M1
Giuliana Mancera Flores - A00840416
01.03.2026
Teoría
El paquete CARET (Classification And REgression Training) es un paquete integral con una amplia variedad de algoritmos para el aprendizaje automático. # Instalar paquetes y llamar librerías
# install.packages("ggplot2") # Gráficas
library(ggplot2)
# install.packages("lattice") # Crear gráficos
library(lattice)
# install.packages("caret") # Algoritmos de aprendizaje automático
library(caret)
# install.packages("datasets") # Usar bases de datos, en este caso Iris
library(datasets)
# install.packages("DataExplorer") # Análisis Exploratorio
library(DataExplorer)Crear la base de datos
#file.choose()
M1 <- read.csv("/Users/giuliana/Downloads/M1_data.csv")
df <- data.frame(M1)
head(df)## trust_apple interest_computers age_computer user_pcmac appleproducts_count
## 1 No 4 8 PC 0
## 2 Yes 2 4 PC 1
## 3 Yes 5 6 PC 0
## 4 Yes 2 6 Apple 4
## 5 Yes 4 4 Apple 7
## 6 Yes 3 1 Apple 2
## familiarity_m1 f_batterylife f_price f_size f_multitasking f_noise
## 1 No 5 4 3 4 4
## 2 No 5 5 5 3 4
## 3 No 3 4 2 4 1
## 4 No 4 3 3 4 4
## 5 Yes 5 3 3 4 4
## 6 No 5 5 4 4 5
## f_performance f_neural f_synergy f_performanceloss m1_consideration
## 1 2 2 1 1 1
## 2 5 2 2 4 2
## 3 4 2 2 2 4
## 4 4 4 4 3 2
## 5 5 3 4 4 4
## 6 5 5 4 2 2
## m1_purchase gender age_group income_group status domain
## 1 Yes Male 2 2 Student Science
## 2 No Male 2 3 Employed Finance
## 3 Yes Male 2 2 Student IT & Technology
## 4 No Female 2 2 Student Arts & Culture
## 5 Yes Male 5 7 Employed Hospitality
## 6 No Female 2 2 Student Politicsnzv <- nearZeroVar(df)
if(length(nzv) > 0) df <- df[ , -nzv]#Variable objetivo a factor
df$m1_purchase <- as.factor(df$m1_consideration)Entender la base de datos
summary(df)## trust_apple interest_computers age_computer user_pcmac
## Length:133 Min. :2.000 Min. :0.000 Length:133
## Class :character 1st Qu.:3.000 1st Qu.:1.000 Class :character
## Mode :character Median :4.000 Median :3.000 Mode :character
## Mean :3.812 Mean :2.827
## 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :9.000
## appleproducts_count familiarity_m1 f_batterylife f_price
## Min. :0.000 Length:133 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 Class :character 1st Qu.:4.000 1st Qu.:3.000
## Median :3.000 Mode :character Median :5.000 Median :4.000
## Mean :2.609 Mean :4.526 Mean :3.872
## 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :8.000 Max. :5.000 Max. :5.000
## f_size f_multitasking f_noise f_performance f_neural
## Min. :1.000 Min. :2.00 Min. :1.000 Min. :2.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:4.00 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:2.000
## Median :3.000 Median :4.00 Median :4.000 Median :5.000 Median :3.000
## Mean :3.158 Mean :4.12 Mean :3.729 Mean :4.398 Mean :3.165
## 3rd Qu.:4.000 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000
## f_synergy f_performanceloss m1_consideration m1_purchase
## Min. :1.000 Min. :1.000 Min. :1.000 1:10
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 2:15
## Median :4.000 Median :4.000 Median :4.000 3:33
## Mean :3.466 Mean :3.376 Mean :3.609 4:34
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 5:41
## Max. :5.000 Max. :5.000 Max. :5.000
## gender age_group income_group status
## Length:133 Min. : 1.00 Min. :1.00 Length:133
## Class :character 1st Qu.: 2.00 1st Qu.:1.00 Class :character
## Mode :character Median : 2.00 Median :2.00 Mode :character
## Mean : 2.97 Mean :2.97
## 3rd Qu.: 3.00 3rd Qu.:4.00
## Max. :10.00 Max. :7.00
## domain
## Length:133
## Class :character
## Mode :character
##
##
## str(df)## 'data.frame': 133 obs. of 22 variables:
## $ trust_apple : chr "No" "Yes" "Yes" "Yes" ...
## $ 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 : chr "PC" "PC" "PC" "Apple" ...
## $ appleproducts_count: int 0 1 0 4 7 2 7 0 6 7 ...
## $ familiarity_m1 : chr "No" "No" "No" "No" ...
## $ f_batterylife : int 5 5 3 4 5 5 4 5 4 5 ...
## $ f_price : int 4 5 4 3 3 5 3 5 4 3 ...
## $ f_size : int 3 5 2 3 3 4 4 4 3 5 ...
## $ f_multitasking : int 4 3 4 4 4 4 5 4 4 5 ...
## $ f_noise : int 4 4 1 4 4 5 5 3 4 5 ...
## $ f_performance : int 2 5 4 4 5 5 5 3 4 5 ...
## $ f_neural : int 2 2 2 4 3 5 3 2 3 3 ...
## $ f_synergy : int 1 2 2 4 4 4 3 2 3 5 ...
## $ f_performanceloss : int 1 4 2 3 4 2 2 3 4 5 ...
## $ m1_consideration : int 1 2 4 2 4 2 3 1 5 5 ...
## $ m1_purchase : Factor w/ 5 levels "1","2","3","4",..: 1 2 4 2 4 2 3 1 5 5 ...
## $ gender : chr "Male" "Male" "Male" "Female" ...
## $ 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 : chr "Student" "Employed" "Student" "Student" ...
## $ domain : chr "Science" "Finance" "IT & Technology" "Arts & Culture" ...# create_report(df)
plot_missing(df)## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the DataExplorer package.
## Please report the issue at
## <https://github.com/boxuancui/DataExplorer/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
plot_histogram(df)
plot_correlation(df)## 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
# Convertir todas las columnas categóricas en factor
cols_factor <- c("trust_apple","user_pcmac","familiarity_m1","m1_consideration", "m1_purchase","gender","status","domain")
df[cols_factor] <- lapply(df[cols_factor], factor)
set.seed(123)
# Normalmente 80-20
renglones_entrenamiento <- createDataPartition(df$m1_consideration, p=0.8,
list=FALSE)
entrenamiento <- df[renglones_entrenamiento, ]
prueba <- df[-renglones_entrenamiento, ]
# Ajustar niveles de factores en prueba para entrenamiento
prueba$status <- factor(prueba$status,
levels = levels(entrenamiento$status))
prueba <- droplevels(prueba)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_consideration ~ ., data=entrenamiento,
method = "svmLinear",
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(C=1)
)## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication , domainConsulting , domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLaw, domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## domainAgriculture, 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, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$m1_consideration)
mcre1## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 8 0 0 0 0
## 2 0 12 0 0 0
## 3 0 0 27 0 0
## 4 0 0 0 28 0
## 5 0 0 0 0 33
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9664, 1)
## No Information Rate : 0.3056
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 1.00000 1.0000 1.00 1.0000 1.0000
## Specificity 1.00000 1.0000 1.00 1.0000 1.0000
## Pos Pred Value 1.00000 1.0000 1.00 1.0000 1.0000
## Neg Pred Value 1.00000 1.0000 1.00 1.0000 1.0000
## Prevalence 0.07407 0.1111 0.25 0.2593 0.3056
## Detection Rate 0.07407 0.1111 0.25 0.2593 0.3056
## Detection Prevalence 0.07407 0.1111 0.25 0.2593 0.3056
## Balanced Accuracy 1.00000 1.0000 1.00 1.0000 1.0000# Matriz de Confusión del Resultado de la Prueba
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$m1_consideration)
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 1 0 0 0 1
## 2 0 3 0 0 0
## 3 0 0 6 0 0
## 4 0 0 0 6 0
## 5 1 0 0 0 7
##
## Overall Statistics
##
## Accuracy : 0.92
## 95% CI : (0.7397, 0.9902)
## No Information Rate : 0.32
## P-Value [Acc > NIR] : 5.992e-10
##
## Kappa : 0.895
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.5000 1.00 1.00 1.00 0.8750
## Specificity 0.9565 1.00 1.00 1.00 0.9412
## Pos Pred Value 0.5000 1.00 1.00 1.00 0.8750
## Neg Pred Value 0.9565 1.00 1.00 1.00 0.9412
## Prevalence 0.0800 0.12 0.24 0.24 0.3200
## Detection Rate 0.0400 0.12 0.24 0.24 0.2800
## Detection Prevalence 0.0800 0.12 0.24 0.24 0.3200
## Balanced Accuracy 0.7283 1.00 1.00 1.00 0.9081Modelo 2. SVM Radial
modelo2 <- train(m1_consideration ~ ., 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(0,
## : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## domainAgriculture, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## domainAgriculture, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## domainAgriculture, domainRetired## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainEconomics, domainLaw## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainEconomics, domainLaw## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainEconomics, domainLaw## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$m1_consideration)
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 5 0 0 0 0
## 2 0 12 0 0 0
## 3 0 0 27 0 0
## 4 2 0 0 28 0
## 5 1 0 0 0 33
##
## Overall Statistics
##
## Accuracy : 0.9722
## 95% CI : (0.921, 0.9942)
## No Information Rate : 0.3056
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9631
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.62500 1.0000 1.00 1.0000 1.0000
## Specificity 1.00000 1.0000 1.00 0.9750 0.9867
## Pos Pred Value 1.00000 1.0000 1.00 0.9333 0.9706
## Neg Pred Value 0.97087 1.0000 1.00 1.0000 1.0000
## Prevalence 0.07407 0.1111 0.25 0.2593 0.3056
## Detection Rate 0.04630 0.1111 0.25 0.2593 0.3056
## Detection Prevalence 0.04630 0.1111 0.25 0.2778 0.3148
## Balanced Accuracy 0.81250 1.0000 1.00 0.9875 0.9933# Matriz de Confusión del Resultado de la Prueba
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$m1_consideration)
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 0 0 0 0 0
## 2 0 2 0 0 0
## 3 0 0 6 0 0
## 4 1 0 0 5 0
## 5 1 1 0 1 8
##
## Overall Statistics
##
## Accuracy : 0.84
## 95% CI : (0.6392, 0.9546)
## No Information Rate : 0.32
## P-Value [Acc > NIR] : 1.197e-07
##
## Kappa : 0.7821
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.00 0.6667 1.00 0.8333 1.0000
## Specificity 1.00 1.0000 1.00 0.9474 0.8235
## Pos Pred Value NaN 1.0000 1.00 0.8333 0.7273
## Neg Pred Value 0.92 0.9565 1.00 0.9474 1.0000
## Prevalence 0.08 0.1200 0.24 0.2400 0.3200
## Detection Rate 0.00 0.0800 0.24 0.2000 0.3200
## Detection Prevalence 0.00 0.0800 0.24 0.2400 0.4400
## Balanced Accuracy 0.50 0.8333 1.00 0.8904 0.9118Modelo 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(0,
## : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting , domainRealestate## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainRetail## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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,
## domainAgriculture, 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, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(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,
## domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainEconomics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$m1_purchase)
mcre3## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 8 0 0 0 0
## 2 0 12 0 0 0
## 3 0 0 27 0 0
## 4 0 0 0 28 0
## 5 0 0 0 0 33
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9664, 1)
## No Information Rate : 0.3056
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 1.00000 1.0000 1.00 1.0000 1.0000
## Specificity 1.00000 1.0000 1.00 1.0000 1.0000
## Pos Pred Value 1.00000 1.0000 1.00 1.0000 1.0000
## Neg Pred Value 1.00000 1.0000 1.00 1.0000 1.0000
## Prevalence 0.07407 0.1111 0.25 0.2593 0.3056
## Detection Rate 0.07407 0.1111 0.25 0.2593 0.3056
## Detection Prevalence 0.07407 0.1111 0.25 0.2593 0.3056
## Balanced Accuracy 1.00000 1.0000 1.00 1.0000 1.0000# Matriz de Confusión del Resultado de la Prueba
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$m1_purchase)
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 1 0 0 0 1
## 2 0 3 0 0 0
## 3 0 0 6 0 0
## 4 0 0 0 6 0
## 5 1 0 0 0 7
##
## Overall Statistics
##
## Accuracy : 0.92
## 95% CI : (0.7397, 0.9902)
## No Information Rate : 0.32
## P-Value [Acc > NIR] : 5.992e-10
##
## Kappa : 0.895
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.5000 1.00 1.00 1.00 0.8750
## Specificity 0.9565 1.00 1.00 1.00 0.9412
## Pos Pred Value 0.5000 1.00 1.00 1.00 0.8750
## Neg Pred Value 0.9565 1.00 1.00 1.00 0.9412
## Prevalence 0.0800 0.12 0.24 0.24 0.3200
## Detection Rate 0.0400 0.12 0.24 0.24 0.2800
## Detection Prevalence 0.0800 0.12 0.24 0.24 0.3200
## Balanced Accuracy 0.7283 1.00 1.00 1.00 0.9081Modelo 4. Árbol de Decisión
modelo4 <- train(m1_consideration ~ ., 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,
## domainAgriculture, domainLaw, domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainConsulting## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainRealestate## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## domainAgriculture, domainCommunication## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture, domainEconomics, domainRetail## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgricultureresultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$m1_consideration)
mcre4## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 0 0 0 0 0
## 2 8 12 0 0 0
## 3 0 0 27 0 0
## 4 0 0 0 28 0
## 5 0 0 0 0 33
##
## Overall Statistics
##
## Accuracy : 0.9259
## 95% CI : (0.8593, 0.9675)
## No Information Rate : 0.3056
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9021
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.00000 1.0000 1.00 1.0000 1.0000
## Specificity 1.00000 0.9167 1.00 1.0000 1.0000
## Pos Pred Value NaN 0.6000 1.00 1.0000 1.0000
## Neg Pred Value 0.92593 1.0000 1.00 1.0000 1.0000
## Prevalence 0.07407 0.1111 0.25 0.2593 0.3056
## Detection Rate 0.00000 0.1111 0.25 0.2593 0.3056
## Detection Prevalence 0.00000 0.1852 0.25 0.2593 0.3056
## Balanced Accuracy 0.50000 0.9583 1.00 1.0000 1.0000# Matriz de Confusión del Resultado de la Prueba
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$m1_consideration)
mcrp4## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 0 0 0 0 0
## 2 2 3 0 0 0
## 3 0 0 6 0 0
## 4 0 0 0 6 0
## 5 0 0 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.92
## 95% CI : (0.7397, 0.9902)
## No Information Rate : 0.32
## P-Value [Acc > NIR] : 5.992e-10
##
## Kappa : 0.8945
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.00 1.0000 1.00 1.00 1.00
## Specificity 1.00 0.9091 1.00 1.00 1.00
## Pos Pred Value NaN 0.6000 1.00 1.00 1.00
## Neg Pred Value 0.92 1.0000 1.00 1.00 1.00
## Prevalence 0.08 0.1200 0.24 0.24 0.32
## Detection Rate 0.00 0.1200 0.24 0.24 0.32
## Detection Prevalence 0.00 0.2000 0.24 0.24 0.32
## Balanced Accuracy 0.50 0.9545 1.00 1.00 1.00Modelo 5. Redes Neuronales
modelo5 <- train(m1_consideration ~ ., data=entrenamiento,
method = "nnet", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10)
#Cambiar
)## # weights: 61
## initial value 158.848906
## iter 10 value 92.097645
## iter 20 value 78.459080
## iter 30 value 77.007965
## iter 40 value 76.046374
## iter 50 value 75.640617
## iter 60 value 75.083905
## iter 70 value 75.080808
## iter 80 value 75.079261
## iter 90 value 75.076860
## iter 100 value 74.634772
## final value 74.634772
## stopped after 100 iterations## # weights: 173
## initial value 167.104380
## iter 10 value 22.389799
## iter 20 value 12.677697
## iter 30 value 10.082608
## iter 40 value 9.938179
## iter 50 value 9.937458
## iter 60 value 9.937383
## final value 9.937382
## converged## # weights: 285
## initial value 181.607170
## iter 10 value 8.476203
## iter 20 value 1.513352
## iter 30 value 0.156845
## iter 40 value 0.020337
## iter 50 value 0.010346
## iter 60 value 0.004537
## iter 70 value 0.001806
## iter 80 value 0.001013
## iter 90 value 0.000269
## final value 0.000064
## converged## # weights: 61
## initial value 179.959760
## iter 10 value 99.332762
## iter 20 value 88.552033
## iter 30 value 84.609910
## iter 40 value 80.902803
## iter 50 value 77.664051
## iter 60 value 76.782385
## iter 70 value 76.722830
## final value 76.722741
## converged## # weights: 173
## initial value 182.776852
## iter 10 value 52.727091
## iter 20 value 25.021894
## iter 30 value 21.952915
## iter 40 value 21.189407
## iter 50 value 21.162097
## final value 21.162027
## converged## # weights: 285
## initial value 157.733913
## iter 10 value 35.127582
## iter 20 value 15.258073
## iter 30 value 14.825848
## iter 40 value 14.817072
## iter 50 value 14.816655
## final value 14.816644
## converged## # weights: 61
## initial value 179.410945
## iter 10 value 112.082754
## iter 20 value 80.879553
## iter 30 value 77.613308
## iter 40 value 76.804140
## iter 50 value 76.700994
## iter 60 value 76.572732
## iter 70 value 76.292971
## iter 80 value 76.247720
## iter 90 value 74.318836
## iter 100 value 70.432240
## final value 70.432240
## stopped after 100 iterations## # weights: 173
## initial value 163.215543
## iter 10 value 21.389274
## iter 20 value 16.949351
## iter 30 value 14.401578
## iter 40 value 12.670615
## iter 50 value 10.062359
## iter 60 value 9.990509
## iter 70 value 7.545880
## iter 80 value 6.863367
## iter 90 value 4.616399
## iter 100 value 4.184670
## final value 4.184670
## stopped after 100 iterations## # weights: 285
## initial value 174.804263
## iter 10 value 0.543134
## iter 20 value 0.146411
## iter 30 value 0.130798
## iter 40 value 0.126477
## iter 50 value 0.113225
## iter 60 value 0.106580
## iter 70 value 0.100244
## iter 80 value 0.096194
## iter 90 value 0.092324
## iter 100 value 0.086283
## final value 0.086283
## stopped after 100 iterations## # weights: 61
## initial value 154.905204
## iter 10 value 90.707528
## iter 20 value 67.812446
## iter 30 value 57.877691
## iter 40 value 48.605131
## iter 50 value 40.362708
## iter 60 value 28.404414
## iter 70 value 22.050834
## iter 80 value 18.917209
## iter 90 value 18.521156
## iter 100 value 18.246455
## final value 18.246455
## stopped after 100 iterations## # weights: 173
## initial value 155.837295
## iter 10 value 71.009951
## iter 20 value 20.788075
## iter 30 value 18.991433
## iter 40 value 16.952079
## iter 50 value 15.651461
## iter 60 value 15.512008
## iter 70 value 15.491572
## iter 80 value 15.487226
## iter 90 value 15.486656
## iter 100 value 15.486152
## final value 15.486152
## stopped after 100 iterations## # weights: 285
## initial value 156.409759
## iter 10 value 1.663892
## iter 20 value 0.037976
## iter 30 value 0.006688
## iter 40 value 0.000229
## final value 0.000100
## converged## # weights: 61
## initial value 177.368862
## iter 10 value 105.420626
## iter 20 value 92.548383
## iter 30 value 85.696558
## iter 40 value 81.119950
## iter 50 value 73.870455
## iter 60 value 73.234045
## iter 70 value 73.126683
## iter 80 value 73.119414
## iter 90 value 73.118093
## final value 73.118077
## converged## # weights: 173
## initial value 154.863327
## iter 10 value 37.546388
## iter 20 value 21.209534
## iter 30 value 20.627485
## iter 40 value 20.623879
## final value 20.623868
## converged## # weights: 285
## initial value 193.227036
## iter 10 value 25.979310
## iter 20 value 16.161769
## iter 30 value 14.229380
## iter 40 value 14.180109
## iter 50 value 14.179418
## final value 14.179417
## converged## # weights: 61
## initial value 173.026961
## iter 10 value 87.791076
## iter 20 value 78.128202
## iter 30 value 76.429700
## iter 40 value 73.141801
## iter 50 value 72.788916
## iter 60 value 69.851557
## iter 70 value 67.031944
## iter 80 value 64.021189
## iter 90 value 61.724728
## iter 100 value 61.528995
## final value 61.528995
## stopped after 100 iterations## # weights: 173
## initial value 169.180418
## iter 10 value 36.653462
## iter 20 value 20.650025
## iter 30 value 12.619561
## iter 40 value 8.477139
## iter 50 value 5.398380
## iter 60 value 2.505108
## iter 70 value 1.780554
## iter 80 value 0.520900
## iter 90 value 0.447609
## iter 100 value 0.381632
## final value 0.381632
## stopped after 100 iterations## # weights: 285
## initial value 144.507008
## iter 10 value 6.819426
## iter 20 value 0.426864
## iter 30 value 0.214016
## iter 40 value 0.186408
## iter 50 value 0.172914
## iter 60 value 0.152920
## iter 70 value 0.138225
## iter 80 value 0.131222
## iter 90 value 0.121472
## iter 100 value 0.117003
## final value 0.117003
## stopped after 100 iterations## # weights: 61
## initial value 172.737622
## iter 10 value 102.772687
## iter 20 value 84.242794
## iter 30 value 75.738540
## iter 40 value 73.723245
## iter 50 value 73.320029
## iter 60 value 73.248233
## iter 70 value 73.232022
## iter 80 value 73.230887
## iter 90 value 73.227730
## iter 100 value 73.227375
## final value 73.227375
## stopped after 100 iterations## # weights: 173
## initial value 148.950812
## iter 10 value 22.937813
## iter 20 value 20.682964
## iter 30 value 19.590260
## iter 40 value 16.720304
## iter 50 value 15.672569
## iter 60 value 14.382235
## iter 70 value 13.538608
## iter 80 value 12.857809
## iter 90 value 12.636381
## iter 100 value 12.191183
## final value 12.191183
## stopped after 100 iterations## # weights: 285
## initial value 184.599289
## iter 10 value 7.546810
## iter 20 value 0.285655
## iter 30 value 0.006369
## iter 40 value 0.000873
## final value 0.000092
## converged## # weights: 61
## initial value 161.947443
## iter 10 value 99.403065
## iter 20 value 88.763766
## iter 30 value 80.103154
## iter 40 value 77.047231
## iter 50 value 75.350415
## iter 60 value 74.915003
## iter 70 value 74.882992
## final value 74.882965
## converged## # weights: 173
## initial value 181.645856
## iter 10 value 63.983474
## iter 20 value 28.585674
## iter 30 value 21.141022
## iter 40 value 20.804361
## iter 50 value 20.799472
## iter 60 value 20.799317
## final value 20.799297
## converged## # weights: 285
## initial value 165.752832
## iter 10 value 42.714819
## iter 20 value 19.940488
## iter 30 value 15.200429
## iter 40 value 14.779843
## iter 50 value 14.731030
## iter 60 value 14.725938
## iter 70 value 14.724632
## iter 80 value 14.723947
## iter 90 value 14.723929
## final value 14.723928
## converged## # weights: 61
## initial value 160.942749
## iter 10 value 87.490587
## iter 20 value 79.052347
## iter 30 value 77.836369
## iter 40 value 77.760842
## iter 50 value 77.684000
## iter 60 value 76.097383
## iter 70 value 75.692606
## iter 80 value 73.530715
## iter 90 value 73.425201
## iter 100 value 73.380175
## final value 73.380175
## stopped after 100 iterations## # weights: 173
## initial value 185.254754
## iter 10 value 35.685149
## iter 20 value 30.299223
## iter 30 value 28.716875
## iter 40 value 28.076246
## iter 50 value 26.999852
## iter 60 value 26.418430
## iter 70 value 21.816748
## iter 80 value 20.604343
## iter 90 value 17.359569
## iter 100 value 15.490948
## final value 15.490948
## stopped after 100 iterations## # weights: 285
## initial value 170.079051
## iter 10 value 0.777881
## iter 20 value 0.239372
## iter 30 value 0.211141
## iter 40 value 0.172859
## iter 50 value 0.134429
## iter 60 value 0.116867
## iter 70 value 0.105830
## iter 80 value 0.100461
## iter 90 value 0.095437
## iter 100 value 0.091176
## final value 0.091176
## stopped after 100 iterations## # weights: 61
## initial value 172.752121
## iter 10 value 95.102463
## iter 20 value 78.367203
## iter 30 value 75.746030
## iter 40 value 73.942316
## iter 50 value 72.472566
## iter 60 value 71.370524
## iter 70 value 69.969433
## iter 80 value 67.864136
## iter 90 value 64.992825
## iter 100 value 64.587784
## final value 64.587784
## stopped after 100 iterations## # weights: 173
## initial value 174.175101
## iter 10 value 7.709053
## iter 20 value 0.020510
## iter 30 value 0.000673
## final value 0.000090
## converged## # weights: 285
## initial value 172.116533
## iter 10 value 10.291664
## iter 20 value 3.654305
## iter 30 value 0.047309
## iter 40 value 0.007106
## iter 50 value 0.001681
## iter 60 value 0.000258
## iter 70 value 0.000135
## final value 0.000094
## converged## # weights: 61
## initial value 155.373905
## iter 10 value 109.484708
## iter 20 value 92.160624
## iter 30 value 85.749411
## iter 40 value 78.401422
## iter 50 value 77.001162
## iter 60 value 75.769320
## iter 70 value 74.683998
## iter 80 value 74.588144
## iter 90 value 74.584793
## final value 74.584791
## converged## # weights: 173
## initial value 177.903821
## iter 10 value 53.221323
## iter 20 value 31.930402
## iter 30 value 23.294192
## iter 40 value 21.866337
## iter 50 value 21.634351
## iter 60 value 21.630367
## iter 70 value 21.630147
## iter 80 value 21.630122
## final value 21.630121
## converged## # weights: 285
## initial value 158.370076
## iter 10 value 42.112169
## iter 20 value 17.862839
## iter 30 value 14.999734
## iter 40 value 14.606922
## iter 50 value 14.586999
## iter 60 value 14.585070
## iter 70 value 14.584608
## final value 14.584605
## converged## # weights: 61
## initial value 158.977579
## iter 10 value 79.645828
## iter 20 value 69.605373
## iter 30 value 63.538684
## iter 40 value 57.772628
## iter 50 value 51.916474
## iter 60 value 50.222700
## iter 70 value 43.491975
## iter 80 value 38.277060
## iter 90 value 37.087200
## iter 100 value 33.975248
## final value 33.975248
## stopped after 100 iterations## # weights: 173
## initial value 171.964243
## iter 10 value 4.143890
## iter 20 value 0.252284
## iter 30 value 0.210309
## iter 40 value 0.171747
## iter 50 value 0.157922
## iter 60 value 0.146886
## iter 70 value 0.139749
## iter 80 value 0.131880
## iter 90 value 0.127725
## iter 100 value 0.125600
## final value 0.125600
## stopped after 100 iterations## # weights: 285
## initial value 144.369233
## iter 10 value 1.991644
## iter 20 value 0.258623
## iter 30 value 0.225269
## iter 40 value 0.190528
## iter 50 value 0.162643
## iter 60 value 0.145515
## iter 70 value 0.131163
## iter 80 value 0.122133
## iter 90 value 0.115341
## iter 100 value 0.106529
## final value 0.106529
## stopped after 100 iterations## # weights: 61
## initial value 185.739781
## iter 10 value 100.131985
## iter 20 value 88.685459
## iter 30 value 80.186646
## iter 40 value 68.009774
## iter 50 value 63.089178
## iter 60 value 61.321048
## iter 70 value 55.090263
## iter 80 value 48.137609
## iter 90 value 47.591774
## iter 100 value 46.352360
## final value 46.352360
## stopped after 100 iterations## # weights: 173
## initial value 164.681649
## iter 10 value 16.281042
## iter 20 value 9.030899
## iter 30 value 5.100376
## iter 40 value 2.743239
## iter 50 value 2.048311
## iter 60 value 1.487361
## iter 70 value 1.436492
## iter 80 value 0.127605
## iter 90 value 0.017266
## iter 100 value 0.014042
## final value 0.014042
## stopped after 100 iterations## # weights: 285
## initial value 163.046619
## iter 10 value 10.444513
## iter 20 value 5.017085
## iter 30 value 4.835442
## iter 40 value 4.718727
## iter 50 value 4.537900
## iter 60 value 4.506500
## iter 70 value 4.200014
## iter 80 value 4.192701
## iter 90 value 4.191377
## iter 100 value 3.823814
## final value 3.823814
## stopped after 100 iterations## # weights: 61
## initial value 168.258759
## iter 10 value 97.881051
## iter 20 value 87.911402
## iter 30 value 80.781523
## iter 40 value 77.631189
## iter 50 value 76.835749
## iter 60 value 76.528393
## final value 76.526459
## converged## # weights: 173
## initial value 159.952198
## iter 10 value 50.929826
## iter 20 value 26.112167
## iter 30 value 21.599042
## iter 40 value 21.478137
## iter 50 value 21.475934
## final value 21.475927
## converged## # weights: 285
## initial value 187.654555
## iter 10 value 41.347728
## iter 20 value 17.217696
## iter 30 value 15.791945
## iter 40 value 15.721278
## iter 50 value 15.719045
## iter 60 value 15.719029
## iter 60 value 15.719028
## iter 60 value 15.719028
## final value 15.719028
## converged## # weights: 61
## initial value 175.956174
## iter 10 value 93.905512
## iter 20 value 86.379059
## iter 30 value 83.296046
## iter 40 value 82.469785
## iter 50 value 82.361880
## iter 60 value 81.510690
## iter 70 value 80.135544
## iter 80 value 77.876279
## iter 90 value 77.567852
## iter 100 value 76.782609
## final value 76.782609
## stopped after 100 iterations## # weights: 173
## initial value 154.225042
## iter 10 value 18.628444
## iter 20 value 10.082070
## iter 30 value 7.508538
## iter 40 value 7.118870
## iter 50 value 5.959463
## iter 60 value 4.139778
## iter 70 value 3.816143
## iter 80 value 3.777468
## iter 90 value 0.612534
## iter 100 value 0.393225
## final value 0.393225
## stopped after 100 iterations## # weights: 285
## initial value 152.251276
## iter 10 value 4.934961
## iter 20 value 0.500935
## iter 30 value 0.385101
## iter 40 value 0.303955
## iter 50 value 0.242761
## iter 60 value 0.200050
## iter 70 value 0.175976
## iter 80 value 0.158231
## iter 90 value 0.139712
## iter 100 value 0.125464
## final value 0.125464
## stopped after 100 iterations## # weights: 61
## initial value 176.144881
## iter 10 value 80.052419
## iter 20 value 76.998464
## iter 30 value 72.243016
## iter 40 value 71.505542
## iter 50 value 70.569363
## iter 60 value 70.345556
## iter 70 value 66.875962
## iter 80 value 60.749580
## iter 90 value 59.757400
## iter 100 value 59.157939
## final value 59.157939
## stopped after 100 iterations## # weights: 173
## initial value 170.716106
## iter 10 value 20.470519
## iter 20 value 13.884539
## iter 30 value 13.070904
## iter 40 value 12.949575
## iter 50 value 10.987055
## iter 60 value 10.968173
## iter 70 value 10.967614
## iter 80 value 10.966661
## iter 90 value 10.966252
## iter 100 value 10.388195
## final value 10.388195
## stopped after 100 iterations## # weights: 285
## initial value 165.550878
## iter 10 value 1.646467
## iter 20 value 0.035408
## iter 30 value 0.001713
## final value 0.000054
## converged## # weights: 61
## initial value 168.533041
## iter 10 value 98.422620
## iter 20 value 84.059195
## iter 30 value 78.873148
## iter 40 value 77.550120
## iter 50 value 76.734739
## iter 60 value 76.505153
## iter 70 value 76.471337
## iter 70 value 76.471336
## iter 70 value 76.471336
## final value 76.471336
## converged## # weights: 173
## initial value 178.911576
## iter 10 value 47.748954
## iter 20 value 24.834547
## iter 30 value 21.923320
## iter 40 value 20.312850
## iter 50 value 20.302248
## final value 20.302244
## converged## # weights: 285
## initial value 173.787961
## iter 10 value 34.096413
## iter 20 value 16.336862
## iter 30 value 15.259726
## iter 40 value 15.241880
## iter 50 value 15.241692
## final value 15.241691
## converged## # weights: 61
## initial value 154.982911
## iter 10 value 90.431291
## iter 20 value 72.624093
## iter 30 value 60.059793
## iter 40 value 46.400846
## iter 50 value 44.434952
## iter 60 value 44.364107
## iter 70 value 43.014331
## iter 80 value 42.416956
## iter 90 value 41.979512
## iter 100 value 41.939256
## final value 41.939256
## stopped after 100 iterations## # weights: 173
## initial value 181.593810
## iter 10 value 21.243423
## iter 20 value 5.461163
## iter 30 value 4.578160
## iter 40 value 3.990025
## iter 50 value 3.361852
## iter 60 value 3.169421
## iter 70 value 3.124572
## iter 80 value 2.991250
## iter 90 value 1.647965
## iter 100 value 0.562464
## final value 0.562464
## stopped after 100 iterations## # weights: 285
## initial value 158.471502
## iter 10 value 6.290362
## iter 20 value 0.594265
## iter 30 value 0.229259
## iter 40 value 0.213859
## iter 50 value 0.180720
## iter 60 value 0.149444
## iter 70 value 0.134028
## iter 80 value 0.121229
## iter 90 value 0.107849
## iter 100 value 0.098468
## final value 0.098468
## stopped after 100 iterations## # weights: 61
## initial value 152.596423
## iter 10 value 97.146504
## iter 20 value 80.331400
## iter 30 value 75.835558
## iter 40 value 74.593698
## iter 50 value 59.343652
## iter 60 value 58.846460
## iter 70 value 58.367422
## iter 80 value 55.245773
## iter 90 value 49.384740
## iter 100 value 47.576513
## final value 47.576513
## stopped after 100 iterations## # weights: 173
## initial value 164.266365
## iter 10 value 42.845773
## iter 20 value 15.182140
## iter 30 value 5.411020
## iter 40 value 3.631922
## iter 50 value 3.385513
## iter 60 value 0.881148
## iter 70 value 0.016166
## iter 80 value 0.003474
## iter 90 value 0.001296
## iter 100 value 0.000443
## final value 0.000443
## stopped after 100 iterations## # weights: 285
## initial value 183.611025
## iter 10 value 5.699622
## iter 20 value 0.660412
## iter 30 value 0.039878
## iter 40 value 0.001958
## iter 50 value 0.000776
## iter 60 value 0.000323
## final value 0.000100
## converged## # weights: 61
## initial value 152.892016
## iter 10 value 100.504958
## iter 20 value 91.295990
## iter 30 value 80.336809
## iter 40 value 77.444098
## iter 50 value 76.613235
## iter 60 value 76.123749
## iter 70 value 75.203084
## iter 80 value 75.091379
## final value 75.091018
## converged## # weights: 173
## initial value 181.155319
## iter 10 value 52.293355
## iter 20 value 29.002610
## iter 30 value 21.497085
## iter 40 value 20.774507
## iter 50 value 20.759870
## final value 20.759766
## converged## # weights: 285
## initial value 182.942165
## iter 10 value 23.303390
## iter 20 value 16.455853
## iter 30 value 14.965169
## iter 40 value 14.933534
## iter 50 value 14.932937
## final value 14.932927
## converged## # weights: 61
## initial value 152.112862
## iter 10 value 100.840692
## iter 20 value 87.933526
## iter 30 value 84.273317
## iter 40 value 84.001979
## iter 50 value 83.284773
## iter 60 value 82.765192
## iter 70 value 82.533187
## iter 80 value 82.503470
## iter 90 value 82.478221
## iter 100 value 82.470726
## final value 82.470726
## stopped after 100 iterations## # weights: 173
## initial value 176.061411
## iter 10 value 29.831605
## iter 20 value 13.632853
## iter 30 value 7.678896
## iter 40 value 6.464520
## iter 50 value 6.193144
## iter 60 value 3.908309
## iter 70 value 0.655366
## iter 80 value 0.464065
## iter 90 value 0.362047
## iter 100 value 0.332728
## final value 0.332728
## stopped after 100 iterations## # weights: 285
## initial value 167.366191
## iter 10 value 22.043206
## iter 20 value 4.591810
## iter 30 value 0.294987
## iter 40 value 0.248543
## iter 50 value 0.204776
## iter 60 value 0.160519
## iter 70 value 0.139508
## iter 80 value 0.129739
## iter 90 value 0.121147
## iter 100 value 0.114373
## final value 0.114373
## stopped after 100 iterations## # weights: 61
## initial value 151.538357
## iter 10 value 95.145970
## iter 20 value 87.007897
## iter 30 value 74.259807
## iter 40 value 63.161318
## iter 50 value 56.457269
## iter 60 value 46.952783
## iter 70 value 37.366442
## iter 80 value 24.575069
## iter 90 value 23.446003
## iter 100 value 20.864844
## final value 20.864844
## stopped after 100 iterations## # weights: 173
## initial value 156.255265
## iter 10 value 16.375364
## iter 20 value 7.555686
## iter 30 value 6.014267
## iter 40 value 3.880816
## iter 50 value 1.996363
## iter 60 value 0.359806
## iter 70 value 0.105881
## iter 80 value 0.041067
## iter 90 value 0.007565
## iter 100 value 0.001895
## final value 0.001895
## stopped after 100 iterations## # weights: 285
## initial value 170.504972
## iter 10 value 10.305700
## iter 20 value 0.265711
## iter 30 value 0.010238
## iter 40 value 0.000988
## iter 50 value 0.000160
## iter 50 value 0.000095
## iter 50 value 0.000095
## final value 0.000095
## converged## # weights: 61
## initial value 155.301041
## iter 10 value 104.209286
## iter 20 value 93.180204
## iter 30 value 87.238719
## iter 40 value 81.380044
## iter 50 value 78.446421
## iter 60 value 77.356302
## iter 70 value 77.185626
## iter 80 value 77.146359
## iter 90 value 77.138950
## final value 77.138740
## converged## # weights: 173
## initial value 174.835526
## iter 10 value 61.832235
## iter 20 value 26.708348
## iter 30 value 22.025578
## iter 40 value 21.804362
## iter 50 value 21.797799
## iter 60 value 21.797433
## final value 21.797432
## converged## # weights: 285
## initial value 180.312494
## iter 10 value 25.892895
## iter 20 value 16.682215
## iter 30 value 15.441749
## iter 40 value 15.409908
## iter 50 value 15.409198
## iter 60 value 15.409162
## final value 15.409159
## converged## # weights: 61
## initial value 173.524504
## iter 10 value 100.180055
## iter 20 value 85.863469
## iter 30 value 76.464732
## iter 40 value 74.004175
## iter 50 value 73.969527
## iter 60 value 73.327741
## iter 70 value 72.507076
## iter 80 value 72.481229
## iter 90 value 72.456244
## iter 100 value 72.433468
## final value 72.433468
## stopped after 100 iterations## # weights: 173
## initial value 158.253694
## iter 10 value 0.600775
## iter 20 value 0.320168
## iter 30 value 0.249846
## iter 40 value 0.202969
## iter 50 value 0.155084
## iter 60 value 0.145414
## iter 70 value 0.136692
## iter 80 value 0.129068
## iter 90 value 0.121330
## iter 100 value 0.117054
## final value 0.117054
## stopped after 100 iterations## # weights: 285
## initial value 165.218775
## iter 10 value 8.847187
## iter 20 value 0.673926
## iter 30 value 0.396835
## iter 40 value 0.324725
## iter 50 value 0.232913
## iter 60 value 0.199662
## iter 70 value 0.168929
## iter 80 value 0.154281
## iter 90 value 0.137098
## iter 100 value 0.122816
## final value 0.122816
## stopped after 100 iterations## # weights: 61
## initial value 168.917515
## iter 10 value 85.030677
## iter 20 value 78.092233
## iter 30 value 76.146014
## iter 40 value 75.612529
## iter 50 value 75.067655
## iter 60 value 74.762911
## iter 70 value 73.153685
## iter 80 value 71.462631
## iter 90 value 69.513328
## iter 100 value 68.448658
## final value 68.448658
## stopped after 100 iterations## # weights: 173
## initial value 154.662220
## iter 10 value 38.887882
## iter 20 value 29.520830
## iter 30 value 25.279568
## iter 40 value 17.945406
## iter 50 value 14.377508
## iter 60 value 11.486017
## iter 70 value 8.594312
## iter 80 value 6.427129
## iter 90 value 6.255335
## iter 100 value 6.071314
## final value 6.071314
## stopped after 100 iterations## # weights: 285
## initial value 157.387523
## iter 10 value 15.777879
## iter 20 value 0.725903
## iter 30 value 0.031018
## iter 40 value 0.001947
## iter 50 value 0.000207
## final value 0.000097
## converged## # weights: 61
## initial value 155.264809
## iter 10 value 96.289761
## iter 20 value 90.050857
## iter 30 value 83.206429
## iter 40 value 78.905580
## iter 50 value 78.507117
## iter 60 value 78.456857
## iter 70 value 78.456597
## iter 70 value 78.456597
## iter 70 value 78.456597
## final value 78.456597
## converged## # weights: 173
## initial value 159.649188
## iter 10 value 53.235785
## iter 20 value 25.320060
## iter 30 value 20.856141
## iter 40 value 20.607971
## iter 50 value 20.600419
## iter 60 value 20.596855
## iter 70 value 20.596360
## final value 20.596351
## converged## # weights: 285
## initial value 221.191543
## iter 10 value 24.473271
## iter 20 value 16.102599
## iter 30 value 14.389098
## iter 40 value 14.289203
## iter 50 value 14.288217
## final value 14.288198
## converged## # weights: 61
## initial value 153.497294
## iter 10 value 93.591247
## iter 20 value 80.739354
## iter 30 value 75.553703
## iter 40 value 70.308870
## iter 50 value 67.521443
## iter 60 value 66.634863
## iter 70 value 65.681519
## iter 80 value 65.319427
## iter 90 value 64.200223
## iter 100 value 63.970235
## final value 63.970235
## stopped after 100 iterations## # weights: 173
## initial value 142.953160
## iter 10 value 12.170522
## iter 20 value 7.317014
## iter 30 value 3.121058
## iter 40 value 0.916861
## iter 50 value 0.396326
## iter 60 value 0.322398
## iter 70 value 0.282446
## iter 80 value 0.252184
## iter 90 value 0.232564
## iter 100 value 0.212108
## final value 0.212108
## stopped after 100 iterations## # weights: 285
## initial value 168.001685
## iter 10 value 15.693436
## iter 20 value 0.319313
## iter 30 value 0.229420
## iter 40 value 0.174021
## iter 50 value 0.131862
## iter 60 value 0.112572
## iter 70 value 0.098181
## iter 80 value 0.089496
## iter 90 value 0.081635
## iter 100 value 0.074656
## final value 0.074656
## stopped after 100 iterations## # weights: 61
## initial value 163.269386
## iter 10 value 93.339421
## iter 20 value 87.237625
## iter 30 value 81.051426
## iter 40 value 78.424868
## iter 50 value 77.477741
## iter 60 value 76.967429
## iter 70 value 76.753332
## iter 80 value 76.734326
## iter 90 value 76.705047
## iter 100 value 76.649845
## final value 76.649845
## stopped after 100 iterations## # weights: 173
## initial value 179.133159
## iter 10 value 13.592558
## iter 20 value 5.519268
## iter 30 value 0.209095
## iter 40 value 0.010335
## iter 50 value 0.001062
## iter 60 value 0.000130
## final value 0.000086
## converged## # weights: 285
## initial value 154.636761
## iter 10 value 5.832823
## iter 20 value 3.189041
## iter 30 value 2.660431
## iter 40 value 2.319176
## iter 50 value 1.456483
## iter 60 value 1.395865
## iter 70 value 1.361660
## iter 80 value 0.011563
## iter 90 value 0.002645
## iter 100 value 0.000698
## final value 0.000698
## stopped after 100 iterations## # weights: 61
## initial value 158.623288
## iter 10 value 97.374745
## iter 20 value 85.873613
## iter 30 value 81.424567
## iter 40 value 78.217919
## iter 50 value 76.644533
## iter 60 value 75.546027
## iter 70 value 74.580073
## iter 80 value 74.297534
## iter 90 value 74.292442
## iter 90 value 74.292442
## iter 90 value 74.292442
## final value 74.292442
## converged## # weights: 173
## initial value 165.726546
## iter 10 value 50.767216
## iter 20 value 26.491061
## iter 30 value 22.972039
## iter 40 value 22.673970
## iter 50 value 22.670281
## iter 60 value 22.670252
## final value 22.670251
## converged## # weights: 285
## initial value 188.516094
## iter 10 value 48.771547
## iter 20 value 19.082938
## iter 30 value 15.196241
## iter 40 value 15.035450
## iter 50 value 15.030713
## final value 15.030694
## converged## # weights: 61
## initial value 156.777933
## iter 10 value 99.629089
## iter 20 value 83.447127
## iter 30 value 80.477081
## iter 40 value 79.414151
## iter 50 value 78.648222
## iter 60 value 74.022091
## iter 70 value 72.095488
## iter 80 value 71.773366
## iter 90 value 71.745347
## iter 100 value 70.330294
## final value 70.330294
## stopped after 100 iterations## # weights: 173
## initial value 166.532509
## iter 10 value 13.273560
## iter 20 value 5.939124
## iter 30 value 3.141337
## iter 40 value 3.120037
## iter 50 value 3.101699
## iter 60 value 3.036601
## iter 70 value 2.896687
## iter 80 value 2.887211
## iter 90 value 0.191384
## iter 100 value 0.164419
## final value 0.164419
## stopped after 100 iterations## # weights: 285
## initial value 188.833982
## iter 10 value 2.039064
## iter 20 value 0.248499
## iter 30 value 0.220179
## iter 40 value 0.189681
## iter 50 value 0.155774
## iter 60 value 0.132979
## iter 70 value 0.110766
## iter 80 value 0.096268
## iter 90 value 0.089342
## iter 100 value 0.082904
## final value 0.082904
## stopped after 100 iterations## # weights: 173
## initial value 177.414702
## iter 10 value 55.551801
## iter 20 value 28.050933
## iter 30 value 23.921467
## iter 40 value 22.580605
## iter 50 value 22.337280
## iter 60 value 22.322894
## iter 70 value 22.321498
## iter 80 value 22.321475
## final value 22.321474
## convergedresultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$m1_consideration)
mcre5## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 8 0 0 0 0
## 2 0 12 0 0 0
## 3 0 0 27 0 0
## 4 0 0 0 28 0
## 5 0 0 0 0 33
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9664, 1)
## No Information Rate : 0.3056
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 1.00000 1.0000 1.00 1.0000 1.0000
## Specificity 1.00000 1.0000 1.00 1.0000 1.0000
## Pos Pred Value 1.00000 1.0000 1.00 1.0000 1.0000
## Neg Pred Value 1.00000 1.0000 1.00 1.0000 1.0000
## Prevalence 0.07407 0.1111 0.25 0.2593 0.3056
## Detection Rate 0.07407 0.1111 0.25 0.2593 0.3056
## Detection Prevalence 0.07407 0.1111 0.25 0.2593 0.3056
## Balanced Accuracy 1.00000 1.0000 1.00 1.0000 1.0000# Matriz de Confusión del Resultado de la Prueba
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$m1_consideration)
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 1 0 0 0 0
## 2 0 3 0 0 0
## 3 0 0 6 0 0
## 4 0 0 0 6 0
## 5 1 0 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.96
## 95% CI : (0.7965, 0.999)
## No Information Rate : 0.32
## P-Value [Acc > NIR] : 2.302e-11
##
## Kappa : 0.9468
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.5000 1.00 1.00 1.00 1.0000
## Specificity 1.0000 1.00 1.00 1.00 0.9412
## Pos Pred Value 1.0000 1.00 1.00 1.00 0.8889
## Neg Pred Value 0.9583 1.00 1.00 1.00 1.0000
## Prevalence 0.0800 0.12 0.24 0.24 0.3200
## Detection Rate 0.0400 0.12 0.24 0.24 0.3200
## Detection Prevalence 0.0400 0.12 0.24 0.24 0.3600
## Balanced Accuracy 0.7500 1.00 1.00 1.00 0.9706Modelo 6. Bosques Aleatorios
modelo6 <- train(m1_consideration ~ ., 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, domainAgriculture
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainLaw
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainLaw
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusUnemployed,
## domainAgriculture, domainLaw## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## domainAgriculture, domainEconomics
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## domainAgriculture, domainEconomics
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacHp, user_pcmacOther,
## domainAgriculture, domainEconomics## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainCommunication## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture,
## domainLogistics## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture, domainRealestate, domainRetail
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture, domainRealestate, domainRetail
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusStudent ant
## employed, domainAgriculture, domainRealestate, domainRetail## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgriculture## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## domainAgriculture, domainConsulting , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## domainAgriculture, domainConsulting , domainRetired
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, statusRetired,
## domainAgriculture, domainConsulting , domainRetired## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut =
## 10, : These variables have zero variances: user_pcmacOther, domainAgricultureresultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$m1_consideration)
mcre6## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 8 0 0 0 0
## 2 0 12 0 0 0
## 3 0 0 27 0 0
## 4 0 0 0 28 0
## 5 0 0 0 0 33
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9664, 1)
## No Information Rate : 0.3056
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 1.00000 1.0000 1.00 1.0000 1.0000
## Specificity 1.00000 1.0000 1.00 1.0000 1.0000
## Pos Pred Value 1.00000 1.0000 1.00 1.0000 1.0000
## Neg Pred Value 1.00000 1.0000 1.00 1.0000 1.0000
## Prevalence 0.07407 0.1111 0.25 0.2593 0.3056
## Detection Rate 0.07407 0.1111 0.25 0.2593 0.3056
## Detection Prevalence 0.07407 0.1111 0.25 0.2593 0.3056
## Balanced Accuracy 1.00000 1.0000 1.00 1.0000 1.0000# Matriz de Confusión del Resultado de la Prueba
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$m1_consideration)
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5
## 1 0 0 0 0 0
## 2 0 3 0 0 0
## 3 0 0 6 0 0
## 4 1 0 0 6 0
## 5 1 0 0 0 8
##
## Overall Statistics
##
## Accuracy : 0.92
## 95% CI : (0.7397, 0.9902)
## No Information Rate : 0.32
## P-Value [Acc > NIR] : 5.992e-10
##
## Kappa : 0.8927
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5
## Sensitivity 0.00 1.00 1.00 1.0000 1.0000
## Specificity 1.00 1.00 1.00 0.9474 0.9412
## Pos Pred Value NaN 1.00 1.00 0.8571 0.8889
## Neg Pred Value 0.92 1.00 1.00 1.0000 1.0000
## Prevalence 0.08 0.12 0.24 0.2400 0.3200
## Detection Rate 0.00 0.12 0.24 0.2400 0.3200
## Detection Prevalence 0.00 0.12 0.24 0.2800 0.3600
## Balanced Accuracy 0.50 1.00 1.00 0.9737 0.9706Tabla 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 rf
## Precisión de entrenamiento 1.00 0.9722222 1.00 0.9259259 1.00 1.00
## Precisión de\nprueba 0.92 0.8400000 0.92 0.9200000 0.96 0.92Conclusiones
*SVM lineal: Mostró excelente desempeño, con 1.00 en entrenamiento y 0.92 en prueba, lo que refleja muy buena capacidad de generalización.
*SVM radial: Tuvo alta precisión en entrenamiento (0.97) y bajó a 0.84 en prueba, indicando una ligera pérdida de generalización.
*SVM polinómico: Alcanzó 1.00 en entrenamiento y 0.92 en prueba, mostrandonos una menor capacidad de generalización.
*rpart (árbol de decisión): Logró 0.93 en entrenamiento y mantuvo 0.92 en prueba, mostrando buen equilibrio y estabilidad.
*nnet (red neuronal): Obtuvo 1.00 en entrenamiento y 0.96 en prueba, lo que indica consistencia y excelente rendimiento.
*Random Forest: Llegó a 1.00 en entrenamiento y bajó a 0.92 en prueba, sugiriendo un leve sobreajuste pero aún con muy buen desempeño.
---
title: "M1"
author: "Giuliana Mancera Flores - A00840416"
date: "01.03.2026"
output:
  html_document:
    toc: TRUE
    toc_float: TRUE
    code_download: TRUE
    theme: yeti
---
<center>
![](https://www.mps.com.co/uploads/blog/articulos/20/apple%20aaa.jpg)
</center>
# <span style="color: blue"> Teoría </span>
El paquete **CARET (Classification And REgression Training)** es un
paquete integral con una amplia variedad de algoritmos para el aprendizaje
automático.
# <span style="color: blue"> Instalar paquetes y llamar librerías </span>
```{r}
# install.packages("ggplot2") # Gráficas
library(ggplot2)
# install.packages("lattice") # Crear gráficos
library(lattice)
# install.packages("caret") # Algoritmos de aprendizaje automático
library(caret)
# install.packages("datasets") # Usar bases de datos, en este caso Iris
library(datasets)
# install.packages("DataExplorer") # Análisis Exploratorio
library(DataExplorer)
```
# <span style="color: blue"> Crear la base de datos </span>
```{r}
#file.choose()
M1 <- read.csv("/Users/giuliana/Downloads/M1_data.csv")
df <- data.frame(M1)
head(df)
```
```{r}
nzv <- nearZeroVar(df) 
if(length(nzv) > 0) df <- df[ , -nzv]
```

```{r}
#Variable objetivo a factor
df$m1_purchase <- as.factor(df$m1_consideration)
```


# <span style="color: blue"> Entender la base de datos </span>
```{r}
summary(df)
str(df)
# create_report(df)
plot_missing(df)
plot_histogram(df)
plot_correlation(df)
```
**NOTA: La variable que queremos predecir debe tener formato de FACTOR**
# <span style="color: blue"> Partir la base de datos </span>
```{r}
# Convertir todas las columnas categóricas en factor 
cols_factor <- c("trust_apple","user_pcmac","familiarity_m1","m1_consideration", "m1_purchase","gender","status","domain") 
df[cols_factor] <- lapply(df[cols_factor], factor)

set.seed(123)

# Normalmente 80-20
renglones_entrenamiento <- createDataPartition(df$m1_consideration, p=0.8,
list=FALSE)
entrenamiento <- df[renglones_entrenamiento, ]
prueba <- df[-renglones_entrenamiento, ]


# Ajustar niveles de factores en prueba para entrenamiento 
prueba$status <- factor(prueba$status, 
                        levels = levels(entrenamiento$status))
prueba <- droplevels(prueba)
```
# <span style="color: blue"> Distintos tipos de Métodos para Modelar
</span>
Los métodos más utilizados para modelar aprendizaje automático son:
* **SVM**: *Support Vector Machine* o Máquina de Vectores de Soporte. Hay
varios subtipos: Lineal (svmLinear), Radial (svmRadial), Polinómico
(svmPoly), etc.
* **Árbol de Decisión**: rpart
* **Redes Neuronales**: nnet
* **Random Forest** o Bosques Aleatorios: rf
# <span style="color: blue"> Modelo 1. SVM Lineal </span>
```{r}
modelo1 <- train(m1_consideration ~ ., data=entrenamiento,
                 method = "svmLinear",
                 preProcess = c("scale", "center"),
                 trControl = trainControl(method="cv", number=10),
                 tuneGrid = data.frame(C=1)
                 )
resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$m1_consideration)
mcre1
# Matriz de Confusión del Resultado de la Prueba
mcrp1 <- confusionMatrix(resultado_prueba1, prueba$m1_consideration)
mcrp1
```
# <span style="color: blue"> Modelo 2. SVM Radial </span>
```{r}
modelo2 <- train(m1_consideration ~ ., data=entrenamiento,
method = "svmRadial", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(sigma=1, C=1) #Cambiar
)
resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$m1_consideration)
mcre2
# Matriz de Confusión del Resultado de la Prueba
mcrp2 <- confusionMatrix(resultado_prueba2, prueba$m1_consideration)
mcrp2
```
# <span style="color: blue"> Modelo 3. SVM Polinómico </span>
```{r}
modelo3 <- train(m1_purchase ~ ., data=entrenamiento,
method = "svmPoly", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(degree=1, scale=1, C=1) #Cambiar
)
resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$m1_purchase)
mcre3
# Matriz de Confusión del Resultado de la Prueba
mcrp3 <- confusionMatrix(resultado_prueba3, prueba$m1_purchase)
mcrp3
```
# <span style="color: blue"> Modelo 4. Árbol de Decisión </span>
```{r}
modelo4 <- train(m1_consideration ~ ., data=entrenamiento,
method = "rpart", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneLength = 10 #Cambiar
)
resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$m1_consideration)
mcre4
# Matriz de Confusión del Resultado de la Prueba
mcrp4 <- confusionMatrix(resultado_prueba4, prueba$m1_consideration)
mcrp4
```
# <span style="color: blue"> Modelo 5. Redes Neuronales </span>
```{r message=FALSE, warning=FALSE}
modelo5 <- train(m1_consideration ~ ., data=entrenamiento,
method = "nnet", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10)
#Cambiar
)
resultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$m1_consideration)
mcre5
# Matriz de Confusión del Resultado de la Prueba
mcrp5 <- confusionMatrix(resultado_prueba5, prueba$m1_consideration)
mcrp5
```
# <span style="color: blue"> Modelo 6. Bosques Aleatorios </span>
```{r}
modelo6 <- train(m1_consideration ~ ., data=entrenamiento,
method = "rf", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = expand.grid(mtry = c(2,4,6)) #Cambiar
)
resultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)
# Matriz de Confusión
# Es una tabla de evaluación que desglosa el rendimiento del modelo de clasificación.
# Matriz de Confusión del Resultado del Entrenamiento
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$m1_consideration)
mcre6
# Matriz de Confusión del Resultado de la Prueba
mcrp6 <- confusionMatrix(resultado_prueba6, prueba$m1_consideration)
mcrp6
```
# <span style="color: blue"> Tabla de Resultados </span>
```{r}
resultados <- data.frame(
"svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
"svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"rf" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisión de entrenamiento", "Precisión de
prueba")
resultados
```
# <span style="color: blue"> Conclusiones </span>

*SVM lineal: Mostró excelente desempeño, con 1.00 en entrenamiento y 0.92 en prueba, lo que refleja muy buena capacidad de generalización.

*SVM radial: Tuvo alta precisión en entrenamiento (0.97) y bajó a 0.84 en prueba, indicando una ligera pérdida de generalización.

*SVM polinómico: Alcanzó 1.00 en entrenamiento y 0.92 en prueba, mostrandonos una menor capacidad de generalización.

*rpart (árbol de decisión): Logró 0.93 en entrenamiento y mantuvo 0.92 en prueba, mostrando buen equilibrio y estabilidad.

*nnet (red neuronal): Obtuvo 1.00 en entrenamiento y 0.96 en prueba, lo que indica consistencia y excelente rendimiento.

*Random Forest: Llegó a 1.00 en entrenamiento y bajó a 0.92 en prueba, sugiriendo un leve sobreajuste pero aún con muy buen desempeño.
