melhor resultado entre os usados
precision recall f1-score support
0 0.02 0.18 0.04 11
1 0.98 0.87 0.92 675
accuracy 0.85 686
macro avg 0.50 0.52 0.48 686 weighted avg 0.97 0.85 0.91 686
pre rec spe f1 geo iba sup
0 0.02 0.18 0.87 0.04 0.40 0.15 11
1 0.98 0.87 0.18 0.92 0.40 0.17 675
avg / total 0.97 0.85 0.19 0.91 0.40 0.17 686
Acurácia balanceada: 0.5235016835016835 [[ 2 9] [ 91 584]]
precision recall f1-score support
0 0.00 0.00 0.00 11
1 0.98 0.97 0.98 675
accuracy 0.95 686
macro avg 0.49 0.48 0.49 686 weighted avg 0.97 0.95 0.96 686
pre rec spe f1 geo iba sup
0 0.00 0.00 0.97 0.00 0.00 0.00 11
1 0.98 0.97 0.00 0.98 0.00 0.00 675
avg / total 0.97 0.95 0.02 0.96 0.00 0.00 686
Acurácia balanceada: 0.48444444444444446 [[ 0 11] [ 21 654]]
precision recall f1-score support
0 0.01 0.09 0.02 11
1 0.98 0.87 0.92 675
accuracy 0.86 686
macro avg 0.50 0.48 0.47 686 weighted avg 0.97 0.86 0.91 686
pre rec spe f1 geo iba sup
0 0.01 0.09 0.87 0.02 0.28 0.07 11
1 0.98 0.87 0.09 0.92 0.28 0.09 675
avg / total 0.97 0.86 0.10 0.91 0.28 0.09 686
Acurácia balanceada: 0.48175084175084176 [[ 1 10] [ 86 589]]
Perceptron Acurácia balanceada: 0.5453872053872054
GradientBoostingClassifier Acurácia balanceada: 0.5235016835016835
RadiusNeighborsClassifier Acurácia balanceada: 0.5044444444444445
BernoulliNB Acurácia balanceada: 0.5022222222222222
precision recall f1-score support
0 0.02 0.82 0.04 11
1 0.99 0.27 0.43 675
accuracy 0.28 686
macro avg 0.50 0.55 0.23 686 weighted avg 0.97 0.28 0.42 686
pre rec spe f1 geo iba sup
0 0.02 0.82 0.27 0.04 0.47 0.24 11
1 0.99 0.27 0.82 0.43 0.47 0.21 675
avg / total 0.97 0.28 0.81 0.42 0.47 0.21 686
Acurácia balanceada: 0.5453872053872054 [[ 9 2] [491 184]]
não fiz
1.4. Support Vector Machines¶
precision recall f1-score support
0 0.01 0.18 0.03 11
1 0.98 0.79 0.87 675
accuracy 0.78 686
macro avg 0.50 0.48 0.45 686 weighted avg 0.97 0.78 0.86 686
pre rec spe f1 geo iba sup
0 0.01 0.18 0.79 0.03 0.38 0.13 11
1 0.98 0.79 0.18 0.87 0.38 0.15 675
avg / total 0.97 0.78 0.19 0.86 0.38 0.15 686
Acurácia balanceada: 0.48350168350168354 [[ 2 9] [145 530]]
precision recall f1-score support
0 0.01 0.09 0.01 11
1 0.98 0.80 0.88 675
accuracy 0.79 686
macro avg 0.49 0.45 0.45 686 weighted avg 0.97 0.79 0.87 686
pre rec spe f1 geo iba sup
0 0.01 0.09 0.80 0.01 0.27 0.07 11
1 0.98 0.80 0.09 0.88 0.27 0.08 675
avg / total 0.97 0.79 0.10 0.87 0.27 0.08 686
Acurácia balanceada: 0.4476767676767677 [[ 1 10] [132 543]]
precision recall f1-score support
0 0.01 0.09 0.01 11
1 0.98 0.80 0.88 675
accuracy 0.78 686
macro avg 0.49 0.44 0.45 686 weighted avg 0.97 0.78 0.87 686
pre rec spe f1 geo iba sup
0 0.01 0.09 0.80 0.01 0.27 0.07 11
1 0.98 0.80 0.09 0.88 0.27 0.08 675
avg / total 0.97 0.78 0.10 0.87 0.27 0.08 686
Acurácia balanceada: 0.44323232323232326 [[ 1 10] [138 537]]
precision recall f1-score support
0 0.02 0.18 0.03 11
1 0.98 0.82 0.89 675
accuracy 0.81 686
macro avg 0.50 0.50 0.46 686 weighted avg 0.97 0.81 0.88 686
pre rec spe f1 geo iba sup
0 0.02 0.18 0.82 0.03 0.39 0.14 11
1 0.98 0.82 0.18 0.89 0.39 0.16 675
avg / total 0.97 0.81 0.19 0.88 0.39 0.16 686
Acurácia balanceada: 0.4990572390572391 [[ 2 9] [124 551]]
1.5. Stochastic Gradient Descent¶
precision recall f1-score support
0 0.00 0.00 0.00 11
1 0.98 0.93 0.96 675
accuracy 0.91 686
macro avg 0.49 0.46 0.48 686 weighted avg 0.97 0.91 0.94 686
pre rec spe f1 geo iba sup
0 0.00 0.00 0.93 0.00 0.00 0.00 11
1 0.98 0.93 0.00 0.96 0.00 0.00 675
avg / total 0.97 0.91 0.01 0.94 0.00 0.00 686
Acurácia balanceada: 0.46444444444444444 [[ 0 11] [ 48 627]]
precision recall f1-score support
0 0.00 0.00 0.00 11
1 0.98 0.93 0.96 675
accuracy 0.91 686
macro avg 0.49 0.46 0.48 686 weighted avg 0.97 0.91 0.94 686
pre rec spe f1 geo iba sup
0 0.00 0.00 0.93 0.00 0.00 0.00 11
1 0.98 0.93 0.00 0.96 0.00 0.00 675
avg / total 0.97 0.91 0.01 0.94 0.00 0.00 686
Acurácia balanceada: 0.46444444444444444 [[ 0 11] [ 48 627]]
1.6. Nearest Neighbors¶
precision recall f1-score support
0 0.00 0.00 0.00 11
1 0.98 0.88 0.93 675
accuracy 0.87 686
macro avg 0.49 0.44 0.47 686 weighted avg 0.97 0.87 0.92 686
pre rec spe f1 geo iba sup
0 0.00 0.00 0.88 0.00 0.00 0.00 11
1 0.98 0.88 0.00 0.93 0.00 0.00 675
avg / total 0.97 0.87 0.01 0.92 0.00 0.00 686
Acurácia balanceada: 0.44222222222222224 [[ 0 11] [ 78 597]]
precision recall f1-score support
0 0.02 1.00 0.03 11
1 1.00 0.01 0.02 675
accuracy 0.02 686
macro avg 0.51 0.50 0.02 686 weighted avg 0.98 0.02 0.02 686
pre rec spe f1 geo iba sup
0 0.02 1.00 0.01 0.03 0.09 0.01 11
1 1.00 0.01 1.00 0.02 0.09 0.01 675
avg / total 0.98 0.02 0.98 0.02 0.09 0.01 686
Acurácia balanceada: 0.5044444444444445 [[ 11 0] [669 6]]
precision recall f1-score support
0 0.01 0.36 0.03 11
1 0.98 0.60 0.74 675
accuracy 0.59 686
macro avg 0.50 0.48 0.38 686 weighted avg 0.97 0.59 0.73 686
pre rec spe f1 geo iba sup
0 0.01 0.36 0.60 0.03 0.47 0.21 11
1 0.98 0.60 0.36 0.74 0.47 0.22 675
avg / total 0.97 0.59 0.37 0.73 0.47 0.22 686
Acurácia balanceada: 0.4795959595959596 [[ 4 7] [273 402]]
1.7. Gaussian Processes¶
Nao fiz, pois o modelo PLSCanonical
pode não ser a melhor escolha para problemas de classificação, pois ele é mais comumente usado para análise de correlação entre conjuntos de dados multivariados.
1.9. Naive Bayes¶
precision recall f1-score support
0 0.01 0.18 0.02 11
1 0.98 0.69 0.81 675
accuracy 0.68 686
macro avg 0.50 0.44 0.41 686 weighted avg 0.97 0.68 0.80 686
pre rec spe f1 geo iba sup
0 0.01 0.18 0.69 0.02 0.35 0.12 11
1 0.98 0.69 0.18 0.81 0.35 0.13 675
avg / total 0.97 0.68 0.19 0.80 0.35 0.13 686
Acurácia balanceada: 0.4368350168350168 [[ 2 9] [208 467]]
precision recall f1-score support
0 0.02 1.00 0.03 11
1 1.00 0.00 0.01 675
accuracy 0.02 686
macro avg 0.51 0.50 0.02 686 weighted avg 0.98 0.02 0.01 686
pre rec spe f1 geo iba sup
0 0.02 1.00 0.00 0.03 0.07 0.00 11
1 1.00 0.00 1.00 0.01 0.07 0.00 675
avg / total 0.98 0.02 0.98 0.01 0.07 0.00 686
Acurácia balanceada: 0.5022222222222222 [[ 11 0] [672 3]]
precision recall f1-score support
0 0.00 0.00 0.00 11
1 0.98 1.00 0.99 675
accuracy 0.98 686
macro avg 0.49 0.50 0.49 686 weighted avg 0.97 0.98 0.97 686
pre rec spe f1 geo iba sup
0 0.00 0.00 1.00 0.00 0.00 0.00 11
1 0.98 1.00 0.00 0.99 0.00 0.00 675
avg / total 0.97 0.98 0.02 0.97 0.00 0.00 686
Acurácia balanceada: 0.49777777777777776 [[ 0 11] [ 3 672]]
precision recall f1-score support
0 0.01 0.09 0.02 11
1 0.98 0.88 0.93 675
accuracy 0.87 686
macro avg 0.50 0.48 0.47 686 weighted avg 0.97 0.87 0.91 686
pre rec spe f1 geo iba sup
0 0.01 0.09 0.88 0.02 0.28 0.07 11
1 0.98 0.88 0.09 0.93 0.28 0.09 675
avg / total 0.97 0.87 0.10 0.91 0.28 0.09 686
Acurácia balanceada: 0.4847138047138047 [[ 1 10] [ 82 593]]
1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking¶
precision recall f1-score support
0 0.00 0.00 0.00 11
1 0.98 0.92 0.95 675
accuracy 0.90 686
macro avg 0.49 0.46 0.47 686 weighted avg 0.97 0.90 0.93 686
pre rec spe f1 geo iba sup
0 0.00 0.00 0.92 0.00 0.00 0.00 11
1 0.98 0.92 0.00 0.95 0.00 0.00 675
avg / total 0.97 0.90 0.01 0.93 0.00 0.00 686
Acurácia balanceada: 0.4585185185185185 [[ 0 11] [ 56 619]]
precision recall f1-score support
0 0.00 0.00 0.00 11
1 0.98 0.96 0.97 675
accuracy 0.94 686
macro avg 0.49 0.48 0.49 686 weighted avg 0.97 0.94 0.96 686
pre rec spe f1 geo iba sup
0 0.00 0.00 0.96 0.00 0.00 0.00 11
1 0.98 0.96 0.00 0.97 0.00 0.00 675
avg / total 0.97 0.94 0.02 0.96 0.00 0.00 686
Acurácia balanceada: 0.48 [[ 0 11] [ 27 648]]
precision recall f1-score support
0 0.01 0.09 0.02 11
1 0.98 0.89 0.94 675
accuracy 0.88 686
macro avg 0.50 0.49 0.48 686 weighted avg 0.97 0.88 0.92 686
pre rec spe f1 geo iba sup
0 0.01 0.09 0.89 0.02 0.28 0.07 11
1 0.98 0.89 0.09 0.94 0.28 0.09 675
avg / total 0.97 0.88 0.10 0.92 0.28 0.09 686
Acurácia balanceada: 0.4921212121212121 [[ 1 10] [ 72 603]]
1.16. Probability calibration¶
1.17. Neural network models (supervised)¶
precision recall f1-score support
0 0.01 0.18 0.02 11
1 0.98 0.67 0.80 675
accuracy 0.66 686
macro avg 0.49 0.43 0.41 686 weighted avg 0.96 0.66 0.79 686
pre rec spe f1 geo iba sup
0 0.01 0.18 0.67 0.02 0.35 0.12 11
1 0.98 0.67 0.18 0.80 0.35 0.13 675
avg / total 0.96 0.66 0.19 0.79 0.35 0.13 686
Acurácia balanceada: 0.42720538720538725 [[ 2 9] [221 454]]