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@Senl 2018-02-04T14:56:05.000000Z 字数 2573 阅读 1305

美赛模拟画图文档

美赛建模

1.男女分用图

1.1 Linear Features

Sex Age Charater Nervousness Psycoticism Relibility
male 0.5739 0.0131 0.6756 0.1431 0.1738
female 0.6125 0.1242 0.4717 0.2039 0.1309
...................... ...................... ...................... ....................... ...................... ......................

1.2 Ridge Features

Sex Age Charater Nervousness Psycoticism Relibility
male 0.5641 0.0140 0.6667 0.1457 0.1715
female 0.6081 0.1241 0.466 0.2011 0.1297
...................... ...................... ...................... ....................... ...................... ......................

1.3 分别对应的权重图

1 代表权重最低 5代表权重最高

Sex Age Charater Nervousness Psycoticism Relibility
male 4 1 5 2 3
female 5 1 4 3 2
...................... ...................... ...................... ....................... ...................... ......................

x.敏感性分析图

x.1 不分男女原始数据的分析

Model Age Charater Nervousness Psycoticism Relibility
Linear Features 0.6025 0.0943 0.6414 0.2509 0.0468
level 4 2 5 3 1
Ridge Features 0.5996 0.0945 0.6357 0.2501 0.0459
level 4 2 5 3 1
................................ ...................... ...................... ....................... ...................... ......................

x.2 不分男女原始数据的权重图

Model Age Charater Nervousness Psycoticism Relibility
Linear Features 4 2 5 3 1
Ridge Features 4 2 5 3 1
................................ ...................... ...................... ....................... ...................... ......................

x.3 不分男女的乘比例系数后的分析

Model Age Charater Nervousness Psycoticism Relibility
Linear Features 0.8672 0.5019 0.5481 0.0425 0.2875
level 5 3 4 1 2
Ridge Features 0.8518 0.4974 0.2825 0.0366 0.5382
level 5 3 4 1 2
................................ ...................... ...................... ....................... ...................... ......................

x.4 不分男女原始数据的权重图

Model Age Charater Nervousness Psycoticism Relibility
Linear Features 5 3 2 1 4
Ridge Features 5 3 2 1 4
................................ ...................... ...................... ....................... ...................... ......................
Model Male(%) Female(%)
Logtisic regression (prediction accuracy) 80.936 84.295
Logtisic regression (validation accuracy) 87.564 90.512
Linear Support Vector Machine (prediction accuracy) 81.459 82.512
Linear Support Vector Machine (validation accuracy) 83.962 85.962
BP Neural Network (prediction accuracy) 85.785 86.538
BP Neural Network (validation accuracy) 87.689 89.155
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