KNN
Column
Iris
Dataset Random
Summary
[1] 0.00 0.25 0.50 0.75 1.00Tables
Grafico
Column
Wine
Corrplot
Normalizado
Summary
high low medium
21.6 3.7 74.6 wine_train_labels
high low medium
796 144 2733
Cell Contents
|-------------------------|
| N |
| N / Table Total |
|-------------------------|
Total Observations in Table: 1225
| wine_test_pred
wine_test_labels | medium | Row Total |
-----------------|-----------|-----------|
high | 264 | 264 |
| 0.216 | |
-----------------|-----------|-----------|
low | 39 | 39 |
| 0.032 | |
-----------------|-----------|-----------|
medium | 922 | 922 |
| 0.753 | |
-----------------|-----------|-----------|
Column Total | 1225 | 1225 |
-----------------|-----------|-----------|
Random Forest
Column
Iris
Grafico
Resultados
Column
Wine
Prediccion
Matriz de Confusion1
Confusion Matrix and Statistics
Reference
Prediction 3 4 5 6 7 8 9
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 1 16 167 77 5 0 0
6 3 15 123 336 141 28 1
7 0 1 1 26 30 7 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
Overall Statistics
Accuracy : 0.545
95% CI : (0.5132, 0.5765)
No Information Rate : 0.4489
P-Value [Acc > NIR] : 1.044e-09
Kappa : 0.2543
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: 3 Class: 4 Class: 5 Class: 6 Class: 7 Class: 8
Sensitivity 0.00000 0.00000 0.5739 0.7654 0.17045 0.00000
Specificity 1.00000 1.00000 0.8559 0.4230 0.95636 1.00000
Pos Pred Value NaN NaN 0.6278 0.5193 0.46154 NaN
Neg Pred Value 0.99591 0.96728 0.8258 0.6888 0.84009 0.96421
Prevalence 0.00409 0.03272 0.2975 0.4489 0.17996 0.03579
Detection Rate 0.00000 0.00000 0.1708 0.3436 0.03067 0.00000
Detection Prevalence 0.00000 0.00000 0.2720 0.6616 0.06646 0.00000
Balanced Accuracy 0.50000 0.50000 0.7149 0.5942 0.56341 0.50000
Class: 9
Sensitivity 0.000000
Specificity 1.000000
Pos Pred Value NaN
Neg Pred Value 0.998978
Prevalence 0.001022
Detection Rate 0.000000
Detection Prevalence 0.000000
Balanced Accuracy 0.500000Grafico
Matriz de Confusion Resultado
Confusion Matrix and Statistics
Reference
Prediction 3 4 5 6 7 8 9
3 0 0 0 0 0 0 0
4 0 9 3 0 0 0 0
5 1 12 210 59 9 0 0
6 3 11 77 352 72 13 1
7 0 0 1 28 95 11 0
8 0 0 0 0 0 11 0
9 0 0 0 0 0 0 0
Overall Statistics
Accuracy : 0.6922
95% CI : (0.6622, 0.7211)
No Information Rate : 0.4489
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.5214
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: 3 Class: 4 Class: 5 Class: 6 Class: 7 Class: 8
Sensitivity 0.00000 0.281250 0.7216 0.8018 0.53977 0.31429
Specificity 1.00000 0.996829 0.8821 0.6716 0.95012 1.00000
Pos Pred Value NaN 0.750000 0.7216 0.6654 0.70370 1.00000
Neg Pred Value 0.99591 0.976190 0.8821 0.8062 0.90391 0.97518
Prevalence 0.00409 0.032720 0.2975 0.4489 0.17996 0.03579
Detection Rate 0.00000 0.009202 0.2147 0.3599 0.09714 0.01125
Detection Prevalence 0.00000 0.012270 0.2975 0.5409 0.13804 0.01125
Balanced Accuracy 0.50000 0.639039 0.8019 0.7367 0.74495 0.65714
Class: 9
Sensitivity 0.000000
Specificity 1.000000
Pos Pred Value NaN
Neg Pred Value 0.998978
Prevalence 0.001022
Detection Rate 0.000000
Detection Prevalence 0.000000
Balanced Accuracy 0.500000Naive Bayes
Column
Iris
Modelo
Naive Bayes
150 samples
4 predictor
3 classes: 'setosa', 'versicolor', 'virginica'
No pre-processing
Resampling: Cross-Validated (10 fold)
Summary of sample sizes: 135, 135, 135, 135, 135, 135, ...
Resampling results across tuning parameters:
usekernel Accuracy Kappa
FALSE 0.9533333 0.93
TRUE 0.9533333 0.93
Tuning parameter 'fL' was held constant at a value of 0
Tuning
parameter 'adjust' was held constant at a value of 1
Accuracy was used to select the optimal model using the largest value.
The final values used for the model were fL = 0, usekernel = FALSE and adjust
= 1.Prediccion
$class
[1] setosa setosa setosa setosa setosa setosa
[7] setosa setosa setosa setosa setosa setosa
[13] setosa setosa setosa setosa setosa setosa
[19] setosa setosa setosa setosa setosa setosa
[25] setosa setosa setosa setosa setosa setosa
[31] setosa setosa setosa setosa setosa setosa
[37] setosa setosa setosa setosa setosa setosa
[43] setosa setosa setosa setosa setosa setosa
[49] setosa setosa versicolor versicolor virginica versicolor
[55] versicolor versicolor versicolor versicolor versicolor versicolor
[61] versicolor versicolor versicolor versicolor versicolor versicolor
[67] versicolor versicolor versicolor versicolor virginica versicolor
[73] versicolor versicolor versicolor versicolor versicolor virginica
[79] versicolor versicolor versicolor versicolor versicolor versicolor
[85] versicolor versicolor versicolor versicolor versicolor versicolor
[91] versicolor versicolor versicolor versicolor versicolor versicolor
[97] versicolor versicolor versicolor versicolor virginica virginica
[103] virginica virginica virginica virginica versicolor virginica
[109] virginica virginica virginica virginica virginica virginica
[115] virginica virginica virginica virginica virginica versicolor
[121] virginica virginica virginica virginica virginica virginica
[127] virginica virginica virginica virginica virginica virginica
[133] virginica versicolor virginica virginica virginica virginica
[139] virginica virginica virginica virginica virginica virginica
[145] virginica virginica virginica virginica virginica virginica
Levels: setosa versicolor virginica
$posterior
setosa versicolor virginica
[1,] 1.000000e+00 2.981309e-18 2.152373e-25
[2,] 1.000000e+00 3.169312e-17 6.938030e-25
[3,] 1.000000e+00 2.367113e-18 7.240956e-26
[4,] 1.000000e+00 3.069606e-17 8.690636e-25
[5,] 1.000000e+00 1.017337e-18 8.885794e-26
[6,] 1.000000e+00 2.717732e-14 4.344285e-21
[7,] 1.000000e+00 2.321639e-17 7.988271e-25
[8,] 1.000000e+00 1.390751e-17 8.166995e-25
[9,] 1.000000e+00 1.990156e-17 3.606469e-25
[10,] 1.000000e+00 7.378931e-18 3.615492e-25
[11,] 1.000000e+00 9.396089e-18 1.474623e-24
[12,] 1.000000e+00 3.461964e-17 2.093627e-24
[13,] 1.000000e+00 2.804520e-18 1.010192e-25
[14,] 1.000000e+00 1.799033e-19 6.060578e-27
[15,] 1.000000e+00 5.533879e-19 2.485033e-25
[16,] 1.000000e+00 6.273863e-17 4.509864e-23
[17,] 1.000000e+00 1.106658e-16 1.282419e-23
[18,] 1.000000e+00 4.841773e-17 2.350011e-24
[19,] 1.000000e+00 1.126175e-14 2.567180e-21
[20,] 1.000000e+00 1.808513e-17 1.963924e-24
[21,] 1.000000e+00 2.178382e-15 2.013989e-22
[22,] 1.000000e+00 1.210057e-15 7.788592e-23
[23,] 1.000000e+00 4.535220e-20 3.130074e-27
[24,] 1.000000e+00 3.147327e-11 8.175305e-19
[25,] 1.000000e+00 1.838507e-14 1.553757e-21
[26,] 1.000000e+00 6.873990e-16 1.830374e-23
[27,] 1.000000e+00 3.192598e-14 1.045146e-21
[28,] 1.000000e+00 1.542562e-17 1.274394e-24
[29,] 1.000000e+00 8.833285e-18 5.368077e-25
[30,] 1.000000e+00 9.557935e-17 3.652571e-24
[31,] 1.000000e+00 2.166837e-16 6.730536e-24
[32,] 1.000000e+00 3.940500e-14 1.546678e-21
[33,] 1.000000e+00 1.609092e-20 1.013278e-26
[34,] 1.000000e+00 7.222217e-20 4.261853e-26
[35,] 1.000000e+00 6.289348e-17 1.831694e-24
[36,] 1.000000e+00 2.850926e-18 8.874002e-26
[37,] 1.000000e+00 7.746279e-18 7.235628e-25
[38,] 1.000000e+00 8.623934e-20 1.223633e-26
[39,] 1.000000e+00 4.612936e-18 9.655450e-26
[40,] 1.000000e+00 2.009325e-17 1.237755e-24
[41,] 1.000000e+00 1.300634e-17 5.657689e-25
[42,] 1.000000e+00 1.577617e-15 5.717219e-24
[43,] 1.000000e+00 1.494911e-18 4.800333e-26
[44,] 1.000000e+00 1.076475e-10 3.721344e-18
[45,] 1.000000e+00 1.357569e-12 1.708326e-19
[46,] 1.000000e+00 3.882113e-16 5.587814e-24
[47,] 1.000000e+00 5.086735e-18 8.960156e-25
[48,] 1.000000e+00 5.012793e-18 1.636566e-25
[49,] 1.000000e+00 5.717245e-18 8.231337e-25
[50,] 1.000000e+00 7.713456e-18 3.349997e-25
[51,] 4.893048e-107 8.018653e-01 1.981347e-01
[52,] 7.920550e-100 9.429283e-01 5.707168e-02
[53,] 5.494369e-121 4.606254e-01 5.393746e-01
[54,] 1.129435e-69 9.999621e-01 3.789964e-05
[55,] 1.473329e-105 9.503408e-01 4.965916e-02
[56,] 1.931184e-89 9.990013e-01 9.986538e-04
[57,] 4.539099e-113 6.592515e-01 3.407485e-01
[58,] 2.549753e-34 9.999997e-01 3.119517e-07
[59,] 6.562814e-97 9.895385e-01 1.046153e-02
[60,] 5.000210e-69 9.998928e-01 1.071638e-04
[61,] 7.354548e-41 9.999997e-01 3.143915e-07
[62,] 4.799134e-86 9.958564e-01 4.143617e-03
[63,] 4.631287e-60 9.999925e-01 7.541274e-06
[64,] 1.052252e-103 9.850868e-01 1.491324e-02
[65,] 4.789799e-55 9.999700e-01 2.999393e-05
[66,] 1.514706e-92 9.787587e-01 2.124125e-02
[67,] 1.338348e-97 9.899311e-01 1.006893e-02
[68,] 2.026115e-62 9.999799e-01 2.007314e-05
[69,] 6.547473e-101 9.941996e-01 5.800427e-03
[70,] 3.016276e-58 9.999913e-01 8.739959e-06
[71,] 1.053341e-127 1.609361e-01 8.390639e-01
[72,] 1.248202e-70 9.997743e-01 2.256698e-04
[73,] 3.294753e-119 9.245812e-01 7.541876e-02
[74,] 1.314175e-95 9.979398e-01 2.060233e-03
[75,] 3.003117e-83 9.982736e-01 1.726437e-03
[76,] 2.536747e-92 9.865372e-01 1.346281e-02
[77,] 1.558909e-111 9.102260e-01 8.977398e-02
[78,] 7.014282e-136 7.989607e-02 9.201039e-01
[79,] 5.034528e-99 9.854957e-01 1.450433e-02
[80,] 1.439052e-41 9.999984e-01 1.601574e-06
[81,] 1.251567e-54 9.999955e-01 4.500139e-06
[82,] 8.769539e-48 9.999983e-01 1.742560e-06
[83,] 3.447181e-62 9.999664e-01 3.361987e-05
[84,] 1.087302e-132 6.134355e-01 3.865645e-01
[85,] 4.119852e-97 9.918297e-01 8.170260e-03
[86,] 1.140835e-102 8.734107e-01 1.265893e-01
[87,] 2.247339e-110 7.971795e-01 2.028205e-01
[88,] 4.870630e-88 9.992978e-01 7.022084e-04
[89,] 2.028672e-72 9.997620e-01 2.379898e-04
[90,] 2.227900e-69 9.999461e-01 5.390514e-05
[91,] 5.110709e-81 9.998510e-01 1.489819e-04
[92,] 5.774841e-99 9.885399e-01 1.146006e-02
[93,] 5.146736e-66 9.999591e-01 4.089540e-05
[94,] 1.332816e-34 9.999997e-01 2.716264e-07
[95,] 6.094144e-77 9.998034e-01 1.966331e-04
[96,] 1.424276e-72 9.998236e-01 1.764463e-04
[97,] 8.302641e-77 9.996692e-01 3.307548e-04
[98,] 1.835520e-82 9.988601e-01 1.139915e-03
[99,] 5.710350e-30 9.999997e-01 3.094739e-07
[100,] 3.996459e-73 9.998204e-01 1.795726e-04
[101,] 3.993755e-249 1.031032e-10 1.000000e+00
[102,] 1.228659e-149 2.724406e-02 9.727559e-01
[103,] 2.460661e-216 2.327488e-07 9.999998e-01
[104,] 2.864831e-173 2.290954e-03 9.977090e-01
[105,] 8.299884e-214 3.175384e-07 9.999997e-01
[106,] 1.371182e-267 3.807455e-10 1.000000e+00
[107,] 3.444090e-107 9.719885e-01 2.801154e-02
[108,] 3.741929e-224 1.782047e-06 9.999982e-01
[109,] 5.564644e-188 5.823191e-04 9.994177e-01
[110,] 2.052443e-260 2.461662e-12 1.000000e+00
[111,] 8.669405e-159 4.895235e-04 9.995105e-01
[112,] 4.220200e-163 3.168643e-03 9.968314e-01
[113,] 4.360059e-190 6.230821e-06 9.999938e-01
[114,] 6.142256e-151 1.423414e-02 9.857659e-01
[115,] 2.201426e-186 1.393247e-06 9.999986e-01
[116,] 2.949945e-191 6.128385e-07 9.999994e-01
[117,] 2.909076e-168 2.152843e-03 9.978472e-01
[118,] 1.347608e-281 2.872996e-12 1.000000e+00
[119,] 2.786402e-306 1.151469e-12 1.000000e+00
[120,] 2.082510e-123 9.561626e-01 4.383739e-02
[121,] 2.194169e-217 1.712166e-08 1.000000e+00
[122,] 3.325791e-145 1.518718e-02 9.848128e-01
[123,] 6.251357e-269 1.170872e-09 1.000000e+00
[124,] 4.415135e-135 1.360432e-01 8.639568e-01
[125,] 6.315716e-201 1.300512e-06 9.999987e-01
[126,] 5.257347e-203 9.507989e-06 9.999905e-01
[127,] 1.476391e-129 2.067703e-01 7.932297e-01
[128,] 8.772841e-134 1.130589e-01 8.869411e-01
[129,] 5.230800e-194 1.395719e-05 9.999860e-01
[130,] 7.014892e-179 8.232518e-04 9.991767e-01
[131,] 6.306820e-218 1.214497e-06 9.999988e-01
[132,] 2.539020e-247 4.668891e-10 1.000000e+00
[133,] 2.210812e-201 2.000316e-06 9.999980e-01
[134,] 1.128613e-128 7.118948e-01 2.881052e-01
[135,] 8.114869e-151 4.900992e-01 5.099008e-01
[136,] 7.419068e-249 1.448050e-10 1.000000e+00
[137,] 1.004503e-215 9.743357e-09 1.000000e+00
[138,] 1.346716e-167 2.186989e-03 9.978130e-01
[139,] 1.994716e-128 1.999894e-01 8.000106e-01
[140,] 8.440466e-185 6.769126e-06 9.999932e-01
[141,] 2.334365e-218 7.456220e-09 1.000000e+00
[142,] 2.179139e-183 6.352663e-07 9.999994e-01
[143,] 1.228659e-149 2.724406e-02 9.727559e-01
[144,] 3.426814e-229 6.597015e-09 1.000000e+00
[145,] 2.011574e-232 2.620636e-10 1.000000e+00
[146,] 1.078519e-187 7.915543e-07 9.999992e-01
[147,] 1.061392e-146 2.770575e-02 9.722942e-01
[148,] 1.846900e-164 4.398402e-04 9.995602e-01
[149,] 1.439996e-195 3.384156e-07 9.999997e-01
[150,] 2.771480e-143 5.987903e-02 9.401210e-01 y
setosa versicolor virginica
setosa 50 0 0
versicolor 0 47 3
virginica 0 3 47Column
Wine
Modelo
fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
1 7.0 0.27 0.36 20.7 0.045
2 6.3 0.30 0.34 1.6 0.049
3 8.1 0.28 0.40 6.9 0.050
4 7.2 0.23 0.32 8.5 0.058
5 7.2 0.23 0.32 8.5 0.058
6 8.1 0.28 0.40 6.9 0.050
free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
1 45 170 1.0010 3.00 0.45 8.8
2 14 132 0.9940 3.30 0.49 9.5
3 30 97 0.9951 3.26 0.44 10.1
4 47 186 0.9956 3.19 0.40 9.9
5 47 186 0.9956 3.19 0.40 9.9
6 30 97 0.9951 3.26 0.44 10.1
quality
1 6
2 6
3 6
4 6
5 6
6 6Regresion Logistica
Column
Iris
Names
[1] "setosa" "versicolor" "virginica" [1] 0Grafico
Summary
Call:
glm(formula = y ~ x, family = "binomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.12681 -0.51865 0.02993 0.30652 2.25044
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -27.500 5.934 -4.634 3.59e-06 ***
x 5.112 1.109 4.611 4.01e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 110.854 on 79 degrees of freedom
Residual deviance: 48.818 on 78 degrees of freedom
AIC: 52.818
Number of Fisher Scoring iterations: 6Prediccion
ir_ctrl.Sepal.Length ir_ctrl.Species predicted_val
1 5.4 setosa 0.52665832
2 5.0 setosa 0.12584710
3 4.8 setosa 0.04923563
4 5.4 setosa 0.52665832
5 5.7 setosa 0.83759291
6 4.9 setosa 0.07948111
7 5.5 setosa 0.64975559
8 5.1 setosa 0.19357325
9 4.5 setosa 0.01104861
10 5.0 setosa 0.12584710
11 5.3 setosa 0.40023260
12 6.9 versicolor 0.99958015
13 5.7 versicolor 0.83759291
14 5.2 versicolor 0.28582944
15 5.6 versicolor 0.75569041
16 5.6 versicolor 0.75569041
17 6.3 versicolor 0.99105619
18 6.4 versicolor 0.99461661
19 5.7 versicolor 0.83759291
20 5.7 versicolor 0.83759291Grafica
Column
Wine
Table
3 4 5 6 7 8 9
20 163 1457 2198 880 175 5 Grafica
Grafica
Clase
[,1] [,2]
[1,] "fixed.acidity" "-0.114"
[2,] "volatile.acidity" "-0.195"
[3,] "citric.acid" "-0.009"
[4,] "residual.sugar" "-0.098"
[5,] "chlorides" "-0.21"
[6,] "free.sulfur.dioxide" "0.008"
[7,] "total.sulfur.dioxide" "-0.175"
[8,] "density" "-0.307"
[9,] "pH" "0.099"
[10,] "sulphates" "0.054"
[11,] "alcohol" "0.436"
[12,] "quality" "1" Var1 Freq Freq Freq
1 3 20 16 4
2 4 163 131 32
3 5 1457 1166 291
4 6 2198 1759 439
5 7 880 704 176
6 8 175 140 35
7 9 5 4 1SVM
Column
Iris
Modelo
Call:
svm(formula = Species ~ ., data = datos.entreno)
Parameters:
SVM-Type: C-classification
SVM-Kernel: radial
cost: 1
Number of Support Vectors: 43Prediccion
1 5 11 13 16 23 24
setosa setosa setosa setosa setosa setosa setosa
27 29 33 35 36 40 46
setosa setosa setosa setosa setosa setosa setosa
47 48 49 51 53 63 67
setosa setosa setosa versicolor versicolor versicolor versicolor
68 69 70 71 75 76 78
versicolor versicolor versicolor virginica versicolor versicolor virginica
82 83 87 91 102 104 107
versicolor versicolor versicolor versicolor virginica virginica versicolor
115 119 121 123 124 126 132
virginica virginica virginica virginica virginica virginica virginica
134 137 141
versicolor virginica virginica
Levels: setosa versicolor virginicaResultados
Species
prediccion setosa versicolor virginica
setosa 17 0 0
versicolor 0 13 2
virginica 0 2 11[1] 91.11111Column
Wine
Corrplot
Red Neuronal
Column
Iris
Modelo
$call
neuralnet(formula = frml, data = Train, hidden = 3, threshold = 0.1,
algorithm = "rprop+")
$response
setosa versicolor virginica
1 TRUE FALSE FALSE
2 TRUE FALSE FALSE
3 TRUE FALSE FALSE
4 TRUE FALSE FALSE
5 TRUE FALSE FALSE
6 TRUE FALSE FALSE
8 TRUE FALSE FALSE
9 TRUE FALSE FALSE
10 TRUE FALSE FALSE
11 TRUE FALSE FALSE
12 TRUE FALSE FALSE
13 TRUE FALSE FALSE
14 TRUE FALSE FALSE
16 TRUE FALSE FALSE
17 TRUE FALSE FALSE
18 TRUE FALSE FALSE
19 TRUE FALSE FALSE
21 TRUE FALSE FALSE
23 TRUE FALSE FALSE
24 TRUE FALSE FALSE
25 TRUE FALSE FALSE
26 TRUE FALSE FALSE
27 TRUE FALSE FALSE
28 TRUE FALSE FALSE
29 TRUE FALSE FALSE
30 TRUE FALSE FALSE
31 TRUE FALSE FALSE
32 TRUE FALSE FALSE
33 TRUE FALSE FALSE
34 TRUE FALSE FALSE
35 TRUE FALSE FALSE
36 TRUE FALSE FALSE
37 TRUE FALSE FALSE
39 TRUE FALSE FALSE
40 TRUE FALSE FALSE
41 TRUE FALSE FALSE
42 TRUE FALSE FALSE
44 TRUE FALSE FALSE
45 TRUE FALSE FALSE
46 TRUE FALSE FALSE
47 TRUE FALSE FALSE
48 TRUE FALSE FALSE
49 TRUE FALSE FALSE
50 TRUE FALSE FALSE
51 FALSE TRUE FALSE
52 FALSE TRUE FALSE
53 FALSE TRUE FALSE
54 FALSE TRUE FALSE
55 FALSE TRUE FALSE
56 FALSE TRUE FALSE
58 FALSE TRUE FALSE
59 FALSE TRUE FALSE
60 FALSE TRUE FALSE
61 FALSE TRUE FALSE
62 FALSE TRUE FALSE
63 FALSE TRUE FALSE
64 FALSE TRUE FALSE
65 FALSE TRUE FALSE
66 FALSE TRUE FALSE
69 FALSE TRUE FALSE
70 FALSE TRUE FALSE
71 FALSE TRUE FALSE
72 FALSE TRUE FALSE
73 FALSE TRUE FALSE
74 FALSE TRUE FALSE
75 FALSE TRUE FALSE
77 FALSE TRUE FALSE
78 FALSE TRUE FALSE
79 FALSE TRUE FALSE
80 FALSE TRUE FALSE
81 FALSE TRUE FALSE
83 FALSE TRUE FALSE
84 FALSE TRUE FALSE
85 FALSE TRUE FALSE
86 FALSE TRUE FALSE
88 FALSE TRUE FALSE
89 FALSE TRUE FALSE
90 FALSE TRUE FALSE
91 FALSE TRUE FALSE
93 FALSE TRUE FALSE
94 FALSE TRUE FALSE
95 FALSE TRUE FALSE
96 FALSE TRUE FALSE
97 FALSE TRUE FALSE
98 FALSE TRUE FALSE
100 FALSE TRUE FALSE
101 FALSE FALSE TRUE
102 FALSE FALSE TRUE
103 FALSE FALSE TRUE
104 FALSE FALSE TRUE
105 FALSE FALSE TRUE
106 FALSE FALSE TRUE
107 FALSE FALSE TRUE
108 FALSE FALSE TRUE
109 FALSE FALSE TRUE
112 FALSE FALSE TRUE
113 FALSE FALSE TRUE
114 FALSE FALSE TRUE
115 FALSE FALSE TRUE
116 FALSE FALSE TRUE
117 FALSE FALSE TRUE
118 FALSE FALSE TRUE
119 FALSE FALSE TRUE
120 FALSE FALSE TRUE
121 FALSE FALSE TRUE
124 FALSE FALSE TRUE
125 FALSE FALSE TRUE
126 FALSE FALSE TRUE
127 FALSE FALSE TRUE
128 FALSE FALSE TRUE
130 FALSE FALSE TRUE
131 FALSE FALSE TRUE
132 FALSE FALSE TRUE
133 FALSE FALSE TRUE
134 FALSE FALSE TRUE
135 FALSE FALSE TRUE
136 FALSE FALSE TRUE
137 FALSE FALSE TRUE
138 FALSE FALSE TRUE
139 FALSE FALSE TRUE
141 FALSE FALSE TRUE
142 FALSE FALSE TRUE
143 FALSE FALSE TRUE
144 FALSE FALSE TRUE
145 FALSE FALSE TRUE
146 FALSE FALSE TRUE
147 FALSE FALSE TRUE
148 FALSE FALSE TRUE
149 FALSE FALSE TRUE
150 FALSE FALSE TRUE
$covariate
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
4 4.6 3.1 1.5 0.2
5 5.0 3.6 1.4 0.2
6 5.4 3.9 1.7 0.4
8 5.0 3.4 1.5 0.2
9 4.4 2.9 1.4 0.2
10 4.9 3.1 1.5 0.1
11 5.4 3.7 1.5 0.2
12 4.8 3.4 1.6 0.2
13 4.8 3.0 1.4 0.1
14 4.3 3.0 1.1 0.1
16 5.7 4.4 1.5 0.4
17 5.4 3.9 1.3 0.4
18 5.1 3.5 1.4 0.3
19 5.7 3.8 1.7 0.3
21 5.4 3.4 1.7 0.2
23 4.6 3.6 1.0 0.2
24 5.1 3.3 1.7 0.5
25 4.8 3.4 1.9 0.2
26 5.0 3.0 1.6 0.2
27 5.0 3.4 1.6 0.4
28 5.2 3.5 1.5 0.2
29 5.2 3.4 1.4 0.2
30 4.7 3.2 1.6 0.2
31 4.8 3.1 1.6 0.2
32 5.4 3.4 1.5 0.4
33 5.2 4.1 1.5 0.1
34 5.5 4.2 1.4 0.2
35 4.9 3.1 1.5 0.2
36 5.0 3.2 1.2 0.2
37 5.5 3.5 1.3 0.2
39 4.4 3.0 1.3 0.2
40 5.1 3.4 1.5 0.2
41 5.0 3.5 1.3 0.3
42 4.5 2.3 1.3 0.3
44 5.0 3.5 1.6 0.6
45 5.1 3.8 1.9 0.4
46 4.8 3.0 1.4 0.3
47 5.1 3.8 1.6 0.2
48 4.6 3.2 1.4 0.2
49 5.3 3.7 1.5 0.2
50 5.0 3.3 1.4 0.2
51 7.0 3.2 4.7 1.4
52 6.4 3.2 4.5 1.5
53 6.9 3.1 4.9 1.5
54 5.5 2.3 4.0 1.3
55 6.5 2.8 4.6 1.5
56 5.7 2.8 4.5 1.3
58 4.9 2.4 3.3 1.0
59 6.6 2.9 4.6 1.3
60 5.2 2.7 3.9 1.4
61 5.0 2.0 3.5 1.0
62 5.9 3.0 4.2 1.5
63 6.0 2.2 4.0 1.0
64 6.1 2.9 4.7 1.4
65 5.6 2.9 3.6 1.3
66 6.7 3.1 4.4 1.4
69 6.2 2.2 4.5 1.5
70 5.6 2.5 3.9 1.1
71 5.9 3.2 4.8 1.8
72 6.1 2.8 4.0 1.3
73 6.3 2.5 4.9 1.5
74 6.1 2.8 4.7 1.2
75 6.4 2.9 4.3 1.3
77 6.8 2.8 4.8 1.4
78 6.7 3.0 5.0 1.7
79 6.0 2.9 4.5 1.5
80 5.7 2.6 3.5 1.0
81 5.5 2.4 3.8 1.1
83 5.8 2.7 3.9 1.2
84 6.0 2.7 5.1 1.6
85 5.4 3.0 4.5 1.5
86 6.0 3.4 4.5 1.6
88 6.3 2.3 4.4 1.3
89 5.6 3.0 4.1 1.3
90 5.5 2.5 4.0 1.3
91 5.5 2.6 4.4 1.2
93 5.8 2.6 4.0 1.2
94 5.0 2.3 3.3 1.0
95 5.6 2.7 4.2 1.3
96 5.7 3.0 4.2 1.2
97 5.7 2.9 4.2 1.3
98 6.2 2.9 4.3 1.3
100 5.7 2.8 4.1 1.3
101 6.3 3.3 6.0 2.5
102 5.8 2.7 5.1 1.9
103 7.1 3.0 5.9 2.1
104 6.3 2.9 5.6 1.8
105 6.5 3.0 5.8 2.2
106 7.6 3.0 6.6 2.1
107 4.9 2.5 4.5 1.7
108 7.3 2.9 6.3 1.8
109 6.7 2.5 5.8 1.8
112 6.4 2.7 5.3 1.9
113 6.8 3.0 5.5 2.1
114 5.7 2.5 5.0 2.0
115 5.8 2.8 5.1 2.4
116 6.4 3.2 5.3 2.3
117 6.5 3.0 5.5 1.8
118 7.7 3.8 6.7 2.2
119 7.7 2.6 6.9 2.3
120 6.0 2.2 5.0 1.5
121 6.9 3.2 5.7 2.3
124 6.3 2.7 4.9 1.8
125 6.7 3.3 5.7 2.1
126 7.2 3.2 6.0 1.8
127 6.2 2.8 4.8 1.8
128 6.1 3.0 4.9 1.8
130 7.2 3.0 5.8 1.6
131 7.4 2.8 6.1 1.9
132 7.9 3.8 6.4 2.0
133 6.4 2.8 5.6 2.2
134 6.3 2.8 5.1 1.5
135 6.1 2.6 5.6 1.4
136 7.7 3.0 6.1 2.3
137 6.3 3.4 5.6 2.4
138 6.4 3.1 5.5 1.8
139 6.0 3.0 4.8 1.8
141 6.7 3.1 5.6 2.4
142 6.9 3.1 5.1 2.3
143 5.8 2.7 5.1 1.9
144 6.8 3.2 5.9 2.3
145 6.7 3.3 5.7 2.5
146 6.7 3.0 5.2 2.3
147 6.3 2.5 5.0 1.9
148 6.5 3.0 5.2 2.0
149 6.2 3.4 5.4 2.3
150 5.9 3.0 5.1 1.8
$model.list
$model.list$response
[1] "setosa" "versicolor" "virginica"
$model.list$variables
[1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
$err.fct
function (x, y)
{
1/2 * (y - x)^2
}
<bytecode: 0x1079d6f0>
<environment: 0x10786be8>
attr(,"type")
[1] "sse"
$act.fct
function (x)
{
1/(1 + exp(-x))
}
<bytecode: 0x107a21a0>
<environment: 0x107a2a98>
attr(,"type")
[1] "logistic"Prediccion
[1] "virginica" "versicolor" "virginica" "virginica" "versicolor"
[6] "setosa" "versicolor" "virginica" "virginica" "setosa"
[11] "versicolor" "versicolor" "versicolor" "setosa" "versicolor"
[16] "setosa" "setosa" "versicolor" "setosa" "virginica" Matriz de Confusion
predict_class
setosa versicolor virginica
setosa 6 0 0
versicolor 0 8 0
virginica 0 0 6Grafica
Column
Wine
Grafica
Wine Alcohol
0 0
Malic_acid Ash
0 0
Alcalinity_ash Magnesium
0 0
Total_phenols Flavanoids
0 0
Nonflavanoinds_phenols Proanthocyanins
0 0
Color_intensity Hue
0 0
OD280_OD315_of_diluted_wines Proline
0 0 
Grafica
Normalizado
Modelo
Prediccion
V1 V2 V3
3 1 0 0
4 1 0 0
8 1 0 0
11 1 0 0
17 1 0 0
19 1 0 0
25 1 0 0
30 1 0 0
38 1 0 0
45 1 0 0[1] 1 1 1 1 1 1Table
Cell Contents
|-------------------------|
| N |
| Chi-square contribution |
| N / Row Total |
| N / Col Total |
| N / Table Total |
|-------------------------|
Total Observations in Table: 44
| predic
test_res | 1 | 2 | 3 | Row Total |
-------------|-----------|-----------|-----------|-----------|
1 | 15 | 0 | 0 | 15 |
| 12.803 | 4.773 | 4.091 | |
| 1.000 | 0.000 | 0.000 | 0.341 |
| 0.833 | 0.000 | 0.000 | |
| 0.341 | 0.000 | 0.000 | |
-------------|-----------|-----------|-----------|-----------|
2 | 3 | 13 | 0 | 16 |
| 1.920 | 12.287 | 4.364 | |
| 0.188 | 0.812 | 0.000 | 0.364 |
| 0.167 | 0.929 | 0.000 | |
| 0.068 | 0.295 | 0.000 | |
-------------|-----------|-----------|-----------|-----------|
3 | 0 | 1 | 12 | 13 |
| 5.318 | 2.378 | 20.161 | |
| 0.000 | 0.077 | 0.923 | 0.295 |
| 0.000 | 0.071 | 1.000 | |
| 0.000 | 0.023 | 0.273 | |
-------------|-----------|-----------|-----------|-----------|
Column Total | 18 | 14 | 12 | 44 |
| 0.409 | 0.318 | 0.273 | |
-------------|-----------|-----------|-----------|-----------|
Matriz de confusion
Confusion Matrix and Statistics
predic
test_res 1 2 3
1 15 0 0
2 3 13 0
3 0 1 12
Overall Statistics
Accuracy : 0.9091
95% CI : (0.7833, 0.9747)
No Information Rate : 0.4091
P-Value [Acc > NIR] : 5.265e-12
Kappa : 0.8631
Mcnemar's Test P-Value : NA
Statistics by Class:
Class: 1 Class: 2 Class: 3
Sensitivity 0.8333 0.9286 1.0000
Specificity 1.0000 0.9000 0.9688
Pos Pred Value 1.0000 0.8125 0.9231
Neg Pred Value 0.8966 0.9643 1.0000
Prevalence 0.4091 0.3182 0.2727
Detection Rate 0.3409 0.2955 0.2727
Detection Prevalence 0.3409 0.3636 0.2955
Balanced Accuracy 0.9167 0.9143 0.9844