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第三问-核函数选取.md

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105 lines (105 loc) · 3.79 KB

xy_train <- read.csv("xy_train.csv")

  • SVM-Type: C-classification
  • SVM-Kernel: linear
  • cost: 1
  • Number of Support Vectors: 1277
  • Train Time: 18.40783 secs
  • Train Accuracy: 0.997038753701558 within 6.554533 secs
  • Test Accuracy: 0.942441492726123 within 3.399499 secs
  • SVM-Type: C-classification
  • SVM-Kernel: radial
  • cost: 1
  • Number of Support Vectors: 2782
  • Train Time: 42.78459 secs
  • Train Accuracy: 0.985837517703103 within 16.28941 secs
  • Test Accuracy: 0.936748893105629 within 6.785011 secs
  • SVM-Type: C-classification
  • SVM-Kernel: polynomial
  • cost: 1
  • degree: 3
  • coef.0: 0
  • Number of Support Vectors: 3201
  • Train Time: 49.27327 secs
  • Train Accuracy: 0.977211278485902 within 17.5321 secs
  • Test Accuracy: 0.910815939278937 within 7.51905 secs
  • SVM-Type: C-classification
  • SVM-Kernel: sigmoid
  • cost: 1
  • coef.0: 0
  • Number of Support Vectors: 2946
  • Train Time: 39.97094 secs
  • Train Accuracy: 0.823998970001288 within 16.06575 secs
  • Test Accuracy: 0.794433902593295 within 6.99282 secs

xy_filtered <- xy_train[, c(1:6, 41:46, 81:86, 121:126, 200, 201, 214, 215, 227, 228, 253, 254, 555:562)]

  • SVM-Type: C-classification
  • SVM-Kernel: linear
  • cost: 1
  • Number of Support Vectors: 1549
  • Train Time: 1.334609 secs
  • Train Accuracy: 0.956482554396807 within 0.5409529 secs
  • Test Accuracy: 0.88741302972802 within 0.23347 secs
  • SVM-Type: C-classification
  • SVM-Kernel: radial
  • cost: 1
  • Number of Support Vectors: 2842
  • Train Time: 4.478163 secs
  • Train Accuracy: 0.95661130423587 within 1.73843 secs
  • Test Accuracy: 0.889310562934851 within 0.7992141 secs
  • SVM-Type: C-classification
  • SVM-Kernel: polynomial
  • cost: 1
  • degree: 3
  • coef.0: 0
  • Number of Support Vectors: 3229
  • Train Time: 2.69545 secs
  • Train Accuracy: 0.941418823226471 within 1.11695 secs
  • Test Accuracy: 0.851043643263757 within 0.4705739 secs
  • SVM-Type: C-classification
  • SVM-Kernel: sigmoid
  • cost: 1
  • coef.0: 0
  • Number of Support Vectors: 3278
  • Train Time: 3.125819 secs
  • Train Accuracy: 0.752414059482426 within 1.340005 secs
  • Test Accuracy: 0.749841872232764 within 0.5437839 secs

xy_improved <- xy_train[, c(1:6, 41:46, 81:86, 121:126, 161:166, 200, 201, 214, 215, 227, 228, 240, 241, 253, 254, 555:562)]

  • SVM-Type: C-classification
  • SVM-Kernel: linear
  • cost: 1
  • Number of Support Vectors: 1513
  • Train Time: 2.483219 secs
  • Train Accuracy: 0.962791296510879 within 1.123421 secs
  • Test Accuracy: 0.899746995572423 within 0.382067 secs
  • SVM-Type: C-classification
  • SVM-Kernel: radial
  • cost: 1
  • Number of Support Vectors: 2963
  • Train Time: 8.446157 secs
  • Train Accuracy: 0.957512553109309 within 3.302577 secs
  • Test Accuracy: 0.894686907020873 within 1.271082 secs
  • SVM-Type: C-classification
  • SVM-Kernel: polynomial
  • cost: 1
  • degree: 3
  • coef.0: 0
  • Number of Support Vectors: 3306
  • Train Time: 5.285049 secs
  • Train Accuracy: 0.943865070168662 within 2.138298 secs
  • Test Accuracy: 0.85325743200506 within 0.8982859 secs
  • SVM-Type: C-classification
  • SVM-Kernel: sigmoid
  • cost: 1
  • coef.0: 0
  • Number of Support Vectors: 3309
  • Train Time: 5.668895 secs
  • Train Accuracy: 0.757692802883996 within 2.711508 secs
  • Test Accuracy: 0.74067046173308 within 0.9908209 secs