{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      },
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Sensivity analysis\n\n\nIn this plot we perform a sensitivity analysis of the `n_estimators`\nparameter and we can see how different values affect the performance\nof the classifier.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "print(__doc__)\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport dagging\n\nfrom sklearn.datasets import load_digits\nfrom sklearn.model_selection import validation_curve\n\nX, y = load_digits(return_X_y=True)\n\nparam_range = range(2, 21)\ntrain_scores, test_scores = validation_curve(\n    dagging.DaggingClassifier(random_state=0),\n    X,\n    y,\n    param_name=\"n_estimators\",\n    param_range=param_range,\n    cv=5,\n    scoring=\"balanced_accuracy\",\n    n_jobs=1,\n)\ntrain_scores_mean = np.mean(train_scores, axis=1)\ntrain_scores_std = np.std(train_scores, axis=1)\ntest_scores_mean = np.mean(test_scores, axis=1)\ntest_scores_std = np.std(test_scores, axis=1)\n\nplt.title(\"Validation Curves with Dagging\")\nplt.xlabel(\"n_bins\")\nplt.ylabel(\"Score\")\nplt.ylim(0.0, 1.0)\nplt.xticks(param_range)\n\nlw = 2\nplt.plot(\n    param_range, train_scores_mean, label=\"Training score\", color=\"darkorange\", lw=lw\n)\nplt.fill_between(\n    param_range,\n    train_scores_mean - train_scores_std,\n    train_scores_mean + train_scores_std,\n    alpha=0.2,\n    color=\"darkorange\",\n    lw=lw,\n)\nplt.plot(\n    param_range, test_scores_mean, label=\"Cross-validation score\", color=\"navy\", lw=lw\n)\nplt.fill_between(\n    param_range,\n    test_scores_mean - test_scores_std,\n    test_scores_mean + test_scores_std,\n    alpha=0.2,\n    color=\"navy\",\n    lw=lw,\n)\nplt.legend(loc=\"best\")\nplt.show()"
      ]
    }
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      "file_extension": ".py",
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