weka - Weka 上多层感知器建模结果的解释

Question

我正在使用 Weka 上的多层感知器生成功率模型，Weka 是一个统计工具箱。

Weka 显示了以下生成的功率模型，但是，我不知道如何解释它。

如何使用 Weka 生成的模型计算预测值？我想知道如何用模型手动计算它。

谢谢。

=== Classifier model (full training set) ===

Linear Node 0
    Inputs    Weights
    Threshold    -0.040111709313733535
    Node 1    -1.8468414006209548
    Node 2    0.8245441127585728
    Node 3    -0.6384807874184006
    Node 4    -0.7484784535220612
Sigmoid Node 1
    Inputs    Weights
    Threshold    -0.24446294747264816
    Attrib CPU-User    -0.608249350584644
    Attrib CPU-System    0.13288901868419942
    Attrib CPU-Idle    1.0072001456456134
    Attrib GPS    0.39886318520181463
    Attrib WIFI    2.661390547312707
    Attrib Disk-Write    3.3144190265114104
    Attrib Screen    -0.18379082022126372
Sigmoid Node 2
    Inputs    Weights
    Threshold    -0.04552879905091134
    Attrib CPU-User    1.2010400180021503
    Attrib CPU-System    -0.415901207849663
    Attrib CPU-Idle    -1.8201808907618635
    Attrib GPS    0.3297713837591742
    Attrib WIFI    2.670046643619425
    Attrib Disk-Write    1.0132120671943607
    Attrib Screen    1.5785512067159402
Sigmoid Node 3
    Inputs    Weights
    Threshold    -7.438472914350278
    Attrib CPU-User    -6.382669043988483
    Attrib CPU-System    -1.6622872921207548
    Attrib CPU-Idle    -0.12729502604878612
    Attrib GPS    -0.9716992577028621
    Attrib WIFI    0.6911695390337304
    Attrib Disk-Write    -1.1769266028873722
    Attrib Screen    0.5101113538728531
Sigmoid Node 4
    Inputs    Weights
    Threshold    -5.509838959208244
    Attrib CPU-User    -0.3709271557180943
    Attrib CPU-System    -1.7448007514288941
    Attrib CPU-Idle    -0.08176108597065958
    Attrib GPS    -1.0234447340811823
    Attrib WIFI    -1.5759133030274077
    Attrib Disk-Write    0.2376861365371351
    Attrib Screen    -1.5654514081278506
Class 
    Input
    Node 0


Time taken to build model: 0.81 seconds

=== Predictions ontest split===

inst#,    actual, predicted, error
     1 153727.273 169587.843  15860.57 
     2 159036.364 168657.043   9620.68 
     ....

Answer 1

This presentation给出了用于神经网络的背景和方程的一些细节。 Weka 的输出为您提供每个节点的类型以及输入和权重。您应该能够使用该信息自行计算数字。