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   "source": [
    "![](images/EscUpmPolit_p.gif \"UPM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Course Notes for Learning Intelligent Systems"
   ]
  },
  {
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   "metadata": {
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    }
   },
   "source": [
    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "## [Introduction to  Preprocessing](00_Intro_Preprocessing.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "# Binarize Data\n",
    "* We can transform our data using a binary threshold. All values above the threshold are marked 1, and all values equal to or below are marked 0.\n",
    "* This is called binarizing your data or thresholding your data. \n",
    "\n",
    "* It can be helpful when you have probabilities that you want to make crisp values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   },
   "source": [
    "## Binarize Data with Scikit-Learn\n",
    "We can create new binary attributes in Python using Scikit-learn with the Binarizer class.\n",
    "I"
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
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   "source": [
    "from sklearn.preprocessing import Binarizer\n",
    "\n",
    "X = [[ 1., -1.,  2.],\n",
    "     [ 2.,  0.,  0.],\n",
    "     [ 0.,  1.1, -1.]]"
   ]
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   "source": [
    "transformer = Binarizer(threshold=1.0).fit(X) # threshold 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1.],\n",
       "       [1., 0., 0.],\n",
       "       [0., 1., 0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer.transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "# References\n",
    "* [Cleaning and Prepping Data with Python for Data Science — Best Practices and Helpful Packages](https://medium.com/@rrfd/cleaning-and-prepping-data-with-python-for-data-science-best-practices-and-helpful-packages-af1edfbe2a3), DeFilippi, 2019, \n",
    "* [Binarizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Binarizer.html), Scikit Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Licence\n",
    "The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/).  \n",
    "\n",
    "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
   ]
  }
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