Machine Learning with Scikit-Learn - The Cancer Dataset - 15 - Decision Trees 5.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+/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\n5ueIPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cc4efef358>"
      ]
     },
     "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",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "mglearn.plots.plot_tree_not_monotone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import graphviz\n",
    "from sklearn.tree import export_graphviz\n",
    "\n",
    "export_graphviz(tree, out_file='cancertree.dot', class_names=['malignant', 'benign'], feature_names=cancer.feature_names,\n",
    "               impurity=False, filled=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "![](cancertree.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances: [ 0.          0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.01019737  0.04839825  0.          0.\n",
      "  0.0024156   0.          0.          0.          0.          0.\n",
      "  0.72682851  0.0458159   0.          0.          0.0141577   0.          0.018188\n",
      "  0.1221132   0.01188548  0.        ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Feature importances: {}'.format(tree.feature_importances_))\n",
    "type(tree.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
      " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
      " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
      " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
      " 'smoothness error' 'compactness error' 'concavity error'\n",
      " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
      " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
      " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
      " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n"
     ]
    }
   ],
   "source": [
    "print(cancer.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KfxBJR8Yz0rt379p/vSRJkmSh6VIjbLqeHGuqucf1U8D95usPU2lstvFnM/vQzN7A15EH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\ngpSMe9ofwH7A2Ih4MSfsojF5vxuTeqSfERF7Sbo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cc4f098b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_features = cancer.data.shape[1]\n",
    "plt.barh(range(n_features), tree.feature_importances_, align='center')\n",
    "plt.yticks(np.arange(n_features), cancer.feature_names)\n",
    "plt.xlabel('Feature Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Advantages of Decision Trees\n",
    "\n",
    " - easy to view and understand\n",
    " - no need to pre-process, normalize, scale, and/or standardize features\n",
    " \n",
    "### Paramaters to work with\n",
    " \n",
    " - max_depth\n",
    " - min_samples_leaf, max_samples_leaf\n",
    " - max_leaf_nodes\n",
    " - etc.\n",
    " \n",
    "### Main Disadvantages\n",
    "\n",
    "- tendency to overfit\n",
    "- poor generalization \n",
    "\n",
    "####  <center> Possible work-around: ensembles of decision trees </center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}