Machine Learning with Scikit-Learn - The Cancer Dataset - 12 - Decision Trees 2.ipynb

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    "### From the Titanic Dataset\n",
    "![](https://upload.wikimedia.org/wikipedia/commons/f/f3/CART_tree_titanic_survivors.png)\n",
    "<div style='text-align:right;'>By Stephen Milborrow (Own work) [CC BY-SA 3.0 via Wikimedia Commons]</div>"
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      "Feature importances: [ 0.  1.]\n"
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SjhYBjxz/Dk8M+Y3V4zN21eG9pwIxfEOd3V0Si/bVI/e8FrfuOv5+erS7GN+/\npDB7/d8tXsABQ+SzGJD9iKNyhKNFwJO1D+LxkCvNFtqMJZDMpADU6wS7uySMt/VpECSY3C8QH4+R\nmgJ6j6hAKB7LNGuVNn/9WFz+oZSjOclnMSD7EXvliDd+/6bDFXZrcyoA8xJIna7xT9YBA39jt7Rx\n/KoO/e+TNM40+CWgv9BHhm2XJBABuPCy1Ow6jNvoTs6Us6ZMPouzLPyErZZnY1vs23/4vcMV9pf6\nByM0SIKzL8sRFRYMlUplGsWYHCPBKwMVWDR/Drbv2mu3vXrJoAD8q8qAbRe1KC7XIae4AYenNbZO\nx4bqzFqljXKKdZiVLLPb/m05LpLIVzFD9nKOyhE7fwzEVVWFw+eJjRDjXwsVWHTIgNpH0pG3fVuz\nEkh6+gTIL+2wfxeHX24RDwCDHpCa7mC85YK22e4LY5mj6RB0a00hvNsIeTuWLPxAS+4qYdkcYrmg\np6oxoNdf6jAjOcispuxKcE/pJkbpzwZTN1/TuRZNWV0ctLEgyZnJ5M0YkP1AS+47Zrkb42RtrNlc\nClt7iR3NPDB7zX310BoErPmlm2/w57VWt8vZWhy0tSDJbXLkrRiQ/YCr+0ctM2prcykW7auHAMHq\n3YadHTTjaqC19TqWC5Kcc0HeigGZmrEMgpZzKSxrupacHV7kaqC19Trjx09AyA87XJ5oR+RpGJDJ\njLW9ypbB195YTCNnhhe1JNBaenmvDlvON+DSnCDOTCavx+FCZMZywIzlXYUBoOB6Y2t09vcNdp/L\n1uAf5+6UDHy5bw+uXFcj+6Tt1wiQAC+lBNocUmQ5GInIFzBD9gPWMldbC22WXJlf0FYzEVqye4TI\nk7ExhEysZa7fzJRDeDvM9C9rUGPzhyAIZv9sBU5rzRrfnCps9vHW/jkK8M5l2rxnG/keliz8QMH3\n3+Fkgf0SAeDaDOIVy5fh1Hcn3VI6cMf1EnkDlizIZWzWIHINSxbkNvfuPGJ7/gQRuY4BmVxiOczI\nOMSIg3+IWo8BmVxi7b57zJKJ2gYDMjnN0ahPZslErcOATE6ztR2NWTJR22BAJqfYyo6NmCUTtR4D\nMjmFzRpE7sfGEHIKmzWI3I8BmZzi7DwLImo5liyIiDwEAzIRkYdwaZZFaGiokJqaavbYuHHj8Prr\nrwMAhg0b1uxjeJzHeZzH/f04Z1kQEXkZTnsjInIzZshERF6GAZmIyEMwIBMReQgGZC+gVCoxd/5C\nRER3hlg49BR7AAAK2ElEQVQiQUR0Z8ydvxBKpbKjL42I2hADsofLz89HUuoAbC28Afn4d/HAazsh\nH/8uthbeQFLqAOTn53f0JRJRG2HrtAdTKpWYMCkDimeWIrB7b9PjssgYyAZPheyh/pgwKQNFZwoQ\nFxfXgVdKRG2BGbIHW5m9GoEJT5sF46YCu/dGYPwIrPrgw3a+MiJyBwZkD5a7eTMC45+ye05gwghs\nyt3cTldERO7EgOzBqqtuQxrexe450rDOqKm63U5XRETuxIDswcIioqC7c9PuObrqWwiNiGqnKyIi\nd2JA9mAZkydDc+GQ3XM0JQcxJWNyO10REbkTA7IHW5y1AJqSA9CUlVo9rikrhebCQSxaOL+dr4yI\n3IEBuYXao1kjLi4O27bkQv3VcqhPbIC2UgVBr4O2UgX1iQ1Qf7Uc27bkcssbkY/wyYDsarB09fz2\nbNYYPXo0is4UYGJqDGrzluLaynTU5i3FxNQYFJ0pwOjRo9vstYioY/nc+M38/HxMmJTRuH83/ilI\nw7tAd+cmNBcOQVNyANu25JoFMeP5AfEjEJQwwnS+umgv6s4fwF/XrMbMmTNN5yuVSiSlDmjWrGGk\nKStF9a7/xvPPPYvde/aiuuo2wiKikDF5MhZnLWA2S+SHnB2/6VMB2Zlgqf5quamzzZnzb2x9E5/+\n9SNTUJ47fyG2Ft6AYvBUm9dReeQzNFwvRdTY1xz+QnDlva3MXo3czZsZ5Im8jF/OQ3a1s21l9moE\nxI+we35oyji8OneBqXzhTLOGInkMGqrKIYuMgUgsgSwyBorBU6F4ZikmTMpwuc7MeRZE/sGnArKr\nnW25mzcjKGGE3fMViaOgF2AK4s42axjuVjd/7Ra0OjedZ6EYPLXNgjwReR6fCsjOBsvqygrMnb8Q\n1dXVuP7JK/jpwym4ffgTaCtVAABtpQq3D3+Cnz6cguufvAJBr8Unn30OpVLpdLOGSCJt9ryA663O\nnGdB5D98KiA7EyzVJUcAqQxbC28gZuZa9Hh9J7pNeR8iaQDKN76Gqm+2oHzjaxBJA9Btyvvo8fpO\nxMxci8D4p5CUOgCDBz3usFlDXbwPisRRZs9bpzwNbaUKNWf+hurqO05vleM8CyL/4VPjNzMmT8bW\nwkOQ2Vhw01aqUPn1p+j64p+ajbOMTJuOkJ6P4sYXbyJ6bBbkvYaYHY8aPguaRx7H17v+GxAMkD3U\n3+w5tJUq1JzdjdoLR2CoU0McHAqIRAhNGYeQno/iZt4fIQhAaPIoxMxca1rs21p4CBtSB9hc7Kuu\nuo1wJ7N+pVLJBT4iL+ZTGbKjzraq4xsRmjjSwSLeWGjK/mHzeHDfpzH8yeFmzRp3//k9yjcshkgi\nRbepK9HjjS/RbeqfTdmxob4W8r4jEPyrFESmZbpUB3a6RBIQzAU+Ii/nUwHZUWdb3eXvoUgaY/c5\nFEmjUVt6zObxwIQROHHiZGNTxsMh+HnDQvz8txXoMv7/InLYDLNgG5k2HV3S38KtXe+ipmAn6v75\nfbOaMmC/DuzMPAv1uf2Q932KC3xEXs6nAjJgv7MNem2Ld0g0PV5TdRuXLl3Cl1/ugji6B0L7P+Nw\n61xo6rOImbnGrKZsdp6NOrAz8yxqznwFQ30txCERXOAj8mI+F5CBxkx5zepsVFXchF6vQ1XFTaxZ\nnY2wSOf+/BeHhNk9Lg+LMG1Fa6gsh6LfSLvPqUgchdp/HDfLmn/es9IsU7Y117hp1l955DOzrL/y\n6Drc3LEMkU++AokiEuUbX4MQ1o0LfEReyqcW9RxxtOgHAOqifMh7p5k9Zlqwu3gMhro7kMgCIesc\nC3FIBAx11S5n3YHde0OR+DRqzu5G1JOzAdifa2zM+ns+0gs1hXsh6LUQh4RB3jsN3ab+GbLIGABo\nXDzcsQxCvdqpzwcReRa/CsiLsxZgQ+qAZjskjDRlpagp3IPoMVmmx+qUp/HznpVQJI5Etynv35t1\nUbwfqvULIZJIobtz0xQUrbGWdSv6jUR57humgOxornFcXBwCAoMQ1G8UItMyrZ4T2L03FP1GoP78\nfnufBiLyUD5ZsrDF0aJf1ZfLIBWLULHvQ1QeXYe6q8X4ec9KdEl/C5Fp080X7IZlouuEPwJiCdTn\n7AdA9bn9zbLuplmzK3ONnSmPiBw+CxF5Ir8KyIDtRb/RD4dADCD6hWWImf4BBL0Ot3YuhzzhSbsL\ndvI+aag5u9fuopu6+ABCU8aZPa6rvgVxcChuH/kUN7a+ibShQ6x+fFPaurtOlUe09XUOn4uIPI/f\nBWTA+qJfWGgYghNHI7B778ZGkCdnQySRITTZ/ja5sEfTAZEIN3csQ+Wx9eaLbl9/jps7lqHT2MXN\nSho1hXsg6LSAwYDO6f8Xf7/teB+xs4uSvMcekXfyqxqyPbmbN0M+/l2zx5xdsBMa6hAz+39Rc3Y3\nynPfgOFuNUQSKQIfTDJbdDPSlJWituQIYjI/uHfswUTIHuqPCZMyTONBLTmzKMl77BF5L7/MkK2x\nNphIHBzm9DY5WWQMQlPGQd47DeLgUAg6LTRXi3F7959Rd7XYqazZ0aAg3mOPyLf51ID61oiI7gz5\n+HfNguTtw59AJA1AZNp0mx9XeWw9BJ0WwbHJpt0Yin5Pm3Zj3D23D3fO7AZ0WkAqQ0ivoQj/zQSb\nuzK0lSrU5i1FVYX1XwSmO6LEj0BgwghIwzpDV30LmpKD0Fw42OIB+ETkPn45oL41rLUoh6aMg7p4\nv8MFu+Cej9rcjRGeNgNdX/wT5GHhEHQNiB41z+4WOVsNIka8xx6R72KG/Atbt3My7UPuNwKKxFGm\njFRdvA/qcwfRaexi1F0phEgaAEW/p5s0kFRDHBwGeZ80hKaMg6bkIGqK9qLTlFV2A7KjDJmIvI+z\nGTIX9X5h3KM8YVIGtE3KAdKo7gjrPRi1xfugLz2M2po7EAfKIRgMkPcZCmlUd9TuXYWIodNRvvG1\n5g0k5w6gfONriEjLhAiA5gIX5YjIOmbIFpRKJVZ98CE25W5GTdVthEZEYUrGZCxaOB9xcXEQSyR4\n4LWd0N252ZgNlx6DobYK4uAwdEl/y2YHoLGlWR4W7vRNWInIN/jlXafbg7XFv39nv4jQ5NE2W5oB\noPLoOtSf348v87ZxUY7Iz3BRz02szicWBKdamrUNWuzeuw+7/l8eF+WIqBkGZBdZ2wssaOucayDR\na7G18Ab+z2/HY+zokc3Gg7JMQeTfGJBd1HRAUc2JHGgrVS41kDi6ZRMR+S8G5BYw7gWelHof7nzx\nBgyaWtQU7rX7MU0nvjnqyCMi/8SA3ELGAUXqO5U4fGA/6h20NFtOfLN1yyYi8l/ch9wGhg8fjl07\ntjfbw6yrvgX1uf1QFx9oNrvCUUceEfkfBuQ2YixjrPrgQ3z013kQdNZvs2TEMZlEZIkBuQ0ZyxiC\nIGBr4Q0o2JFHRC5gDdkNOCaTiFqCGbIb2JqLYdmRx33HRNQUM2Q34ZhMInIVZ1kQEbkZZ1kQEXkZ\nBmQiIg/BgExE5CEYkImIPAQDMhGRh2BAJiLyEAzIREQeggGZiMhDMCATEXkIBmQiIg/BgExE5CEY\nkImIPAQDMhGRh2BAJiLyEAzIREQeggGZiMhDuDSgXiQS3QJw1X2XQ0Tkkx4UBKGzo5NcCshEROQ+\nLFkQEXkIBmQiIg/BgExE5CEYkImIPAQDMhGRh2BAJiLyEAzIREQeggGZiMhDMCATEXmI/w+d0msx\n5ueIPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19ce7b2fb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mglearn # credits to Muller and Guido (https://www.amazon.com/dp/1449369413/)\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "mglearn.plots.plot_tree_not_monotone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the training subset: 1.000\n",
      "Accuracy on the test subset: 0.937\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, stratify=cancer.target, random_state=42)\n",
    "tree = DecisionTreeClassifier(random_state=0)\n",
    "tree.fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy on the training subset: {:.3f}'.format(tree.score(X_train, y_train)))\n",
    "print('Accuracy on the test subset: {:.3f}'.format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the training subset: 0.988\n",
      "Accuracy on the test subset: 0.951\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=4, random_state=0)\n",
    "tree.fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy on the training subset: {:.3f}'.format(tree.score(X_train, y_train)))\n",
    "print('Accuracy on the test subset: {:.3f}'.format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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