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    "## <center> Support Vector Machines (SVMs) in scikit-learn </center>\n",
    "\n",
    "- for classification and regression (SVCs, SVRs)\n",
    "- can be applied on linear and non-linear data\n",
    "- look for the best separating line or decision boundary\n",
    "- look for the largest margin "
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    "![](SVM.png)"
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   "source": [
    "![](SVM2.png)"
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    "![](SVM3.png)"
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    "### Commonly used kernels:\n",
    "\n",
    "- linear\n",
    "- polynomial\n",
    "- radial basis function (RBF) - Gaussian RBF\n",
    "- sigmoid\n",
    "- etc."
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     "text": [
      "The accuracy on the training subset: 1.000\n",
      "The accuracy on the test subset: 0.629\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC \n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, random_state=0)\n",
    "\n",
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "print('The accuracy on the training subset: {:.3f}'.format(svm.score(X_train, y_train)))\n",
    "print('The accuracy on the test subset: {:.3f}'.format(svm.score(X_test, y_test)))"
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