{"id":12362,"date":"2024-10-23T21:36:01","date_gmt":"2024-10-23T12:36:01","guid":{"rendered":"http:\/\/pineplanter.moo.jp\/non-it-salaryman\/?p=12362"},"modified":"2024-10-23T21:36:01","modified_gmt":"2024-10-23T12:36:01","slug":"sklearn-rbf","status":"publish","type":"post","link":"http:\/\/pineplanter.moo.jp\/non-it-salaryman\/2024\/10\/23\/sklearn-rbf\/","title":{"rendered":"\u3010Python\/scikit-learn\u3011SVM\u3067\u975e\u7dda\u5f62\u30c7\u30fc\u30bf\u3092\u5206\u985e\uff01RBF\u30ab\u30fc\u30cd\u30eb\u3092\u4f7f\u3063\u305f\u7c21\u5358\u306a\u89e3\u8aac\u3068\u5b9f\u88c5"},"content":{"rendered":"

\u30b5\u30dd\u30fc\u30c8\u30d9\u30af\u30bf\u30de\u30b7\u30f3\uff08SVM\uff09\u306f\u3001\u30c7\u30fc\u30bf\u5206\u985e\u306e\u5f37\u529b\u306a\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u4e00\u3064\u3067\u3059\u304c\u3001\u5358\u7d14\u306a\u7dda\u5f62\u5206\u96e2\u3060\u3051\u3067\u306a\u304f\u975e\u7dda\u5f62\u30c7\u30fc\u30bf\u306b\u3082\u5bfe\u5fdc\u3067\u304d\u307e\u3059\u3002<\/p>\n

RBF\u30ab\u30fc\u30cd\u30eb\uff08Radial Basis Function Kernel\uff09\u3092\u4f7f\u3046\u3053\u3068\u3067SVM\u306f\u5165\u529b\u30c7\u30fc\u30bf\u3092\u9ad8\u6b21\u5143\u7a7a\u9593\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3057\u8907\u96d1\u306a\u30d1\u30bf\u30fc\u30f3\u3092\u6349\u3048\u308b\u3053\u3068\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002Scikit-learn\u3092\u4f7f\u3063\u3066RBF\u30ab\u30fc\u30cd\u30eb\u3092\u9069\u7528\u3057\u305fSVM\u306e\u5b9f\u88c5\u65b9\u6cd5\u3092\u89e3\u8aac\u3057\u3001\u3069\u306e\u3088\u3046\u306b\u975e\u7dda\u5f62\u30c7\u30fc\u30bf\u3092\u52b9\u679c\u7684\u306b\u5206\u985e\u3067\u304d\u308b\u306e\u304b\u3092\u5b9f\u969b\u306e\u30b3\u30fc\u30c9\u3068\u3068\u3082\u306b\u7d39\u4ecb\u3057\u307e\u3059\u3002<\/p>\n

RBF\u30ab\u30fc\u30cd\u30eb\u306e\u7279\u5fb4<\/h2>\n

\u975e\u7dda\u5f62\u306e\u554f\u984c\u306b\u9069\u7528\u53ef\u80fd<\/strong><\/h5>\n

\u7dda\u5f62\u5206\u96e2\u3067\u304d\u306a\u3044\u8907\u96d1\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u3082\u3001\u9ad8\u6b21\u5143\u7a7a\u9593\u3067\u306e\u7dda\u5f62\u5206\u96e2\u3092\u5b9f\u73fe\u3057\u307e\u3059\u3002<\/p>\n

\u30ac\u30f3\u30de\u30d1\u30e9\u30e1\u30fc\u30bf (gamma<\/code>)<\/strong><\/h5>\n

RBF\u30ab\u30fc\u30cd\u30eb\u306f\u3001\u30ac\u30f3\u30de\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u7528\u3044\u3066\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u306e\u5f71\u97ff\u7bc4\u56f2\u3092\u8abf\u6574\u3057\u307e\u3059\u3002gamma<\/code>\u304c\u5927\u304d\u3044\u307b\u3069\u3001\u30e2\u30c7\u30eb\u304c\u5404\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u306b\u654f\u611f\u306b\u306a\u308a\u3001\u5c40\u6240\u7684\u306b\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0\u3055\u308c\u307e\u3059\u304c\u3001\u904e\u5270\u9069\u5408\u306e\u30ea\u30b9\u30af\u304c\u9ad8\u307e\u308a\u307e\u3059\u3002\u9006\u306bgamma<\/code>\u304c\u5c0f\u3055\u3044\u3068\u3001\u30c7\u30fc\u30bf\u5168\u4f53\u306b\u5bfe\u3057\u3066\u7de9\u3084\u304b\u306b\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0\u3055\u308c\u307e\u3059\u3002<\/p>\n

\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u4f7f\u308f\u308c\u308b\u30ab\u30fc\u30cd\u30eb<\/strong><\/h5>\n

SVC\u30af\u30e9\u30b9\u3067\u306f\u3001RBF\u30ab\u30fc\u30cd\u30eb\u304c\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u30ab\u30fc\u30cd\u30eb\u3068\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u3064\u307e\u308a\u3001SVC()<\/code>\u3068\u66f8\u304f\u3060\u3051\u3067RBF\u30ab\u30fc\u30cd\u30eb\u304c\u9069\u7528\u3055\u308c\u307e\u3059\u3002<\/p>\n

 <\/p>\n

RBF\u30ab\u30fc\u30cd\u30eb\u3092\u7528\u3044\u305fSVM\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u4f8b<\/h2>\n

RBF\u30ab\u30fc\u30cd\u30eb\u3092\u7528\u3044\u305fSVM\u306e\u5b9f\u88c5\u4f8b\u3092\u793a\u3057\u307e\u3059\u3002\u3053\u306e\u4f8b\u3067\u306f\u3001Scikit-learn\u306emake_circles\u95a2\u6570\u3092\u4f7f\u3063\u3066\u975e\u7dda\u5f62\u306a\u30c7\u30fc\u30bf\u3092\u751f\u6210\u3057RBF\u30ab\u30fc\u30cd\u30eb\u3067\u5206\u985e\u3057\u307e\u3059\u3002<\/p>\n

\r\n# \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.datasets import make_circles\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.metrics import accuracy_score\r\n\r\n# \u975e\u7dda\u5f62\u30c7\u30fc\u30bf\u3092\u751f\u6210\uff082\u3064\u306e\u540c\u5fc3\u5186\uff09\r\nX, y = make_circles(n_samples=100, factor=0.3, noise=0.1, random_state=42)\r\n\r\n# \u8a13\u7df4\u30c7\u30fc\u30bf\u3068\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306b\u5206\u5272\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\r\n\r\n# SVM\u30e2\u30c7\u30eb\u306e\u4f5c\u6210\uff08RBF\u30ab\u30fc\u30cd\u30eb\u3092\u4f7f\u7528\uff09\r\nsvm_model = SVC(kernel='rbf', gamma=1.0)\r\n\r\n# \u30e2\u30c7\u30eb\u306e\u8a13\u7df4\r\nsvm_model.fit(X_train, y_train)\r\n\r\n# \u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u4f7f\u3063\u3066\u4e88\u6e2c\r\ny_pred = svm_model.predict(X_test)\r\n\r\n# \u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u3092\u8a08\u7b97\r\naccuracy = accuracy_score(y_test, y_pred)\r\nprint(f"Accuracy: {accuracy * 100:.2f}%")\r\n\r\n# \u5883\u754c\u7dda\u3092\u63cf\u753b\r\ndef plot_svm_decision_boundary(model, X, y):\r\n    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\r\n    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\r\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01),\r\n                         np.arange(y_min, y_max, 0.01))\r\n\r\n    # \u5404\u70b9\u306e\u30af\u30e9\u30b9\u3092\u4e88\u6e2c\r\n    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\r\n    Z = Z.reshape(xx.shape)\r\n\r\n    # \u5883\u754c\u7dda\u306e\u63cf\u753b\r\n    plt.contourf(xx, yy, Z, alpha=0.8)\r\n    plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', marker='o')\r\n    plt.title('SVM with RBF Kernel')\r\n    plt.show()\r\n\r\n# \u8a13\u7df4\u30c7\u30fc\u30bf\u3068SVM\u306b\u3088\u308b\u5883\u754c\u7dda\u3092\u63cf\u753b\r\nplot_svm_decision_boundary(svm_model, X_test, y_test)\r\n \r\n<\/pre>\n
 <\/p>\n
\u30b3\u30fc\u30c9\u306e\u8aac\u660e<\/h2>\n
\u30c7\u30fc\u30bf\u751f\u6210<\/h5>\n
make_circles\u95a2\u6570\u3067\u30012\u3064\u306e\u540c\u5fc3\u5186\u3092\u5f62\u6210\u3059\u308b\u975e\u7dda\u5f62\u306a\u30c7\u30fc\u30bf\u3092\u751f\u6210\u3057\u3066\u3044\u307e\u3059\u3002factor=0.3\u3067\u5185\u5074\u3068\u5916\u5074\u306e\u5186\u306e\u9593\u9694\u3092\u8a2d\u5b9a\u3057\u3001noise=0.1\u3067\u30c7\u30fc\u30bf\u306b\u82e5\u5e72\u306e\u30ce\u30a4\u30ba\u3092\u52a0\u3048\u3066\u3044\u307e\u3059\u3002<\/p>\n
SVM\u30e2\u30c7\u30eb\u306e\u4f5c\u6210<\/h5>\n
SVC(kernel=’rbf’, gamma=1.0)\u3067RBF\u30ab\u30fc\u30cd\u30eb\u3092\u6307\u5b9a\u3057\u3001gamma=1.0\u3068\u3057\u3066\u3044\u307e\u3059\u3002gamma\u306e\u5024\u306fRBF\u30ab\u30fc\u30cd\u30eb\u306e\u30ac\u30a6\u30b9\u95a2\u6570\u306e\u5e45\u3092\u8abf\u6574\u3059\u308b\u30d1\u30e9\u30e1\u30fc\u30bf\u3067\u3001\u5927\u304d\u304f\u3059\u308b\u3068\u3088\u308a\u30ed\u30fc\u30ab\u30eb\u306a\u6c7a\u5b9a\u5883\u754c\u3092\u4f5c\u308a\u307e\u3059\u3002<\/p>\n
\u30e2\u30c7\u30eb\u306e\u8a13\u7df4\u3068\u4e88\u6e2c<\/h5>\n
\u8a13\u7df4\u30c7\u30fc\u30bf\u3067SVM\u3092\u8a13\u7df4\u3057\u3001\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3067\u4e88\u6e2c\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n
\u7cbe\u5ea6\u8a55\u4fa1<\/h5>\n
accuracy_score\u3092\u4f7f\u3063\u3066\u30e2\u30c7\u30eb\u306e\u5206\u985e\u7cbe\u5ea6\u3092\u8a08\u7b97\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u6c7a\u5b9a\u5883\u754c\u306e\u53ef\u8996\u5316<\/h5>\n
plot_svm_decision_boundary\u95a2\u6570\u3067\u3001SVM\u304c\u751f\u6210\u3057\u305f\u30af\u30e9\u30b9\u306e\u5883\u754c\u7dda\u3092\u63cf\u753b\u3057\u307e\u3059\u3002\u975e\u7dda\u5f62\u30c7\u30fc\u30bf\u3067\u3042\u308b\u540c\u5fc3\u5186\u306b\u5bfe\u3057\u3066\u3001RBF\u30ab\u30fc\u30cd\u30eb\u3092\u7528\u3044\u308b\u3053\u3068\u3067\u975e\u7dda\u5f62\u306e\u6c7a\u5b9a\u5883\u754c\u304c\u5f62\u6210\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u304c\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u5b9f\u884c\u7d50\u679c<\/h2>\n
\u3053\u3061\u3089\u304c\u5b9f\u884c\u7d50\u679c\u3067\u3059\u3002\u3053\u306e\u30d7\u30ed\u30c3\u30c8\u306f\u3001RBF\u30ab\u30fc\u30cd\u30eb\u3092\u4f7f\u7528\u3057\u3066SVM\u304c\u975e\u7dda\u5f62\u30c7\u30fc\u30bf\u3092\u5206\u985e\u3057\u305f\u7d50\u679c\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\"\"<\/a><\/p>\n
\u80cc\u666f\u306e\u8272\u5206\u3051<\/strong><\/h5>\n
    \n
  • \u7d2b\u8272\u306e\u80cc\u666f\u90e8\u5206<\/strong>\u306f\u3001\u4e00\u65b9\u306e\u30af\u30e9\u30b9\u306b\u5206\u985e\u3055\u308c\u305f\u9818\u57df\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n
  • \u9ec4\u7dd1\u8272\u306e\u80cc\u666f\u90e8\u5206<\/strong>\u306f\u3001\u3082\u3046\u4e00\u65b9\u306e\u30af\u30e9\u30b9\u306b\u5206\u985e\u3055\u308c\u305f\u9818\u57df\u3092\u8868\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n
  • \u3053\u308c\u3089\u306e\u80cc\u666f\u8272\u306e\u5883\u754c\u306f\u3001SVM\u304c\u30c7\u30fc\u30bf\u3092\u5206\u96e2\u3059\u308b\u305f\u3081\u306b\u898b\u3064\u3051\u305f\u975e\u7dda\u5f62\u306a\u5883\u754c\u7dda\u3067\u3059\u3002\u3053\u306e\u5883\u754c\u7dda\u306f\u3001RBF\u30ab\u30fc\u30cd\u30eb\u306b\u3088\u3063\u3066\u30c7\u30fc\u30bf\u304c\u9ad8\u6b21\u5143\u7a7a\u9593\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3055\u308c\u3001\u975e\u7dda\u5f62\u306e\u30d1\u30bf\u30fc\u30f3\u3092\u6349\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u3066\u3044\u307e\u3059\u3002<\/li>\n<\/ul>\n
    \u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8<\/strong><\/h5>\n
      \n
    • \u9ec4\u8272\u306e\u4e38<\/strong>\u306f\u30011\u3064\u76ee\u306e\u30af\u30e9\u30b9\u306b\u5c5e\u3059\u308b\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u3067\u3059\u3002\u3053\u308c\u3089\u306f\u5186\u306e\u4e2d\u5fc3\u4ed8\u8fd1\u306b\u96c6\u307e\u3063\u3066\u3044\u307e\u3059\u3002<\/li>\n
    • \u7d2b\u8272\u306e\u4e38<\/strong>\u306f\u30012\u3064\u76ee\u306e\u30af\u30e9\u30b9\u306b\u5c5e\u3059\u308b\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u3067\u3059\u3002\u3053\u308c\u3089\u306f\u5916\u5074\u306e\u9818\u57df\u306b\u6563\u3089\u3070\u3063\u3066\u3044\u307e\u3059\u3002<\/li>\n
    • \u3053\u306e\u3088\u3046\u306b\u30012\u3064\u306e\u30af\u30e9\u30b9\u306f\u5186\u72b6\u306b\u5206\u5e03\u3057\u3066\u304a\u308a\u3001\u7dda\u5f62\u5206\u96e2\u304c\u4e0d\u53ef\u80fd\u306a\u30d1\u30bf\u30fc\u30f3\u3067\u3059\u3002\u3057\u304b\u3057\u3001RBF\u30ab\u30fc\u30cd\u30eb\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u3053\u306e\u3088\u3046\u306a\u8907\u96d1\u306a\u30d1\u30bf\u30fc\u30f3\u3067\u3082\u6b63\u78ba\u306b\u5206\u985e\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/li>\n<\/ul>\n
      \u5206\u985e\u5883\u754c<\/strong><\/h5>\n
      RBF\u30ab\u30fc\u30cd\u30eb\u306e\u7279\u6027\u306b\u3088\u308a\u3001SVM\u306f\u5186\u5f62\u306e\u5883\u754c\u7dda\u3092\u63cf\u304d\u3001\u4e2d\u5fc3\u306b\u4f4d\u7f6e\u3059\u308b\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\uff08\u9ec4\u8272\u306e\u4e38\uff09\u3092\u5916\u5074\u306e\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\uff08\u7d2b\u8272\u306e\u4e38\uff09\u3068\u5206\u96e2\u3057\u3066\u3044\u307e\u3059\u3002\u3053\u306e\u3088\u3046\u306a\u975e\u7dda\u5f62\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u306f\u3001RBF\u30ab\u30fc\u30cd\u30eb\u306e\u4f7f\u7528\u304c\u975e\u5e38\u306b\u6709\u52b9\u3067\u3059\u3002<\/p>\n
       <\/p>\n
      \u307e\u3068\u3081<\/h2>\n
      \u4eca\u56de\u306e\u4f8b\u306fRBF\u30ab\u30fc\u30cd\u30eb\u3092\u4f7f\u3063\u305fSVM\u304c\u7dda\u5f62\u3067\u306f\u5206\u96e2\u3067\u304d\u306a\u3044\u30c7\u30fc\u30bf\u3092\u3069\u306e\u3088\u3046\u306b\u3057\u3066\u5206\u985e\u3057\u3066\u3044\u308b\u304b\u3092\u8996\u899a\u7684\u306b\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
      SVM\u306f\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u3092\u9ad8\u6b21\u5143\u7a7a\u9593\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3057\u975e\u7dda\u5f62\u306e\u5883\u754c\u3092\u4f5c\u308b\u3053\u3068\u3067\u3001\u4e2d\u5fc3\u306b\u5bc6\u96c6\u3057\u305f\u30af\u30e9\u30b9\u3068\u5916\u5074\u306b\u5e83\u304c\u308b\u30af\u30e9\u30b9\u3092\u3057\u3063\u304b\u308a\u3068\u5206\u96e2\u3057\u3066\u3044\u307e\u3059\u3002\u4eca\u56de\u306e\u4f8b\u306fSVM\u306e\u975e\u7dda\u5f62\u30ab\u30fc\u30cd\u30eb\uff08\u7279\u306bRBF\u30ab\u30fc\u30cd\u30eb\uff09\u304c\u3069\u308c\u307b\u3069\u5f37\u529b\u304b\u3092\u793a\u3057\u3066\u3044\u308b\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n
       <\/p>\n","protected":false},"excerpt":{"rendered":"
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