From 993dfff0aa90d95b249890dd786b5729517471c0 Mon Sep 17 00:00:00 2001
From: Dan Mikus <daniel.j.mikus@gmail.com>
Date: Sun, 10 Dec 2017 15:09:33 -0500
Subject: [PATCH] Updated Decision_Tree_Image function by removing tree_
 attribute from tree instance

---
 ...ion, and tree-structured models 2017.ipynb | 60 ++++++-------------
 1 file changed, 18 insertions(+), 42 deletions(-)

diff --git a/Module2_Supervised/ Correlation, supervised segmentation, and tree-structured models 2017.ipynb b/Module2_Supervised/ Correlation, supervised segmentation, and tree-structured models 2017.ipynb
index 83c36cc..0cfa142 100644
--- a/Module2_Supervised/ Correlation, supervised segmentation, and tree-structured models 2017.ipynb	
+++ b/Module2_Supervised/ Correlation, supervised segmentation, and tree-structured models 2017.ipynb	
@@ -32,9 +32,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Import the libraries we will be using\n",
@@ -73,9 +71,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\n",
@@ -84,7 +80,7 @@
     "def Decision_Tree_Image(decision_tree, feature_names, name=\"temp\"):\n",
     "    \n",
     "    # Export our decision tree to graphviz format\n",
-    "    dot_file = tree.export_graphviz(decision_tree.tree_, out_file='images/' + name + '.dot', feature_names=feature_names)\n",
+    "    dot_file = tree.export_graphviz(decision_tree, out_file='images/' + name + '.dot', feature_names=feature_names)\n",
     "    \n",
     "    # Call graphviz to make an image file from our decision tree\n",
     "    os.system(\"dot -T png images/\" + name + \".dot -o images/\" + name + \".png\")\n",
@@ -156,9 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Set the randomness\n",
@@ -207,9 +201,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -276,9 +268,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -323,7 +313,6 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -392,9 +381,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "\n",
@@ -440,9 +427,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -469,7 +454,6 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {
-    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -515,9 +499,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -538,9 +520,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -578,9 +558,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "Decision_Surface(X,Y,decision_tree)"
@@ -589,9 +567,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "print ( \"Accuracy = %.3f\" % (metrics.accuracy_score(decision_tree.predict(X), Y)) )"
@@ -609,23 +585,23 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }