Scheme:weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S E 20 -H a
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1 - Rede Neural 1 === Run information === Scheme:weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S E 20 -H a Relation: Aula9_RedesNeurais-2 Instances: 91 Attributes: 7 CLASS_Y LS GA Rep_EST Rep_PC EST_CUST FORN_VEN Test mode:10-fold cross-validation === Classifier model (full training set) === Sigmoid Node 0 Threshold Node Node Node Node Sigmoid Node 1 Threshold Node
2 Node Node Node Sigmoid Node 2 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 3 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 4 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 5
3 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Class I Node 0 Class A Node 1 Time taken to build model: 0.22 seconds === Stratified cross-validation === === Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 91 === Detailed Accuracy By Class ===
4 TP Rate FP Rate Precision Recall F-Measure ROC Area Class I A Weighted Avg === Confusion Matrix === a b <-- classified as 40 9 a = I 4 38 b = A - Rede Neural 2 === Run information === Scheme:weka.classifiers.functions.MultilayerPerceptron -L 0.9 -M 0.2 -N 500 -V 0 -S E 20 -H a Relation: Aula9_RedesNeurais-2 Instances: 91 Attributes: 7 CLASS_Y LS GA Rep_EST Rep_PC EST_CUST FORN_VEN Test mode:10-fold cross-validation
5 === Classifier model (full training set) === Sigmoid Node 0 Threshold Node Node Node Node Sigmoid Node 1 Threshold Node Node Node Node Sigmoid Node 2 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 3 Threshold Attrib LS Attrib GA
6 Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 4 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 5 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Class I Node 0 Class A Node 1 Time taken to build model: 0.14 seconds
7 === Stratified cross-validation === === Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 91 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class I A Weighted Avg === Confusion Matrix === a b <-- classified as 40 9 a = I 2 40 b = A - Rede Neural 3 === Run information ===
8 Scheme:weka.classifiers.functions.MultilayerPerceptron -L 0.2 -M 0.2 -N V 0 -S E 20 -H a Relation: Aula9_RedesNeurais-2 Instances: 91 Attributes: 7 CLASS_Y LS GA Rep_EST Rep_PC EST_CUST FORN_VEN Test mode:10-fold cross-validation === Classifier model (full training set) === Sigmoid Node 0 Threshold Node Node Node Node Sigmoid Node 1 Threshold Node Node Node Node Sigmoid Node 2
9 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 3 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 4 Threshold Attrib LS Attrib GA Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Sigmoid Node 5 Threshold Attrib LS Attrib GA
10 Attrib Rep_EST Attrib Rep_PC Attrib EST_CUST Attrib FORN_VEN Class I Node 0 Class A Node 1 Time taken to build model: 1.39 seconds === Stratified cross-validation === === Summary === Correctly Classified Instances % Incorrectly Classified Instances % Kappa statistic Mean absolute error Root mean squared error Relative absolute error % Root relative squared error % Total Number of Instances 91 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure ROC Area Class I A
11 Weighted Avg === Confusion Matrix === a b <-- classified as 42 7 a = I 3 39 b = A Análise: O modelo 3 apresenta melhor resultado, pois tem o maior acerto (89,01%) e o menor erro relativo médio (23,49%)
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