plot_cv_predict.ipynb

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    "\n",
    "# Plotting Cross-Validated Predictions\n",
    "\n",
    "\n",
    "This example shows how to use `cross_val_predict` to visualize prediction\n",
    "errors.\n",
    "\n"
   ]
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   "cell_type": "code",
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   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "lr = linear_model.LinearRegression()\n",
    "boston = datasets.load_boston()\n",
    "y = boston.target\n",
    "\n",
    "# cross_val_predict returns an array of the same size as `y` where each entry\n",
    "# is a prediction obtained by cross validation:\n",
    "predicted = cross_val_predict(lr, boston.data, y, cv=10)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(y, predicted)\n",
    "ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)\n",
    "ax.set_xlabel('Measured')\n",
    "ax.set_ylabel('Predicted')\n",
    "plt.show()"
   ]
  }
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