ZIP1004551543418023建模代码.zip 934.12KB

2403_86257988需要积分:1(1积分=1元)

资源文件列表:

1004551543418023建模代码.zip 大约有39个文件
  1. .ipynb_checkpoints/
  2. BP神经网络/
  3. BP神经网络/.ipynb_checkpoints/
  4. BP神经网络/.ipynb_checkpoints/BP神经网络-checkpoint.ipynb 35.5KB
  5. BP神经网络/BP神经网络.ipynb 37.58KB
  6. LSTM/
  7. LSTM/.ipynb_checkpoints/
  8. LSTM/.ipynb_checkpoints/LSTM-checkpoint.ipynb 34.2KB
  9. LSTM/LSTM.ipynb 7.41KB
  10. SVM/
  11. SVM/.ipynb_checkpoints/
  12. SVM/.ipynb_checkpoints/直接预测，可视化使用降维-checkpoint.ipynb 155.01KB
  13. SVM/.ipynb_checkpoints/降维后预测-checkpoint.ipynb 154.55KB
  14. SVM/直接预测，可视化使用降维.ipynb 155.97KB
  15. SVM/降维后预测.ipynb 154.55KB
  16. XGBoost/
  17. XGBoost/.ipynb_checkpoints/
  18. XGBoost/.ipynb_checkpoints/分类-checkpoint.ipynb 21.17KB
  19. XGBoost/.ipynb_checkpoints/回归-checkpoint.ipynb 4.93KB
  20. XGBoost/XGBoost模型.png 92.47KB
  21. XGBoost/分类.ipynb 21.17KB
  22. XGBoost/回归.ipynb 4.93KB
  23. XGBoost/新建 Microsoft PowerPoint 演示文稿.pptx 62.41KB
  24. 朴素贝叶斯/
  25. 朴素贝叶斯/.ipynb_checkpoints/
  26. 朴素贝叶斯/.ipynb_checkpoints/朴素贝叶斯分类-checkpoint.ipynb 60.7KB
  27. 朴素贝叶斯/朴素贝叶斯分类.ipynb 60.7KB
  28. 灰色关联/
  29. 灰色关联/.ipynb_checkpoints/
  30. 灰色关联/.ipynb_checkpoints/Untitled-checkpoint.ipynb 30.85KB
  31. 灰色关联/Untitled.ipynb 30.85KB
  32. 遗传算法优化的BP神经网络/
  33. 遗传算法优化的BP神经网络/.ipynb_checkpoints/
  34. 遗传算法优化的BP神经网络/.ipynb_checkpoints/Untitled-checkpoint.ipynb 59.66KB
  35. 遗传算法优化的BP神经网络/Untitled.ipynb 59.49KB
  36. 鲸鱼算法优化的BP神经网络/
  37. 鲸鱼算法优化的BP神经网络/.ipynb_checkpoints/
  38. 鲸鱼算法优化的BP神经网络/.ipynb_checkpoints/Untitled-checkpoint.ipynb 67.04KB
  39. 鲸鱼算法优化的BP神经网络/Untitled.ipynb 67.04KB

资源介绍:

1004551543418023建模代码.zip
<link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89553977/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89553977/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">1<span class="ff2">棵<span class="ff3">树</span>模型</span></div><div class="t m1 x2 h3 y1 ff4 fs1 fc0 sc0 ls0 ws0">K</div><div class="t m0 x3 h2 y1 ff2 fs0 fc0 sc0 ls0 ws0">棵<span class="ff3">树</span>模型<span class="_ _0"></span><span class="ff1">2<span class="ff2">棵<span class="ff3">树</span>模型</span></span></div><div class="t m0 x4 h2 y2 ff3 fs0 fc0 sc0 ls0 ws0">总训练样<span class="ff2">本集</span></div><div class="t m0 x5 h4 y3 ff1 fs0 fc0 sc0 ls0 ws0">首次</div><div class="t m0 x6 h4 y4 ff1 fs0 fc0 sc0 ls0 ws0">有放回抽样</div><div class="t m0 x7 h4 y5 ff1 fs0 fc0 sc0 ls0 ws0">建</div><div class="t m0 x7 h4 y6 ff1 fs0 fc0 sc0 ls0 ws0">模</div><div class="t m0 x8 h4 y7 ff1 fs0 fc0 sc0 ls0 ws0">增加1</div><div class="t m0 x9 h4 y8 ff1 fs0 fc0 sc0 ls0 ws0">棵新的</div><div class="t m0 x9 h4 y9 ff1 fs0 fc0 sc0 ls0 ws0">树模型</div><div class="t m0 xa h4 ya ff1 fs0 fc0 sc0 ls0 ws0">增加1</div><div class="t m0 xb h4 yb ff1 fs0 fc0 sc0 ls0 ws0">棵新的</div><div class="t m0 xb h4 yc ff1 fs0 fc0 sc0 ls0 ws0">树模型</div><div class="t m0 xc h4 yd ff1 fs0 fc0 sc0 ls0 ws0">第2次</div><div class="t m0 xd h4 ye ff1 fs0 fc0 sc0 ls0 ws0">有放回抽样</div><div class="t m0 xe h4 yf ff1 fs0 fc0 sc0 ls0 ws0">加大第1次预测</div><div class="t m0 xf h4 y10 ff1 fs0 fc0 sc0 ls0 ws0">错误的样本权重</div><div class="t m0 x10 h4 y11 ff1 fs0 fc0 sc0 ls0 ws0">第</div><div class="t m1 x11 h3 y11 ff4 fs1 fc0 sc0 ls0 ws0">K</div><div class="t m0 x12 h4 y11 ff1 fs0 fc0 sc0 ls0 ws0">次</div><div class="t m0 x13 h4 y12 ff1 fs0 fc0 sc0 ls0 ws0">有放回抽样</div><div class="t m0 x14 h4 y13 ff1 fs0 fc0 sc0 ls0 ws0">前(</div><div class="t m1 x15 h3 y13 ff4 fs1 fc0 sc0 ls0 ws0">k</div><div class="t m0 x16 h4 y13 ff1 fs0 fc0 sc0 ls0 ws0">-1)次预测错误的样本都</div><div class="t m0 x14 h4 y14 ff1 fs0 fc0 sc0 ls0 ws0">被赋予更大的权重，且预测</div><div class="t m0 x17 h4 y15 ff1 fs0 fc0 sc0 ls0 ws0">错误次数越多，权重越大</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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