17.700885
## iter 30 value 16.081648
## iter 40 value 15.131740
## iter 50 value 15.016241
## iter 60 value 15.013555
## iter 70 value 15.013524
## final value 15.013523
## converged## # weights: 53
## initial value 69.492493
## iter 10 value 34.496450
## iter 20 value 16.606209
## iter 30 value 12.599845
## iter 40 value 12.375604
## iter 50 value 12.370861
## iter 60 value 12.367114
## iter 70 value 12.364187
## iter 80 value 12.362470
## iter 90 value 12.361713
## iter 100 value 12.361107
## final value 12.361107
## stopped after 100 iterations## # weights: 157
## initial value 67.756280
## iter 10 value 16.846605
## iter 20 value 10.142887
## iter 30 value 9.695107
## iter 40 value 9.680182
## iter 50 value 9.402382
## iter 60 value 9.357320
## iter 70 value 9.348530
## iter 80 value 9.334285
## iter 90 value 9.011009
## iter 100 value 8.907797
## final value 8.907797
## stopped after 100 iterations## # weights: 261
## initial value 64.745346
## iter 10 value 14.288357
## iter 20 value 5.791844
## iter 30 value 5.691505
## iter 40 value 3.155618
## iter 50 value 3.044073
## iter 60 value 3.012320
## iter 70 value 2.999319
## iter 80 value 2.986891
## iter 90 value 2.970262
## iter 100 value 2.955535
## final value 2.955535
## stopped after 100 iterations## # weights: 53
## initial value 74.068474
## iter 10 value 39.071990
## iter 20 value 31.193701
## iter 30 value 31.179465
## iter 40 value 31.177062
## iter 50 value 31.176588
## iter 60 value 31.176371
## iter 70 value 31.176232
## final value 31.176108
## converged## # weights: 157
## initial value 66.314658
## iter 10 value 23.742537
## iter 20 value 12.977913
## iter 30 value 11.654001
## iter 40 value 7.241564
## iter 50 value 6.268485
## iter 60 value 6.234133
## iter 70 value 6.156128
## iter 80 value 5.921484
## iter 90 value 3.048187
## iter 100 value 2.787289
## final value 2.787289
## stopped after 100 iterations## # weights: 261
## initial value 64.871318
## iter 10 value 10.862474
## iter 20 value 8.851449
## iter 30 value 8.461939
## iter 40 value 8.444915
## iter 50 value 8.382448
## iter 60 value 7.502376
## iter 70 value 6.518418
## iter 80 value 6.480205
## iter 90 value 6.448040
## iter 100 value 6.300398
## final value 6.300398
## stopped after 100 iterations## # weights: 53
## initial value 67.292322
## iter 10 value 34.882258
## iter 20 value 24.318660
## iter 30 value 22.144389
## iter 40 value 22.140223
## final value 22.140204
## converged## # weights: 157
## initial value 77.934690
## iter 10 value 24.072973
## iter 20 value 17.777152
## iter 30 value 17.334070
## iter 40 value 17.230694
## iter 50 value 17.226353
## iter 60 value 17.226211
## final value 17.226209
## converged## # weights: 261
## initial value 95.903841
## iter 10 value 25.691369
## iter 20 value 15.524434
## iter 30 value 14.404912
## iter 40 value 14.132610
## iter 50 value 14.100657
## iter 60 value 14.093661
## iter 70 value 14.071058
## iter 80 value 14.070374
## final value 14.070372
## converged## # weights: 53
## initial value 78.124854
## iter 10 value 31.460194
## iter 20 value 23.362872
## iter 30 value 21.233676
## iter 40 value 21.198754
## iter 50 value 21.193000
## iter 60 value 21.191254
## iter 70 value 21.190278
## iter 80 value 21.188612
## iter 90 value 21.188113
## iter 100 value 21.187484
## final value 21.187484
## stopped after 100 iterations## # weights: 157
## initial value 60.032637
## iter 10 value 21.084060
## iter 20 value 11.969126
## iter 30 value 11.885077
## iter 40 value 11.845975
## iter 50 value 11.618856
## iter 60 value 11.215713
## iter 70 value 11.207880
## iter 80 value 11.019259
## iter 90 value 11.009335
## iter 100 value 10.816974
## final value 10.816974
## stopped after 100 iterations## # weights: 261
## initial value 65.103047
## iter 10 value 13.323567
## iter 20 value 11.481074
## iter 30 value 11.340267
## iter 40 value 11.314241
## iter 50 value 9.055264
## iter 60 value 8.766264
## iter 70 value 8.614313
## iter 80 value 8.580427
## iter 90 value 8.446084
## iter 100 value 8.392667
## final value 8.392667
## stopped after 100 iterations## # weights: 53
## initial value 65.394834
## iter 10 value 26.649873
## iter 20 value 20.375304
## iter 30 value 19.739350
## final value 19.739193
## converged## # weights: 157
## initial value 63.976560
## iter 10 value 22.329781
## iter 20 value 17.723722
## iter 30 value 17.244174
## iter 40 value 16.894238
## iter 50 value 16.502657
## iter 60 value 16.311228
## iter 70 value 14.955582
## iter 80 value 12.303970
## iter 90 value 9.777311
## iter 100 value 9.693710
## final value 9.693710
## stopped after 100 iterations## # weights: 261
## initial value 65.141344
## iter 10 value 14.743506
## iter 20 value 2.453797
## iter 30 value 1.927316
## iter 40 value 1.919016
## iter 50 value 1.912213
## iter 60 value 1.910136
## iter 70 value 1.909808
## iter 80 value 1.909731
## iter 90 value 1.909696
## iter 100 value 1.909634
## final value 1.909634
## stopped after 100 iterations## # weights: 53
## initial value 75.048952
## iter 10 value 32.734917
## iter 20 value 27.185107
## iter 30 value 24.325691
## iter 40 value 22.145492
## iter 50 value 22.080241
## final value 22.080165
## converged## # weights: 157
## initial value 68.864961
## iter 10 value 31.340439
## iter 20 value 17.972153
## iter 30 value 15.756298
## iter 40 value 15.082444
## iter 50 value 14.602671
## iter 60 value 14.560498
## iter 70 value 14.560351
## final value 14.560350
## converged## # weights: 261
## initial value 77.452817
## iter 10 value 26.216095
## iter 20 value 16.091762
## iter 30 value 14.030347
## iter 40 value 13.602614
## iter 50 value 13.553986
## iter 60 value 13.476409
## iter 70 value 13.322847
## iter 80 value 13.236826
## iter 90 value 13.221558
## iter 100 value 13.220332
## final value 13.220332
## stopped after 100 iterations## # weights: 53
## initial value 62.986207
## iter 10 value 27.957044
## iter 20 value 21.728884
## iter 30 value 19.843760
## iter 40 value 19.788914
## iter 50 value 19.783443
## iter 60 value 19.782078
## iter 70 value 19.781528
## iter 80 value 19.780383
## iter 90 value 19.779830
## iter 100 value 19.779516
## final value 19.779516
## stopped after 100 iterations## # weights: 157
## initial value 67.946829
## iter 10 value 5.143211
## iter 20 value 2.872996
## iter 30 value 2.492564
## iter 40 value 2.468161
## iter 50 value 2.451645
## iter 60 value 2.435860
## iter 70 value 2.324334
## iter 80 value 2.084181
## iter 90 value 2.074648
## iter 100 value 2.064860
## final value 2.064860
## stopped after 100 iterations## # weights: 261
## initial value 65.981497
## iter 10 value 8.865999
## iter 20 value 2.325238
## iter 30 value 2.032740
## iter 40 value 2.019289
## iter 50 value 2.009888
## iter 60 value 2.003198
## iter 70 value 1.998961
## iter 80 value 1.994651
## iter 90 value 1.989856
## iter 100 value 1.987301
## final value 1.987301
## stopped after 100 iterations## # weights: 53
## initial value 67.932119
## iter 10 value 28.209356
## iter 20 value 23.897657
## iter 30 value 23.841782
## iter 40 value 23.840673
## final value 23.840551
## converged## # weights: 157
## initial value 64.420690
## iter 10 value 16.580572
## iter 20 value 13.418847
## iter 30 value 12.972412
## iter 40 value 12.230415
## iter 50 value 12.133453
## iter 60 value 12.051397
## iter 70 value 12.002183
## iter 80 value 11.961278
## iter 90 value 11.919645
## iter 100 value 11.850946
## final value 11.850946
## stopped after 100 iterations## # weights: 261
## initial value 85.400352
## iter 10 value 13.483709
## iter 20 value 10.876053
## iter 30 value 10.303589
## iter 40 value 9.966888
## iter 50 value 9.100327
## iter 60 value 8.117008
## iter 70 value 8.079654
## iter 80 value 8.059474
## iter 90 value 8.048204
## iter 100 value 8.047807
## final value 8.047807
## stopped after 100 iterations## # weights: 53
## initial value 83.971657
## iter 10 value 30.132161
## iter 20 value 21.381929
## iter 30 value 20.672402
## iter 40 value 20.669600
## final value 20.669597
## converged## # weights: 157
## initial value 100.883161
## iter 10 value 31.568586
## iter 20 value 18.330537
## iter 30 value 17.166150
## iter 40 value 16.754423
## iter 50 value 16.656769
## iter 60 value 16.640671
## iter 70 value 16.640298
## final value 16.640282
## converged## # weights: 261
## initial value 65.587782
## iter 10 value 27.487553
## iter 20 value 16.710670
## iter 30 value 15.155886
## iter 40 value 14.461294
## iter 50 value 14.345649
## iter 60 value 14.321782
## iter 70 value 14.308432
## iter 80 value 14.307048
## iter 90 value 14.306754
## final value 14.306737
## converged## # weights: 53
## initial value 65.216886
## iter 10 value 42.153219
## iter 20 value 42.128608
## iter 30 value 42.056425
## iter 40 value 39.697145
## iter 50 value 38.687615
## iter 60 value 36.517515
## iter 70 value 33.764410
## iter 80 value 33.752134
## iter 90 value 32.510324
## iter 100 value 31.198869
## final value 31.198869
## stopped after 100 iterations## # weights: 157
## initial value 68.269707
## iter 10 value 22.524600
## iter 20 value 12.770492
## iter 30 value 10.542288
## iter 40 value 10.346527
## iter 50 value 10.232937
## iter 60 value 10.196297
## iter 70 value 10.165532
## iter 80 value 10.152049
## iter 90 value 10.014289
## iter 100 value 9.605290
## final value 9.605290
## stopped after 100 iterations## # weights: 261
## initial value 62.491620
## iter 10 value 12.893146
## iter 20 value 4.047888
## iter 30 value 2.953651
## iter 40 value 2.874917
## iter 50 value 2.867547
## iter 60 value 2.863212
## iter 70 value 2.859160
## iter 80 value 2.854268
## iter 90 value 2.850111
## iter 100 value 2.847480
## final value 2.847480
## stopped after 100 iterations## # weights: 53
## initial value 65.337921
## iter 10 value 37.626015
## iter 20 value 18.744690
## iter 30 value 12.546229
## iter 40 value 10.334270
## iter 50 value 10.317538
## iter 60 value 10.317447
## iter 70 value 10.317442
## final value 10.317441
## converged## # weights: 157
## initial value 82.995975
## iter 10 value 14.846560
## iter 20 value 11.796021
## iter 30 value 10.070551
## iter 40 value 10.063461
## iter 50 value 9.986432
## iter 60 value 5.080706
## iter 70 value 2.812261
## iter 80 value 2.785430
## iter 90 value 2.778886
## iter 100 value 2.773701
## final value 2.773701
## stopped after 100 iterations## # weights: 261
## initial value 63.040160
## iter 10 value 7.262390
## iter 20 value 3.093367
## iter 30 value 2.796420
## iter 40 value 2.774839
## iter 50 value 2.772766
## iter 60 value 2.772694
## iter 70 value 2.772602
## final value 2.772600
## converged## # weights: 53
## initial value 64.628701
## iter 10 value 39.508283
## iter 20 value 23.598400
## iter 30 value 22.374385
## iter 40 value 21.499770
## iter 50 value 21.491669
## final value 21.491668
## converged## # weights: 157
## initial value 77.868995
## iter 10 value 27.678413
## iter 20 value 18.223761
## iter 30 value 17.034074
## iter 40 value 16.462135
## iter 50 value 15.742847
## iter 60 value 15.623260
## iter 70 value 15.617007
## iter 80 value 15.616519
## final value 15.616518
## converged## # weights: 261
## initial value 72.271506
## iter 10 value 24.316089
## iter 20 value 15.029466
## iter 30 value 14.132286
## iter 40 value 13.827104
## iter 50 value 13.784108
## iter 60 value 13.776826
## iter 70 value 13.776479
## iter 80 value 13.776188
## iter 90 value 13.775651
## iter 100 value 13.774716
## final value 13.774716
## stopped after 100 iterations## # weights: 53
## initial value 65.837746
## iter 10 value 26.442437
## iter 20 value 24.789320
## iter 30 value 24.774657
## iter 40 value 22.599874
## iter 50 value 20.335667
## iter 60 value 19.363658
## iter 70 value 19.230545
## iter 80 value 19.225076
## iter 90 value 19.222539
## iter 100 value 19.221815
## final value 19.221815
## stopped after 100 iterations## # weights: 157
## initial value 79.128507
## iter 10 value 21.693540
## iter 20 value 11.308251
## iter 30 value 9.304688
## iter 40 value 9.091331
## iter 50 value 8.294721
## iter 60 value 8.252576
## iter 70 value 7.762950
## iter 80 value 7.732598
## iter 90 value 7.723106
## iter 100 value 6.310190
## final value 6.310190
## stopped after 100 iterations## # weights: 261
## initial value 66.092716
## iter 10 value 17.880337
## iter 20 value 5.790907
## iter 30 value 5.217146
## iter 40 value 5.190049
## iter 50 value 5.167257
## iter 60 value 5.149218
## iter 70 value 4.825088
## iter 80 value 4.806152
## iter 90 value 4.795985
## iter 100 value 4.789655
## final value 4.789655
## stopped after 100 iterations## # weights: 53
## initial value 67.459353
## iter 10 value 25.517427
## iter 20 value 17.959106
## iter 30 value 15.642334
## iter 40 value 15.297608
## iter 50 value 15.281571
## iter 60 value 15.272550
## iter 70 value 15.268995
## iter 80 value 15.266989
## iter 90 value 15.266291
## iter 100 value 15.266048
## final value 15.266048
## stopped after 100 iterations## # weights: 157
## initial value 93.968042
## iter 10 value 19.946444
## iter 20 value 11.059053
## iter 30 value 9.704478
## iter 40 value 9.258519
## iter 50 value 8.955155
## iter 60 value 8.660756
## iter 70 value 8.536592
## iter 80 value 8.533514
## iter 90 value 8.500897
## iter 100 value 8.411608
## final value 8.411608
## stopped after 100 iterations## # weights: 261
## initial value 66.814206
## iter 10 value 12.276621
## iter 20 value 2.390358
## iter 30 value 1.825604
## iter 40 value 1.451553
## iter 50 value 1.411833
## iter 60 value 1.394269
## iter 70 value 1.390991
## iter 80 value 1.386543
## iter 90 value 1.386391
## iter 100 value 1.386362
## final value 1.386362
## stopped after 100 iterations## # weights: 53
## initial value 71.091928
## iter 10 value 29.017277
## iter 20 value 24.051269
## iter 30 value 22.935587
## iter 40 value 22.924659
## final value 22.924655
## converged## # weights: 157
## initial value 63.802693
## iter 10 value 33.909196
## iter 20 value 19.134554
## iter 30 value 16.106227
## iter 40 value 15.607481
## iter 50 value 15.295147
## iter 60 value 15.240746
## iter 70 value 15.232928
## iter 80 value 15.232876
## iter 80 value 15.232876
## iter 80 value 15.232876
## final value 15.232876
## converged## # weights: 261
## initial value 86.839453
## iter 10 value 23.272635
## iter 20 value 14.940892
## iter 30 value 13.863934
## iter 40 value 13.694021
## iter 50 value 13.641784
## iter 60 value 13.629279
## iter 70 value 13.628584
## iter 80 value 13.628548
## final value 13.628547
## converged## # weights: 53
## initial value 73.029069
## iter 10 value 29.485352
## iter 20 value 24.945855
## iter 30 value 23.038718
## iter 40 value 22.783732
## iter 50 value 22.771367
## iter 60 value 22.769437
## iter 70 value 21.298963
## iter 80 value 21.237013
## iter 90 value 21.231797
## iter 100 value 18.102106
## final value 18.102106
## stopped after 100 iterations## # weights: 157
## initial value 87.859224
## iter 10 value 24.453112
## iter 20 value 13.796061
## iter 30 value 11.463807
## iter 40 value 11.076867
## iter 50 value 11.046600
## iter 60 value 10.596066
## iter 70 value 9.874770
## iter 80 value 9.851977
## iter 90 value 9.843334
## iter 100 value 9.731097
## final value 9.731097
## stopped after 100 iterations## # weights: 261
## initial value 77.156049
## iter 10 value 10.407735
## iter 20 value 4.457759
## iter 30 value 3.488384
## iter 40 value 3.434967
## iter 50 value 3.425848
## iter 60 value 3.420404
## iter 70 value 3.395251
## iter 80 value 1.640209
## iter 90 value 1.528378
## iter 100 value 1.517599
## final value 1.517599
## stopped after 100 iterations## # weights: 53
## initial value 73.252149
## iter 10 value 23.857838
## iter 20 value 19.105436
## iter 30 value 18.995764
## iter 40 value 17.128117
## iter 50 value 17.126783
## iter 60 value 17.126661
## final value 17.126660
## converged## # weights: 157
## initial value 82.375444
## iter 10 value 20.521679
## iter 20 value 11.647108
## iter 30 value 10.138669
## iter 40 value 9.449713
## iter 50 value 9.018879
## iter 60 value 8.978654
## iter 70 value 8.969893
## iter 80 value 7.712849
## iter 90 value 7.553340
## iter 100 value 7.487119
## final value 7.487119
## stopped after 100 iterations## # weights: 261
## initial value 61.065966
## iter 10 value 12.036812
## iter 20 value 6.343037
## iter 30 value 4.894622
## iter 40 value 4.691812
## iter 50 value 4.682827
## iter 60 value 4.682536
## iter 70 value 4.682300
## iter 80 value 4.682231
## iter 90 value 4.682159
## iter 100 value 4.682146
## final value 4.682146
## stopped after 100 iterations## # weights: 53
## initial value 81.631617
## iter 10 value 29.835992
## iter 20 value 20.516844
## iter 30 value 20.165259
## iter 40 value 20.152299
## final value 20.152178
## converged## # weights: 157
## initial value 79.378362
## iter 10 value 27.551644
## iter 20 value 17.303492
## iter 30 value 16.869984
## iter 40 value 16.825890
## iter 50 value 16.824367
## final value 16.824353
## converged## # weights: 261
## initial value 75.569610
## iter 10 value 23.886647
## iter 20 value 14.417824
## iter 30 value 13.587029
## iter 40 value 13.246156
## iter 50 value 13.185449
## iter 60 value 13.171249
## iter 70 value 13.169329
## iter 80 value 13.157412
## iter 90 value 13.140999
## iter 100 value 13.137613
## final value 13.137613
## stopped after 100 iterations## # weights: 53
## initial value 70.312397
## iter 10 value 24.802565
## iter 20 value 19.045351
## iter 30 value 18.976664
## iter 40 value 16.444385
## iter 50 value 16.439287
## iter 60 value 16.438160
## iter 70 value 16.436223
## iter 80 value 16.435426
## iter 90 value 16.434366
## iter 100 value 16.433950
## final value 16.433950
## stopped after 100 iterations## # weights: 157
## initial value 64.879736
## iter 10 value 26.683210
## iter 20 value 9.887488
## iter 30 value 9.195051
## iter 40 value 9.169714
## iter 50 value 8.712941
## iter 60 value 8.704140
## iter 70 value 8.694020
## iter 80 value 8.681755
## iter 90 value 8.652187
## iter 100 value 7.368691
## final value 7.368691
## stopped after 100 iterations## # weights: 261
## initial value 67.123199
## iter 10 value 14.988300
## iter 20 value 5.272010
## iter 30 value 5.113325
## iter 40 value 3.117733
## iter 50 value 3.088037
## iter 60 value 3.051784
## iter 70 value 3.019616
## iter 80 value 3.003190
## iter 90 value 2.975267
## iter 100 value 2.952790
## final value 2.952790
## stopped after 100 iterations## # weights: 53
## initial value 64.342963
## iter 10 value 21.469420
## iter 20 value 14.373840
## iter 30 value 12.066598
## iter 40 value 12.060218
## iter 50 value 12.058461
## iter 60 value 12.057406
## iter 70 value 12.056913
## iter 80 value 12.056595
## iter 90 value 12.056538
## iter 100 value 12.056400
## final value 12.056400
## stopped after 100 iterations## # weights: 157
## initial value 85.024862
## iter 10 value 16.784845
## iter 20 value 8.192739
## iter 30 value 7.493305
## iter 40 value 7.319016
## iter 50 value 7.316908
## iter 60 value 7.316686
## iter 70 value 7.316616
## iter 80 value 7.316469
## iter 90 value 7.316062
## iter 100 value 7.305489
## final value 7.305489
## stopped after 100 iterations## # weights: 261
## initial value 63.805383
## iter 10 value 8.657659
## iter 20 value 5.251050
## iter 30 value 4.915643
## iter 40 value 4.450206
## iter 50 value 4.176527
## iter 60 value 4.166050
## iter 70 value 3.422802
## iter 80 value 1.958080
## iter 90 value 1.927206
## iter 100 value 1.910441
## final value 1.910441
## stopped after 100 iterations## # weights: 53
## initial value 80.872120
## iter 10 value 31.992945
## iter 20 value 24.266337
## iter 30 value 21.586789
## iter 40 value 21.347428
## iter 50 value 21.346806
## final value 21.346805
## converged## # weights: 157
## initial value 81.043016
## iter 10 value 25.310746
## iter 20 value 17.955016
## iter 30 value 16.758138
## iter 40 value 15.954713
## iter 50 value 15.804915
## iter 60 value 15.788677
## iter 70 value 15.787287
## final value 15.787229
## converged## # weights: 261
## initial value 67.474600
## iter 10 value 22.395009
## iter 20 value 15.290687
## iter 30 value 14.622682
## iter 40 value 14.503953
## iter 50 value 14.438805
## iter 60 value 14.432389
## iter 70 value 14.432202
## final value 14.432196
## converged## # weights: 53
## initial value 62.346341
## iter 10 value 33.594427
## iter 20 value 31.468032
## iter 30 value 30.048776
## iter 40 value 30.040786
## iter 50 value 30.027090
## iter 60 value 28.766297
## iter 70 value 28.531882
## iter 80 value 28.527882
## iter 90 value 28.522046
## iter 100 value 25.222311
## final value 25.222311
## stopped after 100 iterations## # weights: 157
## initial value 70.165760
## iter 10 value 9.938940
## iter 20 value 6.774866
## iter 30 value 6.752288
## iter 40 value 6.734533
## iter 50 value 6.723967
## iter 60 value 6.705774
## iter 70 value 5.854378
## iter 80 value 5.849139
## iter 90 value 5.842702
## iter 100 value 5.838469
## final value 5.838469
## stopped after 100 iterations## # weights: 261
## initial value 71.923188
## iter 10 value 12.286653
## iter 20 value 8.924931
## iter 30 value 8.890932
## iter 40 value 8.846728
## iter 50 value 8.797523
## iter 60 value 8.557170
## iter 70 value 2.536808
## iter 80 value 2.102577
## iter 90 value 2.066940
## iter 100 value 2.055528
## final value 2.055528
## stopped after 100 iterations## # weights: 53
## initial value 61.012380
## iter 10 value 35.745953
## iter 20 value 26.526674
## iter 30 value 21.460215
## iter 40 value 21.135892
## iter 50 value 21.117489
## final value 21.117461
## converged## # weights: 157
## initial value 67.438540
## iter 10 value 20.368884
## iter 20 value 5.935729
## iter 30 value 5.305179
## iter 40 value 2.890755
## iter 50 value 2.800269
## iter 60 value 2.775258
## iter 70 value 2.773650
## iter 80 value 2.773067
## iter 90 value 2.772927
## iter 100 value 2.772747
## final value 2.772747
## stopped after 100 iterations## # weights: 261
## initial value 80.179451
## iter 10 value 12.105025
## iter 20 value 4.864277
## iter 30 value 4.192913
## iter 40 value 4.164626
## iter 50 value 4.159308
## iter 60 value 4.158936
## final value 4.158895
## converged## # weights: 53
## initial value 73.135429
## iter 10 value 31.199457
## iter 20 value 24.205167
## iter 30 value 20.806383
## iter 40 value 20.788726
## final value 20.788618
## converged## # weights: 157
## initial value 63.976653
## iter 10 value 27.071379
## iter 20 value 18.690987
## iter 30 value 17.938709
## iter 40 value 17.449040
## iter 50 value 17.178123
## iter 60 value 17.174151
## iter 70 value 17.174001
## final value 17.173998
## converged## # weights: 261
## initial value 69.243457
## iter 10 value 28.336474
## iter 20 value 16.200270
## iter 30 value 14.944088
## iter 40 value 14.597535
## iter 50 value 14.503838
## iter 60 value 14.396834
## iter 70 value 14.388025
## iter 80 value 14.384901
## iter 90 value 14.366870
## iter 100 value 14.361665
## final value 14.361665
## stopped after 100 iterations## # weights: 53
## initial value 67.353966
## iter 10 value 25.799872
## iter 20 value 24.429035
## iter 30 value 23.191284
## iter 40 value 21.788280
## iter 50 value 21.179182
## iter 60 value 21.172661
## iter 70 value 21.170195
## iter 80 value 21.168116
## iter 90 value 21.147002
## iter 100 value 18.955542
## final value 18.955542
## stopped after 100 iterations## # weights: 157
## initial value 67.966433
## iter 10 value 13.503964
## iter 20 value 6.879559
## iter 30 value 6.753208
## iter 40 value 6.737144
## iter 50 value 6.710560
## iter 60 value 6.584771
## iter 70 value 6.182087
## iter 80 value 5.689714
## iter 90 value 5.661633
## iter 100 value 5.645091
## final value 5.645091
## stopped after 100 iterations## # weights: 261
## initial value 65.351999
## iter 10 value 20.349269
## iter 20 value 4.629061
## iter 30 value 3.926935
## iter 40 value 3.884645
## iter 50 value 3.800091
## iter 60 value 3.297263
## iter 70 value 3.102586
## iter 80 value 3.062166
## iter 90 value 3.043879
## iter 100 value 3.030434
## final value 3.030434
## stopped after 100 iterations## # weights: 261
## initial value 67.066511
## iter 10 value 21.642848
## iter 20 value 9.777535
## iter 30 value 8.741794
## iter 40 value 8.708571
## iter 50 value 8.682078
## iter 60 value 8.518986
## iter 70 value 8.513239
## iter 80 value 8.334074
## iter 90 value 7.931736
## iter 100 value 7.914104
## final value 7.914104
## stopped after 100 iterationsresultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$m1_purchase)
mcre5## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 36 4
## Yes 0 67
##
## Accuracy : 0.9626
## 95% CI : (0.907, 0.9897)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 3.214e-14
##
## Kappa : 0.9185
##
## Mcnemar's Test P-Value : 0.1336
##
## Sensitivity : 1.0000
## Specificity : 0.9437
## Pos Pred Value : 0.9000
## Neg Pred Value : 1.0000
## Prevalence : 0.3364
## Detection Rate : 0.3364
## Detection Prevalence : 0.3738
## Balanced Accuracy : 0.9718
##
## 'Positive' Class : No
## mcrp5 <- confusionMatrix(resultado_prueba5, prueba$m1_purchase)
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 4 8
## Yes 5 9
##
## Accuracy : 0.5
## 95% CI : (0.2993, 0.7007)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.9657
##
## Kappa : -0.0242
##
## Mcnemar's Test P-Value : 0.5791
##
## Sensitivity : 0.4444
## Specificity : 0.5294
## Pos Pred Value : 0.3333
## Neg Pred Value : 0.6429
## Prevalence : 0.3462
## Detection Rate : 0.1538
## Detection Prevalence : 0.4615
## Balanced Accuracy : 0.4869
##
## 'Positive' Class : No
## Modelo 6. Bosques Aleatorios
modelo6 <- train(m1_purchase ~ ., data=entrenamiento,
method = "rf", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = expand.grid(mtry = c(2,4,6)) #Cambiar
)## 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 , 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 , domainLogistics, 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 , 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 , 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 , 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_pcmacHp, 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, 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, statusUnemployed, domainCommunication , 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 , 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 , 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 , 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,
## statusStudent ant employed, statusUnemployed, domainCommunication , domainLaw,
## 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 , domainLaw,
## 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 , 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 , 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_pcmacOther, statusRetired,
## statusUnemployed, domainCommunication , domainRetiredresultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)
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
## mcrp6 <- confusionMatrix(resultado_prueba6, prueba$m1_purchase)
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 5 6
## Yes 4 11
##
## Accuracy : 0.6154
## 95% CI : (0.4057, 0.7977)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.7358
##
## Kappa : 0.1925
##
## Mcnemar's Test P-Value : 0.7518
##
## Sensitivity : 0.5556
## Specificity : 0.6471
## Pos Pred Value : 0.4545
## Neg Pred Value : 0.7333
## Prevalence : 0.3462
## Detection Rate : 0.1923
## Detection Prevalence : 0.4231
## Balanced Accuracy : 0.6013
##
## 'Positive' Class : No
## Tabla de Resultados
resultados <- data.frame(
"svmLinear" = c(mcre1$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 0.9345794 0.8878505 0.8785047 0.8037383 0.9626168
## Precisión de\nprueba 0.5384615 0.5769231 0.5384615 0.5769231 0.5000000
## rf
## Precisión de entrenamiento 0.9813084
## Precisión de\nprueba 0.6153846Conclusiones
Me puse a calar varios algoritmos para ver cual le atinaba mejor a si la raza compraria la M1 de Apple y la verdad es que el Random Forest fue el que mejor se porto, sacando un 0.6154 de precision ya en los datos de prueba. Despues me quedaron los modelos de SVM (el Radial y el Poly) que empataron con un 0.5769. Lo que si de plano no jalo fueron las Redes Neuronales (nnet), porque sacaron un 0.50, o sea, fue exactamente lo mismo que si hubiera intentado adivinar el resultado al azar.
Algo que me queda muy claro es que a todos mis modelos les dio el mal del sobreajuste (overfitting). Se emocionaron de mas con los datos de entrenamiento y a la hora de la verdad, con los datos de prueba, la precision se les cayo bastante. Siento que esto me paso porque mi dataset esta bien chiquito, son apenas 133 filas, y pues asi esta dificil que el modelo aprenda patrones que de verdad sirvan para gente nueva y no nada mas se memorice lo que ya vio.
Mi conclusion final del analisis
En conclusion, si hoy me tuviera que jugar la vida con un modelo para predecir las compras, me quedaria mil veces con el Random Forest porque fue el mas estable en la validacion. Pero siendo realistas, la leccion que me llevo es que con tan poquitos datos no se pueden hacer milagros. Mi recomendacion para despues seria conseguir una base de datos mucho mas grande para que el entrenamiento sea mas real y la precision no se me caiga tanto a la hora de probar el modelo.