Redes neuronales - Lirio
Junior Medina
2026-02-20
#< # Instalar paquetes y llamar librerías
#install.packages("neuralnet")
library(neuralnet)
#install.packages("neuralnet")
library(datasets)
#install.packages("DataExplorer")
library(DataExplorer)
#install.packages("caret")
library(caret)## Loading required package: ggplot2## Loading required package: latticeCrear la base de datos
df <- data.frame(iris)Analisis exploratorio
summary(df)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## str(df)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...head(df)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosaboxplot(df$Sepal.Length)
#create_report(df)
plot_missing(df)## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the DataExplorer package.
## Please report the issue at
## <https://github.com/boxuancui/DataExplorer/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
plot_histogram(df)
plot_correlation(df)
Partir datos 80-20
set.seed(123)
reglones_entrenamiento <- createDataPartition(df$Species, p=0.8, list=FALSE)
entrenamiento <- df[reglones_entrenamiento, ]
prueba <- iris[-reglones_entrenamiento, ]Modelo de redes neuronales artificiales
modelo <- train(Species~., data=entrenamiento,
method= "nnet",
preProcess =c("scale","center"),
trControl =trainControl(method = "cv", number=10)
)## # weights: 11
## initial value 137.158154
## iter 10 value 83.679659
## iter 20 value 49.940615
## final value 49.908817
## converged
## # weights: 27
## initial value 116.394990
## iter 10 value 6.186241
## iter 20 value 0.260622
## iter 30 value 0.000744
## final value 0.000077
## converged
## # weights: 43
## initial value 147.341272
## iter 10 value 20.723909
## iter 20 value 1.711652
## iter 30 value 0.002668
## final value 0.000059
## converged
## # weights: 11
## initial value 121.560247
## iter 10 value 65.949471
## iter 20 value 55.881751
## iter 30 value 44.683010
## iter 40 value 44.159606
## iter 40 value 44.159606
## iter 40 value 44.159606
## final value 44.159606
## converged
## # weights: 27
## initial value 162.871678
## iter 10 value 31.973292
## iter 20 value 21.643843
## iter 30 value 21.176664
## iter 40 value 21.175187
## final value 21.175184
## converged
## # weights: 43
## initial value 124.403113
## iter 10 value 22.493781
## iter 20 value 18.970819
## iter 30 value 18.776926
## iter 40 value 18.751512
## iter 50 value 18.594955
## iter 60 value 18.433573
## iter 70 value 18.406314
## final value 18.406244
## converged
## # weights: 11
## initial value 120.330027
## iter 10 value 48.948789
## iter 20 value 19.465594
## iter 30 value 4.922147
## iter 40 value 3.955106
## iter 50 value 3.905223
## iter 60 value 3.894400
## iter 70 value 3.881836
## iter 80 value 3.874804
## iter 90 value 3.872370
## iter 100 value 3.872335
## final value 3.872335
## stopped after 100 iterations
## # weights: 27
## initial value 126.848219
## iter 10 value 26.579951
## iter 20 value 2.125995
## iter 30 value 0.503338
## iter 40 value 0.462699
## iter 50 value 0.426509
## iter 60 value 0.411345
## iter 70 value 0.394772
## iter 80 value 0.382673
## iter 90 value 0.374982
## iter 100 value 0.371874
## final value 0.371874
## stopped after 100 iterations
## # weights: 43
## initial value 139.437764
## iter 10 value 6.381330
## iter 20 value 0.512400
## iter 30 value 0.467261
## iter 40 value 0.445606
## iter 50 value 0.436766
## iter 60 value 0.416541
## iter 70 value 0.388106
## iter 80 value 0.382227
## iter 90 value 0.379333
## iter 100 value 0.371278
## final value 0.371278
## stopped after 100 iterations
## # weights: 11
## initial value 130.106043
## iter 10 value 27.186408
## iter 20 value 4.702041
## iter 30 value 3.010037
## iter 40 value 2.199278
## iter 50 value 1.884656
## iter 60 value 1.836945
## iter 70 value 1.752174
## iter 80 value 1.745749
## iter 90 value 1.507029
## iter 100 value 1.493314
## final value 1.493314
## stopped after 100 iterations
## # weights: 27
## initial value 114.723677
## iter 10 value 6.352999
## iter 20 value 0.555935
## final value 0.000059
## converged
## # weights: 43
## initial value 135.420682
## iter 10 value 7.555059
## iter 20 value 0.331280
## iter 30 value 0.000103
## iter 30 value 0.000051
## iter 30 value 0.000050
## final value 0.000050
## converged
## # weights: 11
## initial value 124.421768
## iter 10 value 60.982773
## iter 20 value 44.161198
## iter 30 value 43.464615
## iter 30 value 43.464614
## iter 30 value 43.464614
## final value 43.464614
## converged
## # weights: 27
## initial value 123.592678
## iter 10 value 29.678577
## iter 20 value 20.064880
## iter 30 value 19.879129
## iter 40 value 19.844432
## final value 19.844390
## converged
## # weights: 43
## initial value 140.131749
## iter 10 value 28.007751
## iter 20 value 18.816789
## iter 30 value 18.340611
## iter 40 value 18.308431
## iter 50 value 18.305748
## final value 18.305348
## converged
## # weights: 11
## initial value 123.494107
## iter 10 value 105.350381
## iter 20 value 99.435136
## iter 30 value 40.065228
## iter 40 value 8.675195
## iter 50 value 4.168312
## iter 60 value 4.104548
## iter 70 value 3.854797
## iter 80 value 3.850383
## iter 90 value 3.845358
## iter 100 value 3.835212
## final value 3.835212
## stopped after 100 iterations
## # weights: 27
## initial value 122.562474
## iter 10 value 8.450360
## iter 20 value 1.814922
## iter 30 value 0.716283
## iter 40 value 0.640788
## iter 50 value 0.621339
## iter 60 value 0.612589
## iter 70 value 0.584200
## iter 80 value 0.544549
## iter 90 value 0.528601
## iter 100 value 0.514779
## final value 0.514779
## stopped after 100 iterations
## # weights: 43
## initial value 123.657348
## iter 10 value 10.614579
## iter 20 value 2.048113
## iter 30 value 0.659304
## iter 40 value 0.544423
## iter 50 value 0.532691
## iter 60 value 0.510665
## iter 70 value 0.466030
## iter 80 value 0.432980
## iter 90 value 0.364387
## iter 100 value 0.341414
## final value 0.341414
## stopped after 100 iterations
## # weights: 11
## initial value 132.593715
## iter 10 value 48.478232
## iter 20 value 26.680423
## iter 30 value 5.247608
## iter 40 value 2.894683
## iter 50 value 2.713712
## iter 60 value 2.620707
## iter 70 value 2.540290
## iter 80 value 2.455773
## iter 90 value 2.267592
## iter 100 value 2.151796
## final value 2.151796
## stopped after 100 iterations
## # weights: 27
## initial value 120.567002
## iter 10 value 4.393773
## iter 20 value 0.043939
## iter 30 value 0.000566
## final value 0.000075
## converged
## # weights: 43
## initial value 119.486317
## iter 10 value 8.852497
## iter 20 value 1.503151
## iter 30 value 0.005087
## iter 40 value 0.000143
## final value 0.000045
## converged
## # weights: 11
## initial value 138.956697
## iter 10 value 64.167257
## iter 20 value 46.791809
## iter 30 value 44.090079
## final value 44.057609
## converged
## # weights: 27
## initial value 117.449640
## iter 10 value 25.035090
## iter 20 value 20.291424
## iter 30 value 20.270756
## final value 20.270746
## converged
## # weights: 43
## initial value 131.615496
## iter 10 value 28.251470
## iter 20 value 19.360745
## iter 30 value 19.289328
## iter 40 value 19.287649
## iter 50 value 19.287597
## final value 19.287594
## converged
## # weights: 11
## initial value 120.067169
## iter 10 value 52.352023
## iter 20 value 50.228278
## iter 30 value 50.176003
## iter 40 value 50.144595
## iter 50 value 50.095225
## iter 60 value 49.938343
## iter 70 value 49.520427
## iter 80 value 49.433208
## iter 90 value 49.391382
## iter 100 value 49.306617
## final value 49.306617
## stopped after 100 iterations
## # weights: 27
## initial value 121.958571
## iter 10 value 4.666620
## iter 20 value 0.776850
## iter 30 value 0.707439
## iter 40 value 0.640867
## iter 50 value 0.600758
## iter 60 value 0.593505
## iter 70 value 0.584147
## iter 80 value 0.557207
## iter 90 value 0.538098
## iter 100 value 0.514508
## final value 0.514508
## stopped after 100 iterations
## # weights: 43
## initial value 122.004578
## iter 10 value 5.297091
## iter 20 value 1.244824
## iter 30 value 0.495554
## iter 40 value 0.483174
## iter 50 value 0.468581
## iter 60 value 0.375798
## iter 70 value 0.362002
## iter 80 value 0.358173
## iter 90 value 0.350027
## iter 100 value 0.341343
## final value 0.341343
## stopped after 100 iterations
## # weights: 11
## initial value 127.875079
## iter 10 value 33.444226
## iter 20 value 6.390350
## iter 30 value 2.177976
## iter 40 value 1.888232
## iter 50 value 0.770754
## iter 60 value 0.617610
## iter 70 value 0.559419
## iter 80 value 0.543517
## iter 90 value 0.477915
## iter 100 value 0.457584
## final value 0.457584
## stopped after 100 iterations
## # weights: 27
## initial value 128.599893
## iter 10 value 44.079424
## iter 20 value 2.154037
## iter 30 value 0.029681
## final value 0.000069
## converged
## # weights: 43
## initial value 124.439568
## iter 10 value 4.443221
## iter 20 value 0.029295
## final value 0.000092
## converged
## # weights: 11
## initial value 140.781917
## iter 10 value 49.139091
## iter 20 value 42.676198
## final value 42.671367
## converged
## # weights: 27
## initial value 130.168472
## iter 10 value 25.483245
## iter 20 value 19.394183
## iter 30 value 18.266411
## iter 40 value 18.183916
## iter 50 value 18.183745
## final value 18.183745
## converged
## # weights: 43
## initial value 123.492335
## iter 10 value 25.604376
## iter 20 value 17.578453
## iter 30 value 17.302413
## iter 40 value 17.239993
## iter 50 value 17.235966
## iter 60 value 17.210698
## iter 70 value 16.990390
## iter 80 value 16.954457
## final value 16.954456
## converged
## # weights: 11
## initial value 141.016346
## iter 10 value 27.908285
## iter 20 value 4.184545
## iter 30 value 2.072889
## iter 40 value 1.990949
## iter 50 value 1.947868
## iter 60 value 1.913708
## iter 70 value 1.905145
## iter 80 value 1.901705
## iter 90 value 1.899713
## iter 100 value 1.897791
## final value 1.897791
## stopped after 100 iterations
## # weights: 27
## initial value 130.318008
## iter 10 value 2.534330
## iter 20 value 0.226734
## iter 30 value 0.212995
## iter 40 value 0.196194
## iter 50 value 0.192300
## iter 60 value 0.173589
## iter 70 value 0.165969
## iter 80 value 0.158151
## iter 90 value 0.151971
## iter 100 value 0.146246
## final value 0.146246
## stopped after 100 iterations
## # weights: 43
## initial value 132.276641
## iter 10 value 27.227544
## iter 20 value 2.208204
## iter 30 value 0.486149
## iter 40 value 0.384802
## iter 50 value 0.322610
## iter 60 value 0.296213
## iter 70 value 0.260001
## iter 80 value 0.186170
## iter 90 value 0.168783
## iter 100 value 0.156405
## final value 0.156405
## stopped after 100 iterations
## # weights: 11
## initial value 124.721231
## iter 10 value 24.907273
## iter 20 value 3.860256
## iter 30 value 2.694222
## iter 40 value 2.606534
## iter 50 value 2.549742
## iter 60 value 2.480770
## iter 70 value 2.432589
## iter 80 value 2.374387
## iter 90 value 2.304015
## iter 100 value 2.277255
## final value 2.277255
## stopped after 100 iterations
## # weights: 27
## initial value 135.883765
## iter 10 value 9.588177
## iter 20 value 2.164867
## iter 30 value 0.003083
## iter 40 value 0.000404
## final value 0.000079
## converged
## # weights: 43
## initial value 128.423859
## iter 10 value 8.362453
## iter 20 value 1.107101
## iter 30 value 0.002056
## final value 0.000080
## converged
## # weights: 11
## initial value 126.814860
## iter 10 value 45.330853
## iter 20 value 43.371713
## final value 43.371698
## converged
## # weights: 27
## initial value 134.971673
## iter 10 value 35.878441
## iter 20 value 23.544146
## iter 30 value 21.359427
## iter 40 value 19.924676
## iter 50 value 19.397158
## iter 60 value 19.396984
## final value 19.396975
## converged
## # weights: 43
## initial value 131.139565
## iter 10 value 26.253238
## iter 20 value 18.403900
## iter 30 value 17.965643
## iter 40 value 17.848649
## iter 50 value 17.842969
## iter 60 value 17.842727
## final value 17.842722
## converged
## # weights: 11
## initial value 121.942596
## iter 10 value 38.114589
## iter 20 value 5.888252
## iter 30 value 4.098676
## iter 40 value 3.775997
## iter 50 value 3.766825
## iter 60 value 3.726327
## iter 70 value 3.712347
## iter 80 value 3.712293
## iter 90 value 3.711980
## final value 3.711809
## converged
## # weights: 27
## initial value 124.424719
## iter 10 value 36.553008
## iter 20 value 9.253423
## iter 30 value 1.918811
## iter 40 value 0.813359
## iter 50 value 0.471166
## iter 60 value 0.434232
## iter 70 value 0.412618
## iter 80 value 0.405562
## iter 90 value 0.400759
## iter 100 value 0.390511
## final value 0.390511
## stopped after 100 iterations
## # weights: 43
## initial value 127.087485
## iter 10 value 3.861857
## iter 20 value 0.789964
## iter 30 value 0.603240
## iter 40 value 0.538274
## iter 50 value 0.482849
## iter 60 value 0.442551
## iter 70 value 0.343017
## iter 80 value 0.327263
## iter 90 value 0.311396
## iter 100 value 0.296024
## final value 0.296024
## stopped after 100 iterations
## # weights: 11
## initial value 120.926669
## iter 10 value 80.051213
## iter 20 value 49.871571
## iter 30 value 37.312293
## iter 40 value 8.554628
## iter 50 value 4.749590
## iter 60 value 4.203783
## iter 70 value 3.338322
## iter 80 value 2.396834
## iter 90 value 2.285253
## iter 100 value 2.266166
## final value 2.266166
## stopped after 100 iterations
## # weights: 27
## initial value 134.314255
## iter 10 value 18.375214
## iter 20 value 3.032836
## iter 30 value 0.088363
## iter 40 value 0.000269
## final value 0.000068
## converged
## # weights: 43
## initial value 127.460669
## iter 10 value 6.647251
## iter 20 value 0.860359
## iter 30 value 0.000183
## iter 30 value 0.000089
## iter 30 value 0.000088
## final value 0.000088
## converged
## # weights: 11
## initial value 129.349972
## iter 10 value 58.657455
## iter 20 value 46.190684
## iter 30 value 43.787672
## final value 43.776742
## converged
## # weights: 27
## initial value 136.564527
## iter 10 value 26.172828
## iter 20 value 21.449170
## iter 30 value 21.350292
## iter 40 value 21.347425
## final value 21.347423
## converged
## # weights: 43
## initial value 134.553205
## iter 10 value 26.863852
## iter 20 value 19.099568
## iter 30 value 18.385082
## iter 40 value 18.319448
## iter 50 value 18.315140
## iter 60 value 18.314237
## final value 18.314138
## converged
## # weights: 11
## initial value 118.845224
## iter 10 value 51.168472
## iter 20 value 49.976921
## iter 30 value 49.973436
## iter 40 value 49.963205
## iter 50 value 49.957210
## iter 60 value 49.917964
## iter 70 value 44.684196
## iter 80 value 14.830732
## iter 90 value 4.745959
## iter 100 value 4.005893
## final value 4.005893
## stopped after 100 iterations
## # weights: 27
## initial value 116.024325
## iter 10 value 39.736151
## iter 20 value 28.650940
## iter 30 value 21.536570
## iter 40 value 10.723406
## iter 50 value 4.960185
## iter 60 value 4.763786
## iter 70 value 4.501322
## iter 80 value 3.728015
## iter 90 value 1.977298
## iter 100 value 1.662670
## final value 1.662670
## stopped after 100 iterations
## # weights: 43
## initial value 139.109512
## iter 10 value 6.839446
## iter 20 value 1.801500
## iter 30 value 0.653309
## iter 40 value 0.629758
## iter 50 value 0.531276
## iter 60 value 0.495929
## iter 70 value 0.479711
## iter 80 value 0.474062
## iter 90 value 0.459572
## iter 100 value 0.439866
## final value 0.439866
## stopped after 100 iterations
## # weights: 11
## initial value 130.134894
## iter 10 value 21.120199
## iter 20 value 3.850425
## iter 30 value 2.619123
## iter 40 value 2.164615
## iter 50 value 2.113793
## iter 60 value 2.099946
## iter 70 value 1.982283
## iter 80 value 1.970304
## iter 90 value 1.855431
## iter 100 value 1.840065
## final value 1.840065
## stopped after 100 iterations
## # weights: 27
## initial value 121.319823
## iter 10 value 9.224554
## iter 20 value 1.619145
## iter 30 value 0.023451
## final value 0.000052
## converged
## # weights: 43
## initial value 119.958783
## iter 10 value 7.355517
## iter 20 value 0.451841
## iter 30 value 0.000974
## final value 0.000055
## converged
## # weights: 11
## initial value 135.882848
## iter 10 value 106.344554
## iter 20 value 49.663389
## iter 30 value 44.082267
## final value 44.081824
## converged
## # weights: 27
## initial value 129.601052
## iter 10 value 25.326332
## iter 20 value 20.841301
## iter 30 value 20.365360
## iter 40 value 19.981453
## iter 50 value 19.971411
## iter 60 value 19.970853
## final value 19.970845
## converged
## # weights: 43
## initial value 133.009438
## iter 10 value 28.591164
## iter 20 value 19.530477
## iter 30 value 19.340802
## iter 40 value 19.337827
## iter 50 value 19.336955
## iter 60 value 19.336942
## final value 19.336937
## converged
## # weights: 11
## initial value 122.494186
## iter 10 value 50.595369
## iter 20 value 49.972100
## final value 49.965717
## converged
## # weights: 27
## initial value 126.005379
## iter 10 value 20.133877
## iter 20 value 1.723439
## iter 30 value 0.850570
## iter 40 value 0.800848
## iter 50 value 0.703194
## iter 60 value 0.587465
## iter 70 value 0.532854
## iter 80 value 0.518100
## iter 90 value 0.494016
## iter 100 value 0.483044
## final value 0.483044
## stopped after 100 iterations
## # weights: 43
## initial value 141.403683
## iter 10 value 5.004731
## iter 20 value 1.695175
## iter 30 value 0.892849
## iter 40 value 0.736870
## iter 50 value 0.604144
## iter 60 value 0.525125
## iter 70 value 0.514531
## iter 80 value 0.503234
## iter 90 value 0.485164
## iter 100 value 0.477446
## final value 0.477446
## stopped after 100 iterations
## # weights: 11
## initial value 134.899919
## iter 10 value 37.356151
## iter 20 value 12.284395
## iter 30 value 4.202226
## iter 40 value 3.073761
## iter 50 value 2.532373
## iter 60 value 2.211445
## iter 70 value 2.166202
## iter 80 value 2.124348
## iter 90 value 2.055965
## iter 100 value 1.816387
## final value 1.816387
## stopped after 100 iterations
## # weights: 27
## initial value 124.434786
## iter 10 value 4.774821
## iter 20 value 0.002590
## iter 30 value 0.000113
## final value 0.000099
## converged
## # weights: 43
## initial value 117.179052
## iter 10 value 2.763293
## iter 20 value 0.003312
## final value 0.000057
## converged
## # weights: 11
## initial value 144.222752
## iter 10 value 60.355328
## iter 20 value 43.895422
## iter 30 value 42.994217
## iter 30 value 42.994216
## iter 30 value 42.994216
## final value 42.994216
## converged
## # weights: 27
## initial value 149.958592
## iter 10 value 29.106817
## iter 20 value 18.723106
## iter 30 value 18.602068
## iter 40 value 18.594731
## final value 18.594730
## converged
## # weights: 43
## initial value 151.323466
## iter 10 value 25.176469
## iter 20 value 17.911917
## iter 30 value 17.801742
## iter 40 value 17.567450
## iter 50 value 17.232979
## iter 60 value 17.040933
## iter 70 value 17.030898
## iter 80 value 17.030049
## final value 17.029957
## converged
## # weights: 11
## initial value 125.330915
## iter 10 value 50.092228
## iter 20 value 49.928518
## iter 30 value 49.225211
## iter 40 value 46.190986
## iter 50 value 39.912675
## iter 60 value 14.721394
## iter 70 value 5.240189
## iter 80 value 3.695497
## iter 90 value 3.515460
## iter 100 value 3.098835
## final value 3.098835
## stopped after 100 iterations
## # weights: 27
## initial value 131.514021
## iter 10 value 21.428450
## iter 20 value 1.857646
## iter 30 value 0.656298
## iter 40 value 0.573496
## iter 50 value 0.440421
## iter 60 value 0.411152
## iter 70 value 0.390519
## iter 80 value 0.385680
## iter 90 value 0.368784
## iter 100 value 0.348473
## final value 0.348473
## stopped after 100 iterations
## # weights: 43
## initial value 131.396056
## iter 10 value 4.227384
## iter 20 value 1.424260
## iter 30 value 0.484542
## iter 40 value 0.451634
## iter 50 value 0.434553
## iter 60 value 0.411818
## iter 70 value 0.380774
## iter 80 value 0.361957
## iter 90 value 0.355571
## iter 100 value 0.341448
## final value 0.341448
## stopped after 100 iterations
## # weights: 11
## initial value 124.725766
## iter 10 value 52.435359
## iter 20 value 49.907922
## iter 30 value 49.382401
## iter 40 value 35.220997
## iter 50 value 8.801913
## iter 60 value 4.262407
## iter 70 value 2.632280
## iter 80 value 2.291911
## iter 90 value 2.171173
## iter 100 value 2.064037
## final value 2.064037
## stopped after 100 iterations
## # weights: 27
## initial value 121.213256
## iter 10 value 4.715440
## iter 20 value 0.012867
## final value 0.000060
## converged
## # weights: 43
## initial value 125.839411
## iter 10 value 5.743691
## iter 20 value 0.160593
## final value 0.000072
## converged
## # weights: 11
## initial value 119.242497
## iter 10 value 44.746080
## iter 20 value 43.871570
## final value 43.871556
## converged
## # weights: 27
## initial value 126.090732
## iter 10 value 27.134842
## iter 20 value 22.347298
## iter 30 value 22.047386
## iter 40 value 21.022820
## iter 50 value 20.939006
## final value 20.938933
## converged
## # weights: 43
## initial value 108.205013
## iter 10 value 33.575657
## iter 20 value 19.798211
## iter 30 value 19.239341
## iter 40 value 18.985431
## iter 50 value 18.950630
## iter 60 value 18.943527
## iter 70 value 18.940566
## final value 18.940477
## converged
## # weights: 11
## initial value 127.705849
## iter 10 value 45.929987
## iter 20 value 36.821210
## iter 30 value 13.164203
## iter 40 value 4.588665
## iter 50 value 3.615648
## iter 60 value 3.539641
## iter 70 value 3.325096
## iter 80 value 3.304364
## iter 90 value 3.301093
## iter 100 value 3.300849
## final value 3.300849
## stopped after 100 iterations
## # weights: 27
## initial value 145.426276
## iter 10 value 10.662092
## iter 20 value 1.885068
## iter 30 value 0.785276
## iter 40 value 0.647444
## iter 50 value 0.560103
## iter 60 value 0.508256
## iter 70 value 0.473915
## iter 80 value 0.426546
## iter 90 value 0.371958
## iter 100 value 0.345431
## final value 0.345431
## stopped after 100 iterations
## # weights: 43
## initial value 129.307234
## iter 10 value 6.466680
## iter 20 value 0.465599
## iter 30 value 0.369290
## iter 40 value 0.356601
## iter 50 value 0.340119
## iter 60 value 0.332450
## iter 70 value 0.314144
## iter 80 value 0.302247
## iter 90 value 0.299302
## iter 100 value 0.291440
## final value 0.291440
## stopped after 100 iterations
## # weights: 11
## initial value 118.395363
## iter 10 value 50.040092
## iter 20 value 49.907040
## final value 49.906755
## converged
## # weights: 27
## initial value 120.969076
## iter 10 value 9.562802
## iter 20 value 0.612910
## iter 30 value 0.002474
## final value 0.000067
## converged
## # weights: 43
## initial value 122.764102
## iter 10 value 8.448140
## iter 20 value 0.780831
## iter 30 value 0.002363
## final value 0.000084
## converged
## # weights: 11
## initial value 130.728649
## iter 10 value 57.681475
## iter 20 value 44.398334
## iter 30 value 43.385700
## final value 43.382021
## converged
## # weights: 27
## initial value 128.997817
## iter 10 value 30.196862
## iter 20 value 19.901318
## iter 30 value 19.715836
## iter 40 value 19.653286
## iter 50 value 19.494844
## iter 60 value 19.484851
## iter 70 value 19.483818
## final value 19.483817
## converged
## # weights: 43
## initial value 102.606354
## iter 10 value 24.828885
## iter 20 value 18.910758
## iter 30 value 18.573516
## iter 40 value 18.463629
## iter 50 value 18.462101
## final value 18.462087
## converged
## # weights: 11
## initial value 120.687211
## iter 10 value 54.577891
## iter 20 value 26.112984
## iter 30 value 4.533226
## iter 40 value 3.795451
## iter 50 value 3.694006
## iter 60 value 3.664231
## iter 70 value 3.658786
## iter 80 value 3.647778
## iter 90 value 3.645322
## iter 100 value 3.645062
## final value 3.645062
## stopped after 100 iterations
## # weights: 27
## initial value 126.392641
## iter 10 value 28.610864
## iter 20 value 6.330440
## iter 30 value 1.064218
## iter 40 value 0.263611
## iter 50 value 0.252745
## iter 60 value 0.248951
## iter 70 value 0.224939
## iter 80 value 0.215765
## iter 90 value 0.214087
## iter 100 value 0.211165
## final value 0.211165
## stopped after 100 iterations
## # weights: 43
## initial value 139.699176
## iter 10 value 4.566035
## iter 20 value 0.709051
## iter 30 value 0.579667
## iter 40 value 0.527335
## iter 50 value 0.469353
## iter 60 value 0.440636
## iter 70 value 0.396077
## iter 80 value 0.370362
## iter 90 value 0.317181
## iter 100 value 0.308036
## final value 0.308036
## stopped after 100 iterations
## # weights: 27
## initial value 176.738412
## iter 10 value 28.632388
## iter 20 value 21.019063
## iter 30 value 20.367429
## iter 40 value 20.366183
## final value 20.366062
## convergedresultado_entrenamiento <- predict(modelo,entrenamiento)
resultado_prueba <- predict(modelo, prueba)
#Matriz de confusión del resultado de entrenamiento
mcre <- confusionMatrix(resultado_entrenamiento, entrenamiento$Species)
mcre## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 40 0 0
## versicolor 0 38 0
## virginica 0 2 40
##
## Overall Statistics
##
## Accuracy : 0.9833
## 95% CI : (0.9411, 0.998)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.975
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 0.9500 1.0000
## Specificity 1.0000 1.0000 0.9750
## Pos Pred Value 1.0000 1.0000 0.9524
## Neg Pred Value 1.0000 0.9756 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3167 0.3333
## Detection Prevalence 0.3333 0.3167 0.3500
## Balanced Accuracy 1.0000 0.9750 0.9875#Matriz de confusion
mcrp <-confusionMatrix(resultado_prueba,prueba$Species)
mcrp## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 10 0 0
## versicolor 0 10 1
## virginica 0 0 9
##
## Overall Statistics
##
## Accuracy : 0.9667
## 95% CI : (0.8278, 0.9992)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 2.963e-13
##
## Kappa : 0.95
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 0.9000
## Specificity 1.0000 0.9500 1.0000
## Pos Pred Value 1.0000 0.9091 1.0000
## Neg Pred Value 1.0000 1.0000 0.9524
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3000
## Detection Prevalence 0.3333 0.3667 0.3000
## Balanced Accuracy 1.0000 0.9750 0.9500Conclusiones
El modelo clasifica setosa con desempeño perfecto, sin errores de predicción.
Presenta un desempeño muy alto en versicolor, con mínima presencia de falsos positivos.
En virginica mantiene alta precisión, aunque con algunos falsos negativos.
En general, el modelo muestra excelente capacidad discriminatoria y alta exactitud balanceada en las tres clases.