-1
得到的結果我在使用WEKA一個新手，所以你能不能解釋一下我下面的結果我從嘗試使用訓練多層感知器（神經網絡）數據有：解釋我從WEKA
也可能你ATLEAST給我一些可以幫助我理解這一點的鏈接？
=== Run information ===
Scheme:weka.classifiers.functions.MultilayerPerceptron -L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H a -G -R
Relation: Dengue
Instances: 520
Attributes: 12
MinTemp
MaxTemp
MeanTemp
RelativeHumidity
Rainfall
Wind
LandArea
IncomeClass
WasteGenerated
PopulationDensity
HouseNumber
Dengue
Test mode:evaluate on training data
=== Classifier model (full training set) ===
Linear Node 0
Inputs Weights
Threshold 1.045699824540429
Node 1 -0.7885241220010747
Node 2 -0.5679021300029351
Node 3 -0.6990681220652758
Node 4 -1.7036399417988182
Node 5 -1.7986596505677839
Node 6 -1.0031026344357001
Sigmoid Node 1
Inputs Weights
Threshold -2.7846715622473632
Attrib MinTemp -0.3756262925227143
Attrib MaxTemp -1.0113362508935868
Attrib MeanTemp -0.6867107452689675
Attrib RelativeHumidity -1.357278537485863
Attrib Rainfall 0.9346189251054217
Attrib Wind -2.4697988150023895
Attrib LandArea -0.04802972345084459
Attrib IncomeClass -0.0023757695994812353
Attrib WasteGenerated -0.5219516258114455
Attrib PopulationDensity 0.6275856253232837
Attrib HouseNumber 0.4794517421072107
Sigmoid Node 2
Inputs Weights
Threshold -2.238113558499396
Attrib MinTemp 0.6634817443452294
Attrib MaxTemp 0.04177526569735764
Attrib MeanTemp 0.4213111516398967
Attrib RelativeHumidity 0.9477161615423007
Attrib Rainfall -0.06941110528380763
Attrib Wind 0.1398767209217198
Attrib LandArea 0.011908782901326666
Attrib IncomeClass -0.03177518077905532
Attrib WasteGenerated -2.111275394512881
Attrib PopulationDensity -0.002225384228836655
Attrib HouseNumber -0.18689477740073276
Sigmoid Node 3
Inputs Weights
Threshold -1.5469990007413668
Attrib MinTemp -0.538188914566223
Attrib MaxTemp 0.2452404814154855
Attrib MeanTemp -0.07155897171503904
Attrib RelativeHumidity -0.6490463479419373
Attrib Rainfall 1.2010399306686497
Attrib Wind 0.7275195821368675
Attrib LandArea -0.033472141554108756
Attrib IncomeClass 0.021303339082304765
Attrib WasteGenerated -0.12403826628027773
Attrib PopulationDensity -0.2663352902864381
Attrib HouseNumber 0.5153046727550502
Sigmoid Node 4
Inputs Weights
Threshold -1.3273158445760431
Attrib MinTemp -0.511476470658412
Attrib MaxTemp -1.4472764735477759
Attrib MeanTemp -0.992550007766579
Attrib RelativeHumidity -0.4889201348001783
Attrib Rainfall 4.777705232733897
Attrib Wind 1.0057960261924193
Attrib LandArea 0.01594686951090471
Attrib IncomeClass -0.012053049723794618
Attrib WasteGenerated -0.29397677127551647
Attrib PopulationDensity 0.8760275665744505
Attrib HouseNumber 0.26513119051179107
Sigmoid Node 5
Inputs Weights
Threshold 0.9085281334048771
Attrib MinTemp -2.3264253136843633
Attrib MaxTemp 4.342385678707546
Attrib MeanTemp 1.26274142914379
Attrib RelativeHumidity 0.3589371377240767
Attrib Rainfall -6.060544069949767
Attrib Wind -1.7001357028288409
Attrib LandArea -0.04696606932834255
Attrib IncomeClass -0.02765457448569584
Attrib WasteGenerated -4.685692052378084
Attrib PopulationDensity 0.7497806979087069
Attrib HouseNumber -1.817884131764966
Sigmoid Node 6
Inputs Weights
Threshold -2.343332128576834
Attrib MinTemp -1.7808827758329944
Attrib MaxTemp 2.3738961064086217
Attrib MeanTemp 0.6053466030736496
Attrib RelativeHumidity 0.4178221348007889
Attrib Rainfall 0.2646387686505043
Attrib Wind 0.6941590574632328
Attrib LandArea 0.022879267506905346
Attrib IncomeClass -0.030599400189594162
Attrib WasteGenerated 0.2341906598765536
Attrib PopulationDensity -0.054518515830522876
Attrib HouseNumber -0.6802930287343757
Class
Input
Node 0
Time taken to build model: 17.83 seconds
=== Evaluation on training set ===
=== Summary ===
Correlation coefficient 0.7747
Mean absolute error 1.477
Root mean squared error 1.9605
Relative absolute error 110.9364 %
Root relative squared error 86.4544 %
Total Number of Instances 518
Ignored Class Unknown Instances 2