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      "cell_type": "code",
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      "source": [
        "%matplotlib inline"
      ]
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      "source": [
        "# Recursive feature elimination\n\nAn example of ``scikit-learn``, copied from\n[scikit-learn.org](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html#sphx-glr-auto-examples-feature-selection-plot-rfe-digits-py).\n\nA recursive feature elimination example showing the relevance of pixels in\na digit classification task.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>See also `sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`</p></div>\n"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
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      "source": [
        "# noqa: E501\n\nfrom sklearn.svm import SVC\nfrom sklearn.datasets import load_digits\nfrom sklearn.feature_selection import RFE\nimport matplotlib.pyplot as plt\n\n# Load the digits dataset\ndigits = load_digits()\nX = digits.images.reshape((len(digits.images), -1))\ny = digits.target\n\n# Create the RFE object and rank each pixel\nsvc = SVC(kernel=\"linear\", C=1)\nrfe = RFE(estimator=svc, n_features_to_select=1, step=1)\nrfe.fit(X, y)\nranking = rfe.ranking_.reshape(digits.images[0].shape)\n\n# Plot pixel ranking\nplt.matshow(ranking, cmap=plt.cm.Blues)\nplt.colorbar()\nplt.title(\"Ranking of pixels with RFE\")\nplt.show()"
      ]
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