SVM M1
Dahir Torres A01571601
2026-02-27
output: html_document: toc: TRUE toc_float: TRUE code_download: TRUE theme: cosmo —
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
df <- read.csv("C:\\Users\\Torres\\Downloads\\M1_data.csv")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 Length:133
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 Class :character
## Median :4.000 Median :4.000 Median :4.000 Mode :character
## Mean :3.466 Mean :3.376 Mean :3.609
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000
## 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 : chr "Yes" "No" "Yes" "No" ...
## $ 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
df$user_pcmac <- factor(df$user_pcmac)
df$m1_purchase <- factor(df$m1_purchase)
df$trust_apple <- factor(df$trust_apple)
df$familiarity_m1 <- factor(df$familiarity_m1)
df$gender <- factor(df$gender)
df$status <- factor(df$status)
df$domain <- factor(df$domain)Cambiar a factor las variables
df$user_pcmac <- factor(df$user_pcmac)
df$m1_purchase <- factor(df$m1_purchase)
df$trust_apple <- factor(df$trust_apple)
df$familiarity_m1 <- factor(df$familiarity_m1)
df$gender <- factor(df$gender)
df$status <- factor(df$status)
df$domain <- factor(df$domain)Partir la base de datos
# Normalmente 80-20
set.seed(123)
renglones_entrenamiento <- createDataPartition(df$m1_purchase, p=0.8,
list=FALSE)
entrenamiento <- df[renglones_entrenamiento, ]
prueba <- df[-renglones_entrenamiento, ]Modelo 1. SVM Lineal
modelo1 <- train(m1_purchase ~ ., data=entrenamiento,
method = "svmLinear", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = data.frame(C=1) #Cambiar
)
resultado_entrenamiento1 <- predict(modelo1, entrenamiento)
resultado_prueba1 <- predict(modelo1, prueba)
mcre1 <- confusionMatrix(resultado_entrenamiento1, entrenamiento$m1_purchase)
mcre1## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 30 4
## Yes 6 67
##
## Accuracy : 0.9065
## 95% CI : (0.8348, 0.9543)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 4.281e-09
##
## Kappa : 0.7878
##
## Mcnemar's Test P-Value : 0.7518
##
## Sensitivity : 0.8333
## Specificity : 0.9437
## Pos Pred Value : 0.8824
## Neg Pred Value : 0.9178
## Prevalence : 0.3364
## Detection Rate : 0.2804
## Detection Prevalence : 0.3178
## Balanced Accuracy : 0.8885
##
## 'Positive' Class : No
## mcrp1 <- confusionMatrix(resultado_prueba1, prueba$m1_purchase)
mcrp1## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 3 6
## Yes 6 11
##
## Accuracy : 0.5385
## 95% CI : (0.3337, 0.7341)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.9231
##
## Kappa : -0.0196
##
## Mcnemar's Test P-Value : 1.0000
##
## Sensitivity : 0.3333
## Specificity : 0.6471
## Pos Pred Value : 0.3333
## Neg Pred Value : 0.6471
## Prevalence : 0.3462
## Detection Rate : 0.1154
## Detection Prevalence : 0.3462
## Balanced Accuracy : 0.4902
##
## 'Positive' Class : No
## Modelo 2. SVM Radial
modelo2 <- train(m1_purchase ~ ., data=entrenamiento,
method = "svmRadial", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(sigma=1, C=1) #Cambiar
)
resultado_entrenamiento2 <- predict(modelo2, entrenamiento)
resultado_prueba2 <- predict(modelo2, prueba)
mcre2 <- confusionMatrix(resultado_entrenamiento2, entrenamiento$m1_purchase)
mcre2## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 26 0
## Yes 10 71
##
## Accuracy : 0.9065
## 95% CI : (0.8348, 0.9543)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 4.281e-09
##
## Kappa : 0.7753
##
## Mcnemar's Test P-Value : 0.004427
##
## Sensitivity : 0.7222
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.8765
## Prevalence : 0.3364
## Detection Rate : 0.2430
## Detection Prevalence : 0.2430
## Balanced Accuracy : 0.8611
##
## 'Positive' Class : No
## mcrp2 <- confusionMatrix(resultado_prueba2, prueba$m1_purchase)
mcrp2## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 1 4
## Yes 8 13
##
## Accuracy : 0.5385
## 95% CI : (0.3337, 0.7341)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.9231
##
## Kappa : -0.1387
##
## Mcnemar's Test P-Value : 0.3865
##
## Sensitivity : 0.11111
## Specificity : 0.76471
## Pos Pred Value : 0.20000
## Neg Pred Value : 0.61905
## Prevalence : 0.34615
## Detection Rate : 0.03846
## Detection Prevalence : 0.19231
## Balanced Accuracy : 0.43791
##
## 'Positive' Class : No
## Modelo 3. SVM Polinómico
modelo3 <- train(m1_purchase ~ ., data=entrenamiento,
method = "svmPoly", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGride = data.frame(degree=1, scale=1, C=1) #Cambiar
)
resultado_entrenamiento3 <- predict(modelo3, entrenamiento)
resultado_prueba3 <- predict(modelo3, prueba)
mcre3 <- confusionMatrix(resultado_entrenamiento3, entrenamiento$m1_purchase)
mcre3## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 29 4
## Yes 7 67
##
## Accuracy : 0.8972
## 95% CI : (0.8235, 0.9476)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 1.967e-08
##
## Kappa : 0.7649
##
## Mcnemar's Test P-Value : 0.5465
##
## Sensitivity : 0.8056
## Specificity : 0.9437
## Pos Pred Value : 0.8788
## Neg Pred Value : 0.9054
## Prevalence : 0.3364
## Detection Rate : 0.2710
## Detection Prevalence : 0.3084
## Balanced Accuracy : 0.8746
##
## 'Positive' Class : No
## mcrp3 <- confusionMatrix(resultado_prueba3, prueba$m1_purchase)
mcrp3## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 4 7
## Yes 5 10
##
## Accuracy : 0.5385
## 95% CI : (0.3337, 0.7341)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.9231
##
## Kappa : 0.0311
##
## Mcnemar's Test P-Value : 0.7728
##
## Sensitivity : 0.4444
## Specificity : 0.5882
## Pos Pred Value : 0.3636
## Neg Pred Value : 0.6667
## Prevalence : 0.3462
## Detection Rate : 0.1538
## Detection Prevalence : 0.4231
## Balanced Accuracy : 0.5163
##
## 'Positive' Class : No
## Modelo 4. Árbol de Decisión
modelo4 <- train(m1_purchase ~ ., data=entrenamiento,
method = "rpart", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneLength = 10 #Cambiar
)
resultado_entrenamiento4 <- predict(modelo4, entrenamiento)
resultado_prueba4 <- predict(modelo4, prueba)
mcre4 <- confusionMatrix(resultado_entrenamiento4, entrenamiento$m1_purchase)
mcre4## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 17 2
## Yes 19 69
##
## Accuracy : 0.8037
## 95% CI : (0.7158, 0.8742)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 0.0010139
##
## Kappa : 0.5025
##
## Mcnemar's Test P-Value : 0.0004803
##
## Sensitivity : 0.4722
## Specificity : 0.9718
## Pos Pred Value : 0.8947
## Neg Pred Value : 0.7841
## Prevalence : 0.3364
## Detection Rate : 0.1589
## Detection Prevalence : 0.1776
## Balanced Accuracy : 0.7220
##
## 'Positive' Class : No
## mcrp4 <- confusionMatrix(resultado_prueba4, prueba$m1_purchase)
mcrp4## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 4 6
## Yes 5 11
##
## Accuracy : 0.5769
## 95% CI : (0.3692, 0.7665)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.8485
##
## Kappa : 0.0892
##
## Mcnemar's Test P-Value : 1.0000
##
## Sensitivity : 0.4444
## Specificity : 0.6471
## Pos Pred Value : 0.4000
## Neg Pred Value : 0.6875
## Prevalence : 0.3462
## Detection Rate : 0.1538
## Detection Prevalence : 0.3846
## Balanced Accuracy : 0.5458
##
## 'Positive' Class : No
## Modelo 5. Redes Neuronales
modelo5 <- train(m1_purchase ~ ., data=entrenamiento,
method = "nnet", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10)
#Cambiar
)## # weights: 50
## initial value 74.247141
## iter 10 value 35.573297
## iter 20 value 26.955288
## iter 30 value 23.575587
## iter 40 value 21.351510
## iter 50 value 16.796235
## iter 60 value 16.743905
## iter 70 value 16.142754
## iter 80 value 15.212566
## iter 90 value 15.211433
## iter 100 value 15.211262
## final value 15.211262
## stopped after 100 iterations## # weights: 148
## initial value 60.994638
## iter 10 value 14.797393
## iter 20 value 6.726077
## iter 30 value 5.846459
## iter 40 value 5.335932
## iter 50 value 5.285527
## iter 60 value 5.279700
## iter 70 value 5.028168
## iter 80 value 5.025583
## iter 90 value 5.021837
## iter 100 value 4.707235
## final value 4.707235
## stopped after 100 iterations## # weights: 246
## initial value 63.088441
## iter 10 value 17.956727
## iter 20 value 3.111347
## iter 30 value 2.798906
## iter 40 value 2.773111
## iter 50 value 2.772634
## iter 60 value 2.772589
## iter 60 value 2.772589
## iter 60 value 2.772589
## final value 2.772589
## converged## # weights: 50
## initial value 63.156136
## iter 10 value 29.495525
## iter 20 value 22.340354
## iter 30 value 21.589614
## iter 40 value 21.584546
## final value 21.584544
## converged## # weights: 148
## initial value 68.046300
## iter 10 value 37.446921
## iter 20 value 18.090359
## iter 30 value 15.989113
## iter 40 value 15.620063
## iter 50 value 15.396680
## iter 60 value 15.279846
## iter 70 value 15.270745
## iter 80 value 15.270617
## final value 15.270616
## converged## # weights: 246
## initial value 72.789509
## iter 10 value 25.271723
## iter 20 value 15.439661
## iter 30 value 14.559762
## iter 40 value 14.236887
## iter 50 value 14.094303
## iter 60 value 14.067507
## iter 70 value 14.056102
## iter 80 value 14.051176
## iter 90 value 14.049230
## iter 100 value 14.048526
## final value 14.048526
## stopped after 100 iterations## # weights: 50
## initial value 66.619352
## iter 10 value 22.867635
## iter 20 value 11.433596
## iter 30 value 10.408121
## iter 40 value 10.394737
## iter 50 value 10.392697
## iter 60 value 10.391389
## iter 70 value 10.389948
## iter 80 value 10.389260
## iter 90 value 10.388650
## iter 100 value 10.388089
## final value 10.388089
## stopped after 100 iterations## # weights: 148
## initial value 70.831792
## iter 10 value 21.635375
## iter 20 value 13.882540
## iter 30 value 7.837304
## iter 40 value 6.894951
## iter 50 value 6.758358
## iter 60 value 6.735467
## iter 70 value 6.205625
## iter 80 value 5.695199
## iter 90 value 5.686045
## iter 100 value 4.943829
## final value 4.943829
## stopped after 100 iterations## # weights: 246
## initial value 71.178764
## iter 10 value 10.920175
## iter 20 value 3.173858
## iter 30 value 2.955508
## iter 40 value 2.924206
## iter 50 value 2.910129
## iter 60 value 2.895697
## iter 70 value 2.887648
## iter 80 value 2.879736
## iter 90 value 2.873228
## iter 100 value 2.868855
## final value 2.868855
## stopped after 100 iterations## # weights: 50
## initial value 70.461136
## iter 10 value 29.637622
## iter 20 value 24.896018
## iter 30 value 24.785136
## iter 40 value 23.849898
## iter 50 value 22.266883
## iter 60 value 22.028012
## iter 70 value 22.026220
## iter 80 value 22.024817
## iter 90 value 22.022883
## iter 100 value 22.022083
## final value 22.022083
## stopped after 100 iterations## # weights: 148
## initial value 83.038936
## iter 10 value 17.906396
## iter 20 value 9.024395
## iter 30 value 7.962449
## iter 40 value 7.588703
## iter 50 value 7.542751
## iter 60 value 7.533131
## iter 70 value 7.528144
## iter 80 value 7.526916
## iter 90 value 7.525144
## iter 100 value 7.524613
## final value 7.524613
## stopped after 100 iterations## # weights: 246
## initial value 97.089941
## iter 10 value 13.236253
## iter 20 value 7.027142
## iter 30 value 6.931576
## iter 40 value 6.931398
## iter 50 value 6.137821
## iter 60 value 6.137649
## final value 6.137648
## converged## # weights: 50
## initial value 63.592743
## iter 10 value 33.572146
## iter 20 value 23.660654
## iter 30 value 22.625912
## iter 40 value 22.606223
## iter 50 value 22.605796
## final value 22.605794
## converged## # weights: 148
## initial value 68.123293
## iter 10 value 27.334352
## iter 20 value 18.593081
## iter 30 value 17.534828
## iter 40 value 17.180908
## iter 50 value 17.102883
## iter 60 value 17.092293
## iter 70 value 17.090896
## iter 80 value 17.090635
## iter 90 value 17.090623
## final value 17.090623
## converged## # weights: 246
## initial value 62.664827
## iter 10 value 22.058294
## iter 20 value 17.701457
## iter 30 value 16.737544
## iter 40 value 15.736257
## iter 50 value 15.503372
## iter 60 value 15.470090
## iter 70 value 15.467216
## final value 15.467182
## converged## # weights: 50
## initial value 85.720549
## iter 10 value 29.959529
## iter 20 value 26.252869
## iter 30 value 24.738443
## iter 40 value 23.238898
## iter 50 value 23.212547
## iter 60 value 23.208734
## iter 70 value 23.207691
## iter 80 value 23.206458
## iter 90 value 23.205360
## iter 100 value 23.204799
## final value 23.204799
## stopped after 100 iterations## # weights: 148
## initial value 78.476532
## iter 10 value 22.218352
## iter 20 value 15.234602
## iter 30 value 14.602727
## iter 40 value 14.009794
## iter 50 value 13.417621
## iter 60 value 13.347010
## iter 70 value 12.991732
## iter 80 value 12.784718
## iter 90 value 12.730930
## iter 100 value 12.705643
## final value 12.705643
## stopped after 100 iterations## # weights: 246
## initial value 61.979459
## iter 10 value 12.700194
## iter 20 value 9.700148
## iter 30 value 7.774787
## iter 40 value 7.635994
## iter 50 value 7.370546
## iter 60 value 7.354467
## iter 70 value 4.596214
## iter 80 value 3.655218
## iter 90 value 3.592889
## iter 100 value 3.577700
## final value 3.577700
## stopped after 100 iterations## # weights: 50
## initial value 77.361947
## iter 10 value 27.133126
## iter 20 value 20.197498
## iter 30 value 20.033281
## iter 40 value 20.029611
## final value 20.029596
## converged## # weights: 148
## initial value 69.253864
## iter 10 value 29.640040
## iter 20 value 12.079151
## iter 30 value 11.040399
## iter 40 value 10.669686
## iter 50 value 10.331229
## iter 60 value 10.327987
## iter 70 value 9.762637
## iter 80 value 9.735740
## iter 90 value 9.734909
## iter 100 value 9.734451
## final value 9.734451
## stopped after 100 iterations## # weights: 246
## initial value 65.841760
## iter 10 value 20.066964
## iter 20 value 6.109942
## iter 30 value 2.839043
## iter 40 value 2.777050
## iter 50 value 2.772746
## iter 60 value 2.772641
## iter 70 value 2.772596
## final value 2.772592
## converged## # weights: 50
## initial value 60.756744
## iter 10 value 33.658598
## iter 20 value 28.344173
## iter 30 value 23.299959
## iter 40 value 22.070420
## iter 50 value 22.063349
## final value 22.063336
## converged## # weights: 148
## initial value 64.540074
## iter 10 value 26.041092
## iter 20 value 18.051452
## iter 30 value 17.076063
## iter 40 value 15.939398
## iter 50 value 15.788762
## iter 60 value 15.781481
## iter 70 value 15.772708
## iter 80 value 15.770683
## final value 15.770632
## converged## # weights: 246
## initial value 90.721474
## iter 10 value 28.159264
## iter 20 value 19.691693
## iter 30 value 16.842922
## iter 40 value 15.921174
## iter 50 value 15.542337
## iter 60 value 15.445324
## iter 70 value 15.422428
## iter 80 value 15.362703
## iter 90 value 15.359990
## iter 100 value 15.359560
## final value 15.359560
## stopped after 100 iterations## # weights: 50
## initial value 76.389314
## iter 10 value 30.170576
## iter 20 value 23.629854
## iter 30 value 23.218148
## iter 40 value 23.205239
## iter 50 value 23.201991
## iter 60 value 23.199864
## iter 70 value 23.198628
## iter 80 value 23.197717
## iter 90 value 23.197236
## iter 100 value 23.196748
## final value 23.196748
## stopped after 100 iterations## # weights: 148
## initial value 65.732139
## iter 10 value 17.144385
## iter 20 value 3.989383
## iter 30 value 2.994798
## iter 40 value 2.916044
## iter 50 value 2.908516
## iter 60 value 2.902956
## iter 70 value 2.896174
## iter 80 value 2.888044
## iter 90 value 2.883371
## iter 100 value 2.878192
## final value 2.878192
## stopped after 100 iterations## # weights: 246
## initial value 76.181890
## iter 10 value 9.534100
## iter 20 value 3.103306
## iter 30 value 3.004992
## iter 40 value 2.975824
## iter 50 value 2.942289
## iter 60 value 2.916197
## iter 70 value 2.895984
## iter 80 value 2.879800
## iter 90 value 2.870633
## iter 100 value 2.863583
## final value 2.863583
## stopped after 100 iterations## # weights: 50
## initial value 64.273549
## iter 10 value 31.787518
## iter 20 value 28.070434
## iter 30 value 28.054779
## iter 40 value 28.051093
## final value 28.051001
## converged## # weights: 148
## initial value 63.869210
## iter 10 value 11.707651
## iter 20 value 7.787308
## iter 30 value 4.108267
## iter 40 value 3.860734
## iter 50 value 3.825131
## iter 60 value 3.820141
## iter 70 value 3.469875
## iter 80 value 2.280526
## iter 90 value 1.918474
## iter 100 value 1.912727
## final value 1.912727
## stopped after 100 iterations## # weights: 246
## initial value 68.839143
## iter 10 value 8.882069
## iter 20 value 5.905050
## iter 30 value 5.871238
## iter 40 value 5.868301
## iter 50 value 5.774048
## iter 60 value 5.276243
## iter 70 value 5.274727
## final value 5.274626
## converged## # weights: 50
## initial value 70.795422
## iter 10 value 32.047907
## iter 20 value 26.946720
## iter 30 value 24.652170
## iter 40 value 23.186315
## iter 50 value 22.182320
## iter 60 value 22.090320
## iter 70 value 22.087915
## final value 22.087900
## converged## # weights: 148
## initial value 66.421053
## iter 10 value 22.215929
## iter 20 value 19.043928
## iter 30 value 16.731489
## iter 40 value 14.916165
## iter 50 value 14.748428
## iter 60 value 14.693656
## iter 70 value 14.689541
## final value 14.689498
## converged## # weights: 246
## initial value 93.703985
## iter 10 value 27.186630
## iter 20 value 15.600002
## iter 30 value 14.796207
## iter 40 value 14.482175
## iter 50 value 13.660491
## iter 60 value 13.395956
## iter 70 value 13.279273
## iter 80 value 13.266561
## iter 90 value 13.265572
## iter 100 value 13.265551
## final value 13.265551
## stopped after 100 iterations## # weights: 50
## initial value 71.936346
## iter 10 value 25.833431
## iter 20 value 20.219778
## iter 30 value 19.807449
## iter 40 value 19.797149
## iter 50 value 19.795972
## iter 60 value 19.795041
## iter 70 value 19.794244
## iter 80 value 19.781644
## iter 90 value 16.687917
## iter 100 value 16.629863
## final value 16.629863
## stopped after 100 iterations## # weights: 148
## initial value 74.065216
## iter 10 value 25.870791
## iter 20 value 10.295126
## iter 30 value 7.935096
## iter 40 value 5.275161
## iter 50 value 4.906966
## iter 60 value 4.307070
## iter 70 value 4.297093
## iter 80 value 3.955364
## iter 90 value 3.948602
## iter 100 value 3.942260
## final value 3.942260
## stopped after 100 iterations## # weights: 246
## initial value 71.824251
## iter 10 value 18.318084
## iter 20 value 3.744115
## iter 30 value 3.553298
## iter 40 value 3.525206
## iter 50 value 3.027529
## iter 60 value 2.183698
## iter 70 value 2.131979
## iter 80 value 2.097128
## iter 90 value 2.087701
## iter 100 value 2.062456
## final value 2.062456
## stopped after 100 iterations## # weights: 50
## initial value 70.814612
## iter 10 value 33.114590
## iter 20 value 23.056801
## iter 30 value 22.332807
## iter 40 value 22.329435
## final value 22.329409
## converged## # weights: 148
## initial value 88.455029
## iter 10 value 16.295791
## iter 20 value 11.527525
## iter 30 value 10.925408
## iter 40 value 9.431090
## iter 50 value 8.961886
## iter 60 value 8.959162
## iter 70 value 8.955490
## iter 80 value 8.322922
## iter 90 value 8.319476
## iter 100 value 8.318925
## final value 8.318925
## stopped after 100 iterations## # weights: 246
## initial value 66.041374
## iter 10 value 17.342252
## iter 20 value 7.200597
## iter 30 value 6.627714
## iter 40 value 6.254961
## iter 50 value 6.143318
## iter 60 value 6.139434
## iter 70 value 6.083228
## iter 80 value 5.547651
## iter 90 value 5.547405
## iter 100 value 5.547120
## final value 5.547120
## stopped after 100 iterations## # weights: 50
## initial value 67.496814
## iter 10 value 30.515368
## iter 20 value 21.068589
## iter 30 value 20.405972
## iter 40 value 20.376071
## final value 20.375844
## converged## # weights: 148
## initial value 65.359171
## iter 10 value 31.831775
## iter 20 value 17.622850
## iter 30 value 16.504227
## iter 40 value 16.369671
## iter 50 value 16.357483
## iter 60 value 16.356548
## final value 16.356537
## converged## # weights: 246
## initial value 77.788060
## iter 10 value 27.562391
## iter 20 value 18.189761
## iter 30 value 16.358855
## iter 40 value 16.063131
## iter 50 value 15.965410
## iter 60 value 15.712505
## iter 70 value 15.479670
## iter 80 value 15.431879
## iter 90 value 15.427298
## iter 100 value 15.425827
## final value 15.425827
## stopped after 100 iterations## # weights: 50
## initial value 68.995071
## iter 10 value 27.815322
## iter 20 value 18.916135
## iter 30 value 12.956311
## iter 40 value 12.905134
## iter 50 value 10.965171
## iter 60 value 10.349348
## iter 70 value 10.331287
## iter 80 value 10.328183
## iter 90 value 10.326941
## iter 100 value 10.325329
## final value 10.325329
## stopped after 100 iterations## # weights: 148
## initial value 59.068759
## iter 10 value 13.342250
## iter 20 value 11.194082
## iter 30 value 10.241402
## iter 40 value 9.993602
## iter 50 value 9.462438
## iter 60 value 9.076044
## iter 70 value 9.037729
## iter 80 value 9.022014
## iter 90 value 9.015627
## iter 100 value 8.635475
## final value 8.635475
## stopped after 100 iterations## # weights: 246
## initial value 66.347293
## iter 10 value 17.090083
## iter 20 value 7.512161
## iter 30 value 6.003608
## iter 40 value 5.917302
## iter 50 value 5.804523
## iter 60 value 3.496168
## iter 70 value 3.078921
## iter 80 value 3.016211
## iter 90 value 3.003395
## iter 100 value 2.986100
## final value 2.986100
## stopped after 100 iterations## # weights: 50
## initial value 64.513897
## iter 10 value 19.485039
## iter 20 value 11.639033
## iter 30 value 10.325810
## iter 40 value 10.317461
## iter 50 value 10.317443
## final value 10.317441
## converged## # weights: 148
## initial value 70.083238
## iter 10 value 23.244851
## iter 20 value 19.156835
## iter 30 value 11.426313
## iter 40 value 9.671358
## iter 50 value 9.658430
## iter 60 value 9.656097
## iter 70 value 9.570473
## iter 80 value 9.567748
## iter 90 value 9.567562
## iter 100 value 9.381220
## final value 9.381220
## stopped after 100 iterations## # weights: 246
## initial value 60.593916
## iter 10 value 20.991066
## iter 20 value 19.200181
## iter 30 value 17.178885
## iter 40 value 16.306274
## iter 50 value 16.257038
## iter 60 value 15.075406
## iter 70 value 15.015195
## iter 80 value 14.935005
## iter 90 value 14.925358
## iter 100 value 14.623355
## final value 14.623355
## stopped after 100 iterations## # weights: 50
## initial value 62.537967
## iter 10 value 35.254701
## iter 20 value 28.825978
## iter 30 value 23.312996
## iter 40 value 21.993042
## iter 50 value 21.961746
## final value 21.961708
## converged## # weights: 148
## initial value 69.974251
## iter 10 value 29.162916
## iter 20 value 18.193376
## iter 30 value 16.280391
## iter 40 value 15.677443
## iter 50 value 15.563683
## iter 60 value 15.501291
## iter 70 value 15.483204
## iter 80 value 15.482852
## final value 15.482837
## converged## # weights: 246
## initial value 72.262586
## iter 10 value 20.223094
## iter 20 value 15.945918
## iter 30 value 14.963202
## iter 40 value 14.590589
## iter 50 value 14.543217
## iter 60 value 14.539162
## iter 70 value 14.537981
## iter 80 value 14.537896
## iter 90 value 14.537886
## final value 14.537885
## converged## # weights: 50
## initial value 66.198486
## iter 10 value 29.766360
## iter 20 value 22.370221
## iter 30 value 22.230156
## iter 40 value 22.222300
## iter 50 value 22.220421
## iter 60 value 22.218938
## iter 70 value 22.216435
## iter 80 value 22.215234
## iter 90 value 22.213851
## iter 100 value 22.213010
## final value 22.213010
## stopped after 100 iterations## # weights: 148
## initial value 83.100811
## iter 10 value 15.918306
## iter 20 value 10.231947
## iter 30 value 9.215357
## iter 40 value 8.765780
## iter 50 value 8.310662
## iter 60 value 8.246038
## iter 70 value 7.897242
## iter 80 value 7.888984
## iter 90 value 7.884686
## iter 100 value 7.879003
## final value 7.879003
## stopped after 100 iterations## # weights: 246
## initial value 69.897959
## iter 10 value 11.694864
## iter 20 value 2.963380
## iter 30 value 2.903771
## iter 40 value 2.885586
## iter 50 value 2.873110
## iter 60 value 2.861245
## iter 70 value 2.854449
## iter 80 value 2.849238
## iter 90 value 2.847592
## iter 100 value 2.845049
## final value 2.845049
## stopped after 100 iterations## # weights: 50
## initial value 61.779635
## iter 10 value 28.140517
## iter 20 value 23.522733
## iter 30 value 23.211933
## iter 40 value 23.205503
## iter 50 value 23.205073
## final value 23.205072
## converged## # weights: 148
## initial value 77.841695
## iter 10 value 27.698163
## iter 20 value 13.889326
## iter 30 value 10.970202
## iter 40 value 10.734741
## iter 50 value 10.493446
## iter 60 value 10.486599
## final value 10.486513
## converged## # weights: 246
## initial value 67.400763
## iter 10 value 24.173096
## iter 20 value 8.955481
## iter 30 value 3.031711
## iter 40 value 2.790163
## iter 50 value 2.772812
## final value 2.772599
## converged## # weights: 50
## initial value 65.789546
## iter 10 value 34.585115
## iter 20 value 25.767771
## iter 30 value 22.195990
## iter 40 value 21.600466
## iter 50 value 21.494987
## final value 21.494860
## converged## # weights: 148
## initial value 65.361519
## iter 10 value 21.838720
## iter 20 value 16.768068
## iter 30 value 16.286188
## iter 40 value 16.125547
## iter 50 value 16.062607
## iter 60 value 16.049749
## iter 70 value 16.049097
## final value 16.049096
## converged## # weights: 246
## initial value 69.311848
## iter 10 value 21.702957
## iter 20 value 15.443827
## iter 30 value 14.432362
## iter 40 value 14.113921
## iter 50 value 14.085298
## iter 60 value 14.083951
## iter 70 value 14.083780
## iter 80 value 14.083775
## final value 14.083773
## converged## # weights: 50
## initial value 90.774473
## iter 10 value 29.923901
## iter 20 value 19.856270
## iter 30 value 19.638293
## iter 40 value 17.654798
## iter 50 value 15.383303
## iter 60 value 15.349196
## iter 70 value 15.341885
## iter 80 value 15.339941
## iter 90 value 15.338566
## iter 100 value 15.337600
## final value 15.337600
## stopped after 100 iterations## # weights: 148
## initial value 83.276931
## iter 10 value 10.264692
## iter 20 value 8.302523
## iter 30 value 7.938103
## iter 40 value 7.610760
## iter 50 value 7.219643
## iter 60 value 7.208584
## iter 70 value 7.203998
## iter 80 value 7.173276
## iter 90 value 5.685959
## iter 100 value 5.636098
## final value 5.636098
## stopped after 100 iterations## # weights: 246
## initial value 65.459160
## iter 10 value 16.538406
## iter 20 value 1.813725
## iter 30 value 1.560631
## iter 40 value 1.544815
## iter 50 value 1.529450
## iter 60 value 1.514236
## iter 70 value 1.504423
## iter 80 value 1.498174
## iter 90 value 1.493509
## iter 100 value 1.487447
## final value 1.487447
## stopped after 100 iterations## # weights: 50
## initial value 63.625801
## iter 10 value 18.232677
## iter 20 value 11.383281
## iter 30 value 10.268632
## iter 40 value 10.256586
## iter 50 value 10.254205
## iter 60 value 10.254073
## iter 70 value 10.253959
## final value 10.253948
## converged## # weights: 148
## initial value 65.355276
## iter 10 value 14.243692
## iter 20 value 5.801682
## iter 30 value 4.705341
## iter 40 value 4.689118
## iter 50 value 4.683514
## iter 60 value 4.682293
## iter 70 value 4.682146
## final value 4.682145
## converged## # weights: 246
## initial value 92.903065
## iter 10 value 14.058391
## iter 20 value 6.159199
## iter 30 value 4.766579
## iter 40 value 4.169264
## iter 50 value 2.865474
## iter 60 value 2.797039
## iter 70 value 2.781501
## iter 80 value 2.778008
## iter 90 value 2.776146
## iter 100 value 2.774291
## final value 2.774291
## stopped after 100 iterations## # weights: 50
## initial value 72.687273
## iter 10 value 36.785816
## iter 20 value 22.756274
## iter 30 value 21.498570
## iter 40 value 21.461387
## final value 21.458412
## converged## # weights: 148
## initial value 71.410261
## iter 10 value 29.131096
## iter 20 value 18.595055
## iter 30 value 16.659037
## iter 40 value 15.404559
## iter 50 value 15.131581
## iter 60 value 14.664536
## iter 70 value 14.517692
## iter 80 value 14.512139
## iter 90 value 14.511535
## final value 14.511520
## converged## # weights: 246
## initial value 104.192783
## iter 10 value 24.768062
## iter 20 value 16.302580
## iter 30 value 15.734348
## iter 40 value 15.565991
## iter 50 value 15.402766
## iter 60 value 15.375984
## iter 70 value 15.359687
## iter 80 value 15.351814
## iter 90 value 15.344482
## iter 100 value 15.344293
## final value 15.344293
## stopped after 100 iterations## # weights: 50
## initial value 73.202895
## iter 10 value 24.895801
## iter 20 value 21.156401
## iter 30 value 19.002029
## iter 40 value 18.978148
## iter 50 value 18.971586
## iter 60 value 18.970203
## iter 70 value 16.548010
## iter 80 value 16.450616
## iter 90 value 16.441013
## iter 100 value 16.438674
## final value 16.438674
## stopped after 100 iterations## # weights: 148
## initial value 68.120569
## iter 10 value 16.325503
## iter 20 value 9.969414
## iter 30 value 9.657565
## iter 40 value 6.434655
## iter 50 value 6.266739
## iter 60 value 5.953360
## iter 70 value 5.702718
## iter 80 value 5.678465
## iter 90 value 5.482990
## iter 100 value 5.302906
## final value 5.302906
## stopped after 100 iterations## # weights: 246
## initial value 62.857655
## iter 10 value 5.477674
## iter 20 value 2.962081
## iter 30 value 2.929581
## iter 40 value 2.916364
## iter 50 value 2.899859
## iter 60 value 2.885782
## iter 70 value 2.876054
## iter 80 value 2.869832
## iter 90 value 2.863646
## iter 100 value 2.859856
## final value 2.859856
## stopped after 100 iterations## # weights: 50
## initial value 72.318449
## iter 10 value 23.905200
## iter 20 value 14.508402
## iter 30 value 14.348878
## iter 40 value 14.347977
## final value 14.347976
## converged## # weights: 148
## initial value 63.946219
## iter 10 value 21.282347
## iter 20 value 11.393931
## iter 30 value 10.828035
## iter 40 value 10.789203
## iter 50 value 10.749876
## iter 60 value 10.736921
## iter 70 value 9.454619
## iter 80 value 9.339563
## iter 90 value 9.087192
## iter 100 value 9.044489
## final value 9.044489
## stopped after 100 iterations## # weights: 246
## initial value 75.363655
## iter 10 value 17.219448
## iter 20 value 9.739452
## iter 30 value 4.175350
## iter 40 value 4.159409
## iter 50 value 4.158950
## final value 4.158883
## converged## # weights: 50
## initial value 60.841012
## iter 10 value 30.539360
## iter 20 value 22.492960
## iter 30 value 21.020821
## iter 40 value 20.909006
## final value 20.908686
## converged## # weights: 148
## initial value 74.528678
## iter 10 value 26.712829
## iter 20 value 17.373002
## iter 30 value 16.727912
## iter 40 value 16.692688
## iter 50 value 16.686953
## iter 60 value 16.686728
## final value 16.686721
## converged## # weights: 246
## initial value 78.076159
## iter 10 value 21.956834
## iter 20 value 16.437015
## iter 30 value 14.691541
## iter 40 value 14.195768
## iter 50 value 14.085760
## iter 60 value 13.964192
## iter 70 value 13.888741
## iter 80 value 13.848692
## iter 90 value 13.842821
## iter 100 value 13.842766
## final value 13.842766
## stopped after 100 iterations## # weights: 50
## initial value 64.246782
## iter 10 value 32.902723
## iter 20 value 27.230156
## iter 30 value 23.629822
## iter 40 value 23.347362
## iter 50 value 23.342720
## iter 60 value 23.341645
## iter 70 value 23.340590
## iter 80 value 23.339990
## iter 90 value 23.339493
## iter 100 value 23.339217
## final value 23.339217
## stopped after 100 iterations## # weights: 148
## initial value 85.712520
## iter 10 value 16.791619
## iter 20 value 11.603040
## iter 30 value 11.074299
## iter 40 value 10.843327
## iter 50 value 10.333362
## iter 60 value 10.185515
## iter 70 value 9.996806
## iter 80 value 9.856838
## iter 90 value 9.572686
## iter 100 value 9.562315
## final value 9.562315
## stopped after 100 iterations## # weights: 246
## initial value 79.120018
## iter 10 value 6.377651
## iter 20 value 2.244223
## iter 30 value 2.179833
## iter 40 value 2.135336
## iter 50 value 2.114168
## iter 60 value 2.095743
## iter 70 value 2.081811
## iter 80 value 2.070862
## iter 90 value 2.061911
## iter 100 value 2.055363
## final value 2.055363
## stopped after 100 iterations## # weights: 50
## initial value 77.557564
## iter 10 value 28.387225
## iter 20 value 19.964057
## iter 30 value 16.618183
## iter 40 value 16.562671
## iter 50 value 16.554093
## iter 60 value 16.551415
## iter 70 value 16.546870
## iter 80 value 16.543692
## iter 90 value 16.543029
## iter 100 value 16.542131
## final value 16.542131
## stopped after 100 iterations## # weights: 148
## initial value 64.464868
## iter 10 value 16.133207
## iter 20 value 8.703340
## iter 30 value 7.680735
## iter 40 value 7.561353
## iter 50 value 7.553601
## iter 60 value 7.553180
## iter 70 value 7.552963
## final value 7.552950
## converged## # weights: 246
## initial value 62.611608
## iter 10 value 10.328249
## iter 20 value 3.188274
## iter 30 value 2.806133
## iter 40 value 2.775114
## iter 50 value 2.773460
## iter 60 value 2.772798
## iter 70 value 2.772726
## iter 80 value 2.772628
## final value 2.772627
## converged## # weights: 50
## initial value 73.592805
## iter 10 value 34.840619
## iter 20 value 23.072476
## iter 30 value 20.065711
## iter 40 value 19.997377
## final value 19.996969
## converged## # weights: 148
## initial value 82.051734
## iter 10 value 27.173160
## iter 20 value 17.850339
## iter 30 value 16.355921
## iter 40 value 16.148313
## iter 50 value 16.116440
## iter 60 value 16.085791
## iter 70 value 16.084163
## iter 80 value 16.084133
## final value 16.084133
## converged## # weights: 246
## initial value 76.495503
## iter 10 value 22.318568
## iter 20 value 15.959355
## iter 30 value 15.296612
## iter 40 value 15.047693
## iter 50 value 14.955480
## iter 60 value 14.866659
## iter 70 value 14.857073
## iter 80 value 14.847217
## iter 90 value 14.844692
## iter 100 value 14.844617
## final value 14.844617
## stopped after 100 iterations## # weights: 50
## initial value 68.311073
## iter 10 value 29.994470
## iter 20 value 15.165530
## iter 30 value 12.551919
## iter 40 value 12.389177
## iter 50 value 12.380944
## iter 60 value 12.377296
## iter 70 value 12.370890
## iter 80 value 12.366133
## iter 90 value 12.364434
## iter 100 value 12.362784
## final value 12.362784
## stopped after 100 iterations## # weights: 148
## initial value 69.746220
## iter 10 value 14.162096
## iter 20 value 9.299150
## iter 30 value 9.278387
## iter 40 value 9.213619
## iter 50 value 8.760456
## iter 60 value 8.749491
## iter 70 value 8.745224
## iter 80 value 8.491255
## iter 90 value 7.612465
## iter 100 value 7.020722
## final value 7.020722
## stopped after 100 iterations## # weights: 246
## initial value 67.362469
## iter 10 value 13.287662
## iter 20 value 6.978096
## iter 30 value 6.617655
## iter 40 value 5.973096
## iter 50 value 5.642162
## iter 60 value 5.054511
## iter 70 value 4.790129
## iter 80 value 4.320301
## iter 90 value 4.207467
## iter 100 value 2.982785
## final value 2.982785
## stopped after 100 iterations## # weights: 148
## initial value 90.759886
## iter 10 value 37.280163
## iter 20 value 23.240553
## iter 30 value 18.488033
## iter 40 value 17.849119
## iter 50 value 17.699662
## iter 60 value 17.665383
## iter 70 value 17.637204
## iter 80 value 17.625254
## iter 90 value 17.623952
## iter 100 value 17.623916
## final value 17.623916
## stopped after 100 iterationsresultado_entrenamiento5 <- predict(modelo5, entrenamiento)
resultado_prueba5 <- predict(modelo5, prueba)
mcre5 <- confusionMatrix(resultado_entrenamiento5, entrenamiento$m1_purchase)
mcre5## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 33 0
## Yes 3 71
##
## Accuracy : 0.972
## 95% CI : (0.9202, 0.9942)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : 2.388e-15
##
## Kappa : 0.9359
##
## Mcnemar's Test P-Value : 0.2482
##
## Sensitivity : 0.9167
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.9595
## Prevalence : 0.3364
## Detection Rate : 0.3084
## Detection Prevalence : 0.3084
## Balanced Accuracy : 0.9583
##
## 'Positive' Class : No
## mcrp5 <- confusionMatrix(resultado_prueba5, prueba$m1_purchase)
mcrp5## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 2 7
## Yes 7 10
##
## Accuracy : 0.4615
## 95% CI : (0.2659, 0.6663)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.9866
##
## Kappa : -0.1895
##
## Mcnemar's Test P-Value : 1.0000
##
## Sensitivity : 0.22222
## Specificity : 0.58824
## Pos Pred Value : 0.22222
## Neg Pred Value : 0.58824
## Prevalence : 0.34615
## Detection Rate : 0.07692
## Detection Prevalence : 0.34615
## Balanced Accuracy : 0.40523
##
## 'Positive' Class : No
## Modelo 6. Bosques Aleatorios
modelo6 <- train(m1_purchase ~ ., data=entrenamiento,
method = "rf", #Cambiar
preProcess = c("scale", "center"),
trControl = trainControl(method="cv", number=10),
tuneGrid = expand.grid(mtry = c(2,4,6)) #Cambiar
)
resultado_entrenamiento6 <- predict(modelo6, entrenamiento)
resultado_prueba6 <- predict(modelo6, prueba)
mcre6 <- confusionMatrix(resultado_entrenamiento6, entrenamiento$m1_purchase)
mcre6## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 34 0
## Yes 2 71
##
## Accuracy : 0.9813
## 95% CI : (0.9341, 0.9977)
## No Information Rate : 0.6636
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9576
##
## Mcnemar's Test P-Value : 0.4795
##
## Sensitivity : 0.9444
## Specificity : 1.0000
## Pos Pred Value : 1.0000
## Neg Pred Value : 0.9726
## Prevalence : 0.3364
## Detection Rate : 0.3178
## Detection Prevalence : 0.3178
## Balanced Accuracy : 0.9722
##
## 'Positive' Class : No
## mcrp6 <- confusionMatrix(resultado_prueba6, prueba$m1_purchase)
mcrp6## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 4 6
## Yes 5 11
##
## Accuracy : 0.5769
## 95% CI : (0.3692, 0.7665)
## No Information Rate : 0.6538
## P-Value [Acc > NIR] : 0.8485
##
## Kappa : 0.0892
##
## Mcnemar's Test P-Value : 1.0000
##
## Sensitivity : 0.4444
## Specificity : 0.6471
## Pos Pred Value : 0.4000
## Neg Pred Value : 0.6875
## Prevalence : 0.3462
## Detection Rate : 0.1538
## Detection Prevalence : 0.3846
## Balanced Accuracy : 0.5458
##
## 'Positive' Class : No
## Tabla de Resultados
resultados <- data.frame(
"svmLinear" = c(mcre1$overall["Accuracy"], mcrp1$overall["Accuracy"]),
"svmRadial" = c(mcre2$overall["Accuracy"], mcrp2$overall["Accuracy"]),
"svmPoly" = c(mcre3$overall["Accuracy"], mcrp3$overall["Accuracy"]),
"rpart" = c(mcre4$overall["Accuracy"], mcrp4$overall["Accuracy"]),
"nnet" = c(mcre5$overall["Accuracy"], mcrp5$overall["Accuracy"]),
"rf" = c(mcre6$overall["Accuracy"], mcrp6$overall["Accuracy"])
)
rownames(resultados) <- c("Precisión de entrenamiento", "Precisión de
prueba")
resultados## svmLinear svmRadial svmPoly rpart nnet
## Precisión de entrenamiento 0.9065421 0.9065421 0.8971963 0.8037383 0.9719626
## Precisión de\nprueba 0.5384615 0.5384615 0.5384615 0.5769231 0.4615385
## rf
## Precisión de entrenamiento 0.9813084
## Precisión de\nprueba 0.5769231Conclusiones
En conclusión, aunque todos los modelos tienen un nivel alto de precisión con los datos de entrenamiento, todos tienen un nivel bajo de precisión con los datos de prueba. Los mejores modelos con precisión con los datos de prueba son los modelos de decision tree y random forest.
Sin embargo, el modelo de random forest tiene una precisión muy alta (98%) lo que puede que signifique que tenga sobreajuste por la baja precision en los datos de prueba, minetras que el modelo de decision tree muestra un comportamiento mas equilibrado entre entremaniento y prueba.