02_classification.ipynb

    "For instance, to classify images of digits:\n",
    "\n",
    "- we could build 10 classifiers `is it 0 or other digit`, `is it 1 or other digit`, etc.\n",
    "  \n",
    "  A new image then would hopefully yield `True` for exactly one of the classifier, in other situations the result is unclear.\n",
    "   \n",
    "   \n",
    "- we could build 45 classifiers `is it 0 or 1`, `is it 0 or 2`, etc.\n",
    "\n",
    "  For a new image we could choose the final outcome based on which class \"wins\" most often.\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-info\"><p><i class=\"fa fa-info-circle\"></i>&nbsp;\n",
    "    In <code>scikit-learn</code> many classifiers support multi-class problems out of the box and also offer functionalities to implement <strong>one-vs-all</strong> or <strong>one-vs-one</strong> in some cases (cf. <a href=\"https://scikit-learn.org/stable/modules/multiclass.html\"><code>scikit-learn</code> multiclass and multilabel algorithms</a>).\n",
    "</p></div>"
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