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   "source": [
    "# %load ../../preconfig.py\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)\n",
    "plt.rcParams['axes.grid'] = False\n",
    "\n",
    "#import numpy as np\n",
    "#import pandas as pd\n",
    "\n",
    "#import sklearn\n",
    "\n",
    "#import itertools\n",
    "\n",
    "import logging\n",
    "logger = logging.getLogger()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 Linear Methods for Regression\n",
    "=================\n",
    "\n",
    "### 3.1 Introduction\n",
    "For prediction purposes linear methods can sometimes outperform fancier nonlinear models, especially in situations with small numbers of training cases, low signal-to-noise ratio or sparse data.\n",
    "\n",
    "linear methods $\\to$ basis-function methods.\n",
    "\n",
    "an understanding of linear methods is essential for understanding nonlinear ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Linear Regression Models and Least Squares\n"
   ]
  }
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