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    {
      "cell_type": "code",
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      "source": [
        "%matplotlib inline"
      ]
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      "source": [
        "\n# LCERegressor on Diabetes dataset\n\nAn example of :class:`lce.LCERegressor`\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
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      "source": [
        "from lce import LCERegressor\nfrom sklearn.datasets import load_diabetes\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split\n\n\n# Load data and generate a train/test split\ndata = load_diabetes()\nX_train, X_test, y_train, y_test = train_test_split(\n    data.data, data.target, random_state=0\n)\n\n# Train LCERegressor with default parameters\nreg = LCERegressor(n_jobs=-1, random_state=0)\nreg.fit(X_train, y_train)\n\n# Make prediction\ny_pred = reg.predict(X_test)\nmse = mean_squared_error(y_test, y_pred)\nprint(\"The mean squared error (MSE) on test set: {:.0f}\".format(mse))"
      ]
    }
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