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    {
      "cell_type": "code",
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        "%matplotlib inline"
      ]
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      "source": [
        "\n# Cross-validation on Digits Dataset Exercise\n\nA tutorial exercise using Cross-validation with an SVM on the Digits dataset.\n\nThis exercise is used in the `cv_generators_tut` part of the\n`model_selection_tut` section of the `stat_learn_tut_index`.\n"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "import numpy as np\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn import datasets, svm\n\nX, y = datasets.load_digits(return_X_y=True)\n\nsvc = svm.SVC(kernel=\"linear\")\nC_s = np.logspace(-10, 0, 10)\n\nscores = list()\nscores_std = list()\nfor C in C_s:\n    svc.C = C\n    this_scores = cross_val_score(svc, X, y, n_jobs=1)\n    scores.append(np.mean(this_scores))\n    scores_std.append(np.std(this_scores))\n\n# Do the plotting\nimport matplotlib.pyplot as plt\n\nplt.figure()\nplt.semilogx(C_s, scores)\nplt.semilogx(C_s, np.array(scores) + np.array(scores_std), \"b--\")\nplt.semilogx(C_s, np.array(scores) - np.array(scores_std), \"b--\")\nlocs, labels = plt.yticks()\nplt.yticks(locs, list(map(lambda x: \"%g\" % x, locs)))\nplt.ylabel(\"CV score\")\nplt.xlabel(\"Parameter C\")\nplt.ylim(0, 1.1)\nplt.show()"
      ]
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