Question

我是使用WEKA的新手，所以你能解释一下我尝试使用MultilayerPerceptron（神经网络）训练数据后得到的结果：

您还可以至少给我一些可以帮助我理解这一点的链接吗？

=== 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

Answer 1

您针对数据运行了多层感知器（MLP）算法。 MLP使用反向传播来对实例进行分类。我会假设您熟悉基本统计，反向传播和人工神经网络，因为您选择了这种特殊的算法来训练您的模型。如果不是这种情况，那么您已经将购物车放在马前，并且需要在使用此模型之前学习数学。 Here is a training presentation that may help you if this is the case.

在说出“运行信息”之后，＆＃39;它显示了您运行的命令以及您设置的所有参数（在Weka documentation中说明 - 您选择它们或至少使用默认值）。在此之后，它显示您正在使用登革热文件（可能是与受感染者的发烧和人口统计相关的数据，但是由于您选择了这些数据，我认为您对如何收集数据以及数据是什么有基本的了解）。实例是数据文件中的样本数，属性是列数。

sigmoid节点是backpropogation中使用的节点和相关数据。这是网络本身（其权重和属性）。此网络的隐藏层中的节点都是S形，但输出节点是线性单元（例如，线性节点0是您的输出单元，S形节点1-6是您的六个隐藏单元。给出的所有值都是您的互连权重。您可以使用它们手动计算结果（在网络下方为您完成）。

正如我刚才所说，底部是从网络计算的最终结果。这部分是所有基本统计数据，所以我不会再进一步阐述。

解释我从WEKA得到的结果

1 个答案: