dev/notebooks/auto_examples/svm/plot_svm_regression.ipynb

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    {
      "cell_type": "markdown",
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      "source": [
        "\n# Support Vector Regression (SVR) using linear and non-linear kernels\n\nToy example of 1D regression using linear, polynomial and RBF kernels.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Authors: The scikit-learn developers\n# SPDX-License-Identifier: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.svm import SVR"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Generate sample data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "X = np.sort(5 * np.random.rand(40, 1), axis=0)\ny = np.sin(X).ravel()\n\n# add noise to targets\ny[::5] += 3 * (0.5 - np.random.rand(8))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Fit regression model\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "svr_rbf = SVR(kernel=\"rbf\", C=100, gamma=0.1, epsilon=0.1)\nsvr_lin = SVR(kernel=\"linear\", C=100, gamma=\"auto\")\nsvr_poly = SVR(kernel=\"poly\", C=100, gamma=\"auto\", degree=3, epsilon=0.1, coef0=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Look at the results\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "lw = 2\n\nsvrs = [svr_rbf, svr_lin, svr_poly]\nkernel_label = [\"RBF\", \"Linear\", \"Polynomial\"]\nmodel_color = [\"m\", \"c\", \"g\"]\n\nfig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)\nfor ix, svr in enumerate(svrs):\n    axes[ix].plot(\n        X,\n        svr.fit(X, y).predict(X),\n        color=model_color[ix],\n        lw=lw,\n        label=\"{} model\".format(kernel_label[ix]),\n    )\n    axes[ix].scatter(\n        X[svr.support_],\n        y[svr.support_],\n        facecolor=\"none\",\n        edgecolor=model_color[ix],\n        s=50,\n        label=\"{} support vectors\".format(kernel_label[ix]),\n    )\n    axes[ix].scatter(\n        X[np.setdiff1d(np.arange(len(X)), svr.support_)],\n        y[np.setdiff1d(np.arange(len(X)), svr.support_)],\n        facecolor=\"none\",\n        edgecolor=\"k\",\n        s=50,\n        label=\"other training data\",\n    )\n    axes[ix].legend(\n        loc=\"upper center\",\n        bbox_to_anchor=(0.5, 1.1),\n        ncol=1,\n        fancybox=True,\n        shadow=True,\n    )\n\nfig.text(0.5, 0.04, \"data\", ha=\"center\", va=\"center\")\nfig.text(0.06, 0.5, \"target\", ha=\"center\", va=\"center\", rotation=\"vertical\")\nfig.suptitle(\"Support Vector Regression\", fontsize=14)\nplt.show()"
      ]
    }
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