{
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    {
      "cell_type": "code",
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# LCEClassifier on Iris dataset\n\nAn example of :class:`lce.LCEClassifier`\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from lce import LCEClassifier\nfrom sklearn.datasets import load_iris\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\n\n\n# Load data and generate a train/test split\ndata = load_iris()\nX_train, X_test, y_train, y_test = train_test_split(\n    data.data, data.target, random_state=0\n)\n\n# Train LCEClassifier with default parameters\nclf = LCEClassifier(n_jobs=-1, random_state=0)\nclf.fit(X_train, y_train)\n\n# Make prediction and compute accuracy score\ny_pred = clf.predict(X_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(\"Accuracy: {:.1f}%\".format(accuracy * 100))"
      ]
    }
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