{"id":2891,"date":"2017-04-21T20:39:38","date_gmt":"2017-04-21T20:39:38","guid":{"rendered":"http:\/\/dataaspirant.com\/?p=2891"},"modified":"2020-08-19T03:09:55","modified_gmt":"2020-08-19T03:09:55","slug":"visualize-decision-tree-python-graphviz","status":"publish","type":"post","link":"https:\/\/dataaspirant.com\/visualize-decision-tree-python-graphviz\/","title":{"rendered":"visualize decision tree in python with graphviz"},"content":{"rendered":"<div id=\"bsf_rt_marker\"><\/div><div id=\"attachment_2892\" style=\"width: 710px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-2892\" class=\"size-full wp-image-2892 lazyload\" data-src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/visualize-Decision-Tree.jpg\" alt=\"Visualize Decision Tree\" width=\"700\" height=\"400\" data-srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/visualize-Decision-Tree.jpg 700w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/visualize-Decision-Tree-300x171.jpg 300w\" data-sizes=\"(max-width: 700px) 100vw, 700px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 700px; --smush-placeholder-aspect-ratio: 700\/400;\" \/><noscript><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-2892\" class=\"size-full wp-image-2892\" src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/visualize-Decision-Tree.jpg\" alt=\"Visualize Decision Tree\" width=\"700\" height=\"400\" srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/visualize-Decision-Tree.jpg 700w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/visualize-Decision-Tree-300x171.jpg 300w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/noscript><p id=\"caption-attachment-2892\" class=\"wp-caption-text\">Visualize Decision Tree<\/p><\/div>\n<h1>How to visualize a decision tree in Python<\/h1>\n<p style=\"text-align: justify;\">The decision tree classifier is the most popularly used <a href=\"https:\/\/dataaspirant.com\/2014\/09\/19\/supervised-and-unsupervised-learning\/\" target=\"_blank\">supervised learning<\/a> algorithm. Unlike other classification algorithms, the decision tree classifier is not a black box in the modeling phase. \u00a0What that&#8217;s means, we can visualize the trained decision tree to understand how the decision tree gonna work for the give input features.<\/p>\n<p>So in this article, you are going to learn\u00a0how to visualize the trained decision tree model in Python with Graphviz. So let&#8217;s begin with the table of contents.<\/p>\n<span class='bctt-click-to-tweet'><span class='bctt-ctt-text'><a href='https:\/\/twitter.com\/intent\/tweet?url=https%3A%2F%2Fdataaspirant.com%2F%3Fp%3D2891&#038;text=How%20to%20visualize%20decision%20tree%20in%20Python&#038;via=dataaspirant&#038;related=dataaspirant' target='_blank'rel=\"noopener noreferrer\">How to visualize decision tree in Python <\/a><\/span><a href='https:\/\/twitter.com\/intent\/tweet?url=https%3A%2F%2Fdataaspirant.com%2F%3Fp%3D2891&#038;text=How%20to%20visualize%20decision%20tree%20in%20Python&#038;via=dataaspirant&#038;related=dataaspirant' target='_blank' class='bctt-ctt-btn'rel=\"noopener noreferrer\">Click To Tweet<\/a><\/span>\n<h3>Table of contents<\/h3>\n<ul>\n<li>A basic introduction to decision tree classifier<\/li>\n<li>Fruit classification with decision tree classifier<\/li>\n<li>Why we need to visualize the trained decision tree<\/li>\n<li>Understand the visualized decision tree<\/li>\n<li>Visualize decision tree in python\n<ul>\n<li>What is Graphviz<\/li>\n<li>Visualize the decision tree online<\/li>\n<li>Visualize the decision tree as pdf<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2>Introduction to Decision tree classifier<\/h2>\n<p style=\"text-align: justify;\">The decision tree classifier is mostly used classification\u00a0algorithm because of its advantages over other <a href=\"https:\/\/dataaspirant.com\/2016\/09\/24\/classification-clustering-alogrithms\/\" target=\"_blank\">classification algorithms<\/a>. When we say the advantages it&#8217;s not about the accuracy of the trained decision tree model. It&#8217;s all about the usage and understanding of the algorithm.<\/p>\n<h4 style=\"text-align: justify;\">Decision tree advantages:<\/h4>\n<ul>\n<li>Implementation wise building decision tree algorithm is so simple.<\/li>\n<li>The trained decision tree can use for both <a href=\"https:\/\/dataaspirant.com\/2014\/09\/27\/classification-and-prediction\/\" target=\"_blank\">classification and regression problems<\/a>.<\/li>\n<li>The complexity-wise decision tree is logarithmic in the number of observations in the training dataset.<\/li>\n<li>The trained decision tree can visualize.<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">As we knew the advantages of using the decision tree over other classification algorithms. Now let&#8217;s look at the basic introduction to the decision tree.<\/p>\n<p style=\"text-align: justify;\">If you go through the article about the working of <a href=\"https:\/\/dataaspirant.com\/2017\/01\/30\/how-decision-tree-algorithm-works\/\" target=\"_blank\">decision tree classifiers in machine learning<\/a>. You could aware of the decision tree keywords like root node, leaf node, information gain, Gini index, tree pruning ..etc<\/p>\n<p style=\"text-align: justify;\">The above keywords used to give you the basic introduction to the decision tree classifier. If new to the decision tree classifier, Please spend some time on the below articles before you continue reading about how to visualize the decision tree in Python.<\/p>\n<ul>\n<li>\n<blockquote><p><a href=\"https:\/\/dataaspirant.com\/2017\/01\/30\/how-decision-tree-algorithm-works\/\" target=\"_blank\">How the decision tree classifier works in machine learning<\/a><\/p><\/blockquote>\n<\/li>\n<li>\n<blockquote><p><a href=\"https:\/\/dataaspirant.com\/2017\/02\/01\/decision-tree-algorithm-python-with-scikit-learn\/\" target=\"_blank\">Implementing decision tree classifier in Python with Scikit-Learn<\/a><\/p><\/blockquote>\n<\/li>\n<li>\n<blockquote><p><a href=\"https:\/\/dataaspirant.com\/2017\/02\/03\/decision-tree-classifier-implementation-in-r\/\" target=\"_blank\">Building decision tree classifier in R programming language<\/a><\/p><\/blockquote>\n<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">The decision tree classifier is a classification model that creates a set of rules from the training dataset. Later the created rules used to predict the target class. To get a clear picture of the rules and the need for visualizing decision, Let build a toy kind of decision tree classifier. Later use the build decision tree to understand the need to visualize the trained decision tree.<\/p>\n<h2>Fruit classification with decision tree classifier<\/h2>\n<div id=\"attachment_2896\" style=\"width: 510px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-2896\" class=\"size-full wp-image-2896 lazyload\" data-src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/fruit-classification-with-decision-tree.jpg\" alt=\"fruit classification with decision tree\" width=\"500\" height=\"300\" data-srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/fruit-classification-with-decision-tree.jpg 500w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/fruit-classification-with-decision-tree-300x180.jpg 300w\" data-sizes=\"(max-width: 500px) 100vw, 500px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 500px; --smush-placeholder-aspect-ratio: 500\/300;\" \/><noscript><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-2896\" class=\"size-full wp-image-2896\" src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/fruit-classification-with-decision-tree.jpg\" alt=\"fruit classification with decision tree\" width=\"500\" height=\"300\" srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/fruit-classification-with-decision-tree.jpg 500w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/fruit-classification-with-decision-tree-300x180.jpg 300w\" sizes=\"(max-width: 500px) 100vw, 500px\" \/><\/noscript><p id=\"caption-attachment-2896\" class=\"wp-caption-text\">Fruit classification with decision tree<\/p><\/div>\n<p style=\"text-align: justify;\">The decision tree classifier will train using the apple and orange features, later the trained classifier can be used to predict the fruit label given the fruit features.<\/p>\n<p style=\"text-align: justify;\">The fruit features is a dummy dataset. Below are the dataset features and the targets.<\/p>\n<table style=\"height: 356px; width: 592px;\">\n<tbody>\n<tr>\n<td style=\"width: 190px; text-align: center;\"><strong>Weight (grams)<\/strong><\/td>\n<td style=\"width: 223.671875px; text-align: center;\"><strong>Smooth (Range of 1 to 10)<\/strong><\/td>\n<td style=\"width: 157.328125px; text-align: center;\"><strong>Fruit<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"width: 190px; text-align: center;\">170<\/td>\n<td style=\"width: 223.671875px; text-align: center;\">9<\/td>\n<td style=\"width: 157.328125px; text-align: center;\">1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 190px; text-align: center;\">175<\/td>\n<td style=\"width: 223.671875px; text-align: center;\">10<\/td>\n<td style=\"width: 157.328125px; text-align: center;\">1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 190px; text-align: center;\">180<\/td>\n<td style=\"width: 223.671875px; text-align: center;\">8<\/td>\n<td style=\"width: 157.328125px; text-align: center;\">1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 190px; text-align: center;\">178<\/td>\n<td style=\"width: 223.671875px; text-align: center;\">8<\/td>\n<td style=\"width: 157.328125px; text-align: center;\">1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 190px; text-align: center;\">182<\/td>\n<td style=\"width: 223.671875px; text-align: center;\">7<\/td>\n<td style=\"width: 157.328125px; text-align: center;\">1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 190px; text-align: center;\">130<\/td>\n<td style=\"width: 223.671875px; text-align: center;\">3<\/td>\n<td style=\"width: 157.328125px; text-align: center;\">0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 190px; text-align: center;\">120<\/td>\n<td style=\"width: 223.671875px; text-align: center;\">4<\/td>\n<td style=\"width: 157.328125px; text-align: center;\">0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 190px; text-align: center;\">130<\/td>\n<td style=\"width: 223.671875px; text-align: center;\">2<\/td>\n<td style=\"width: 157.328125px; text-align: center;\">0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 190px; text-align: center;\">138<\/td>\n<td style=\"width: 223.671875px; text-align: center;\">5<\/td>\n<td style=\"width: 157.328125px; text-align: center;\">0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 190px; text-align: center;\">145<\/td>\n<td style=\"width: 223.671875px; text-align: center;\">6<\/td>\n<td style=\"width: 157.328125px; text-align: center;\">0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify;\">\u00a0The dummy dataset having <strong>two features and targets<\/strong>.<\/p>\n<ul>\n<li style=\"text-align: justify;\"><strong>Weight:<\/strong> Is the weight of the fruit in grams<\/li>\n<li style=\"text-align: justify;\"><strong>Smooth:<\/strong> Is the smoothness of the fruit in the range of <strong>1 to 10<\/strong><\/li>\n<li style=\"text-align: justify;\"><strong>Fruit:<\/strong> Is the target <strong>1<\/strong> means for <strong>apple<\/strong> and <strong>0<\/strong> means for <strong>orange<\/strong>.<\/li>\n<\/ul>\n<p>Let&#8217;s follow the below <strong>workflow for modeling<\/strong> the fruit classifier.<\/p>\n<ul>\n<li>Loading the required Python machine learning packages<\/li>\n<li>Create and load the data in Pandas dataframe<\/li>\n<li>Building the fruit classifier with decision tree algorithm<\/li>\n<li>Predicting the fruit type from the trained classifier<\/li>\n<\/ul>\n<h3>Loading the required Python machine learning packages<\/h3>\n<pre class=\"lang:python decode:true \" title=\"Required Python machine learing packages\"># Required Python Packages\nimport pandas as pd\nimport numpy as np\nfrom sklearn import tree<\/pre>\n<p style=\"text-align: justify;\">The required python machine learning packages for building the fruit classifier are <strong>Pandas, Numpy<\/strong>, and <strong>Scikit-learn<\/strong><\/p>\n<ul>\n<li><strong>Pandas:<\/strong> For loading the dataset into dataframe, Later the loaded dataframe passed an input parameter for modeling the classifier.<\/li>\n<li><strong>Numpy:<\/strong> For creating the dataset and for performing the numerical calculation.<\/li>\n<li><strong>Sklearn:<\/strong> For training the decision tree classifier on the loaded dataset.<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">Now let&#8217;s create the dummy data set and load into the pandas dataframe<\/p>\n<h3>Create and load the data in Pandas dataframe<\/h3>\n<pre class=\"lang:python decode:true \" title=\"Creating the dataset\"># creating dataset for modeling Apple \/ Orange classification\nfruit_data_set = pd.DataFrame()\nfruit_data_set[\"fruit\"] = np.array([1, 1, 1, 1, 1,      # 1 for apple\n                                    0, 0, 0, 0, 0])     # 0 for orange\nfruit_data_set[\"weight\"] = np.array([170, 175, 180, 178, 182,\n                                     130, 120, 130, 138, 145])\nfruit_data_set[\"smooth\"] = np.array([9, 10, 8, 8, 7,\n                                     3, 4, 2, 5, 6])<\/pre>\n<ul>\n<li>The empty pandas dataframe created for creating the fruit data set.<\/li>\n<li>Using the NumPy created arrays for <strong>target, weight, smooth<\/strong>.\n<ul>\n<li>The <strong>target<\/strong>\u00a0having two unique values 1 for <strong>apple<\/strong> and 0 for <strong>orange<\/strong>.<\/li>\n<li><strong>Weight<\/strong> is the weight of the fruit in grams.<\/li>\n<li><strong>Smooth<\/strong> is the smoothness of the fruit in the range of 1 to 10.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">Now, let&#8217;s use the loaded dummy dataset to train a decision tree classifier.<\/p>\n<h3>Building the fruit classifier with decision tree algorithm<\/h3>\n<pre class=\"lang:python decode:true \" title=\"Building Decision Tree Classifier\">fruit_classifier = tree.DecisionTreeClassifier()\nfruit_classifier.fit(fruit_data_set[[\"weight\", \"smooth\"]], fruit_data_set[\"fruit\"])\n\nprint \"&gt;&gt;&gt;&gt;&gt; Trained fruit_classifier &lt;&lt;&lt;&lt;&lt;\"\nprint fruit_classifier<\/pre>\n<ul>\n<li>Creating the decision tree classifier instance from the imported sci-kit learn tree class.<\/li>\n<li>Using the loaded fruit data set features and the target to train the decision tree model.<\/li>\n<li>Print the trained fruit classifier.<\/li>\n<\/ul>\n<h4>Script Output:<\/h4>\n<pre class=\"lang:python decode:true \" title=\"Script Output\">&gt;&gt;&gt;&gt;&gt; Trained fruit_classifier &lt;&lt;&lt;&lt;&lt;\nDecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n            max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n            min_samples_split=2, min_weight_fraction_leaf=0.0,\n            presort=False, random_state=None, splitter='best')<\/pre>\n<p>Now let&#8217;s use the fruit classifier to predict the fruit type by giving the fruit features.<\/p>\n<h3>Predicting the fruit type from the trained classifier<\/h3>\n<pre class=\"lang:python decode:true \" title=\"Predicting fruit type\"># fruit data set 1st observation\ntest_features_1 = [[fruit_data_set[\"weight\"][0], fruit_data_set[\"smooth\"][0]]]\ntest_features_1_fruit = fruit_classifier.predict(test_features_1)\nprint \"Actual fruit type: {act_fruit} , Fruit classifier predicted: {predicted_fruit}\".format(\n    act_fruit=fruit_data_set[\"fruit\"][0], predicted_fruit=test_features_1_fruit)\n\n# fruit data set 3rd observation\ntest_features_3 = [[fruit_data_set[\"weight\"][2], fruit_data_set[\"smooth\"][2]]]\ntest_features_3_fruit = fruit_classifier.predict(test_features_3)\nprint \"Actual fruit type: {act_fruit} , Fruit classifier predicted: {predicted_fruit}\".format(\n    act_fruit=fruit_data_set[\"fruit\"][2], predicted_fruit=test_features_3_fruit)\n\n# fruit data set 8th observation\ntest_features_8 = [[fruit_data_set[\"weight\"][7], fruit_data_set[\"smooth\"][7]]]\ntest_features_8_fruit = fruit_classifier.predict(test_features_8)\nprint \"Actual fruit type: {act_fruit} , Fruit classifier predicted: {predicted_fruit}\".format(\n    act_fruit=fruit_data_set[\"fruit\"][7], predicted_fruit=test_features_8_fruit)<\/pre>\n<p style=\"text-align: justify;\">Created 3 test data sets and using the trained fruit classifier to predict the fruit type and comparing with the real fruit type.<\/p>\n<p><strong>Script Output:\u00a0<\/strong><\/p>\n<pre class=\"lang:python decode:true \" title=\"Script Output\">Actual fruit type: 1 , Fruit classifier predicted: [1]\nActual fruit type: 1 , Fruit classifier predicted: [1]\nActual fruit type: 0 , Fruit classifier predicted: [0]<\/pre>\n<p style=\"text-align: justify;\">The trained fruit classifier using the decision tree algorithm is accurately predicting the target fruit type for the given fruit features. You only know that the decision tree is predicting the target fruit type for the given fruit features in a black-box way and you don&#8217;t know what&#8217;s happing inside the black box.<\/p>\n<p style=\"text-align: justify;\">To understand what happing inside the trained decision tree model and how it&#8217;s predicting the target class for the given features we need a visual representation of the trained decision tree classifier.<\/p>\n<h2>Why we need to visualize the trained decision tree<\/h2>\n<p style=\"text-align: justify;\">To answer the question of why we need to visualize the trained decision tree, I am going to show you the visual representation of the above fruit classifier.<\/p>\n<p style=\"text-align: justify;\">Before I show you the visual representation of the trained decision tree classifier, have a look at the 3 test observations we considered for predicting the target fruit type from the fruit classifier.<\/p>\n<table style=\"height: 150px; width: 612px;\">\n<tbody>\n<tr>\n<td style=\"width: 146px; text-align: center;\"><strong>Weight<\/strong><\/td>\n<td style=\"width: 146px; text-align: center;\"><strong>Smoot<\/strong><\/td>\n<td style=\"width: 125.796875px; text-align: center;\"><strong>Fruit<\/strong><\/td>\n<td style=\"width: 166.21875px; text-align: center;\"><strong>Classifier Predicted<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"width: 146px; text-align: center;\">170<\/td>\n<td style=\"width: 146px; text-align: center;\">9<\/td>\n<td style=\"width: 125.796875px; text-align: center;\">1<\/td>\n<td style=\"width: 166.21875px; text-align: center;\">1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 146px; text-align: center;\">180<\/td>\n<td style=\"width: 146px; text-align: center;\">10<\/td>\n<td style=\"width: 125.796875px; text-align: center;\">1<\/td>\n<td style=\"width: 166.21875px; text-align: center;\">1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 146px; text-align: center;\">130<\/td>\n<td style=\"width: 146px; text-align: center;\">2<\/td>\n<td style=\"width: 125.796875px; text-align: center;\">0<\/td>\n<td style=\"width: 166.21875px; text-align: center;\">0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify;\">The below image is the visual representation of the trained fruit classifier.<\/p>\n<div id=\"attachment_2943\" style=\"width: 298px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-2943\" class=\"wp-image-2943 size-full lazyload\" title=\"Decision tree visualization in Python with graphviz\" data-src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/decision_tree_visualization.png\" alt=\"Decision tree visualization in Python with graphviz\" width=\"288\" height=\"244\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 288px; --smush-placeholder-aspect-ratio: 288\/244;\" \/><noscript><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-2943\" class=\"wp-image-2943 size-full\" title=\"Decision tree visualization in Python with graphviz\" src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/decision_tree_visualization.png\" alt=\"Decision tree visualization in Python with graphviz\" width=\"288\" height=\"244\" \/><\/noscript><p id=\"caption-attachment-2943\" class=\"wp-caption-text\">Decision tree visualization in Python with Graphviz<\/p><\/div>\n<p>In the next coming section, you are going to learn how to visualize the decision tree in Python with Graphviz.<\/p>\n<h3><\/h3>\n<h3>Decision tree visualization explanation<\/h3>\n<p style=\"text-align: justify;\">The trained decision tree having the root node as fruit weight <strong>(x[0])<\/strong>. These conditions are populated with the provided train dataset.<\/p>\n<p style=\"text-align: justify;\">If the weight is less than are equal to <strong>157.5<\/strong> go to the <strong>left\u00a0node<\/strong>. If the weight is greater than 157.5 go to the <strong>right node<\/strong>. In fact, the right and left nodes are the leaf nodes as the decision tree considered only one feature (weight) is enough for classifying the fruit type.<\/p>\n<p style=\"text-align: justify;\">The below pseudo-code can represent the above graph into simple <strong>if-else<\/strong> conditions.<\/p>\n<pre class=\"lang:python decode:true\" title=\"Decision tree with if else\">def predict(weight, smoot):\n    if weight &lt;= 157.5:\n        return 0\n    else:\n        return 1<\/pre>\n<p style=\"text-align: justify;\">Now if you pass the same 3 test observations we used to predict the fruit type from the trained fruit classifier you get to know why and how the trained decision tree\u00a0predicting the fruit type for the given fruit features.<\/p>\n<h2 style=\"text-align: justify;\">Visualize decision tree in python with Graphviz<\/h2>\n<p style=\"text-align: justify;\">I hope you the advantages of visualizing the decision tree. Now let&#8217;s move the key section of this article, Which is visualizing the decision tree in python with Graphviz.<\/p>\n<p style=\"text-align: justify;\">You can visualize the trained decision tree in python with the help of Graphviz. Below are two ways to visualize the decision tree model.<\/p>\n<ul>\n<li>Visualize the decision tree online<\/li>\n<li>Visualize the decision tree as pdf<\/li>\n<\/ul>\n<p style=\"text-align: justify;\">In both these cases, you need first convert the trained decision tree classifier into graphviz object. Later we use the converted graphviz object for visualization. So It&#8217;s better to know about the python graphviz before looking into the visualization part.<\/p>\n<h3>What is Graphviz<\/h3>\n<p style=\"text-align: justify;\">Graphviz is one of the visualization libraries. The greatness of graphviz is that it&#8217;s an open-source visualization library. Graphviz widely used in networking application were to visualize the connection between the switches hub and different networks. When it comes to machine learning used for decision tree and neural networks.<\/p>\n<p style=\"text-align: justify;\">Now let&#8217;s look at how to visualize the decision tree with graphviz.<\/p>\n<h3>Visualize the decision tree online<\/h3>\n<p style=\"text-align: justify;\">To visualize the decision tree online first you need to convert the trained decision tree, in our case the fruit classifier into a file (txt is better). Later you can use the contents of the converted file to visualize online.<\/p>\n<p>The below can will convert the trained fruit classifier into graphviz object and saves it into the txt file.<\/p>\n<pre class=\"lang:python decode:true \" title=\"Visualize the decision tree online\">with open(\"fruit_classifier.txt\", \"w\") as f:\n    f = tree.export_graphviz(fruit_classifier, out_file=f)<\/pre>\n<p>If you are having the proper python machine learning packages set up in your system. After running the above code fruit_classifier.txt will be saved on your local system.<\/p>\n<p>To visualize the decision tree, you just need to open the fruit_classifier.txt file and copy the contents of the file to paste in the graphviz web portal. Below is the address for the web portal.<\/p>\n<blockquote><p>graphviz web portal address:\u00a0<a href=\"http:\/\/webgraphviz.com\" target=\"_blank\">http:\/\/webgraphviz.com<\/a><\/p><\/blockquote>\n<p>You can see the below graphviz web portal.<\/p>\n<div id=\"attachment_2969\" style=\"width: 651px\" class=\"wp-caption aligncenter\"><a href=\"http:\/\/webgraphviz.com\" target=\"_blank\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-2969\" class=\"size-full wp-image-2969 lazyload\" data-src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/webgraphviz.png\" alt=\"graphviz web portal\" width=\"641\" height=\"510\" data-srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/webgraphviz.png 641w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/webgraphviz-300x239.png 300w\" data-sizes=\"(max-width: 641px) 100vw, 641px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 641px; --smush-placeholder-aspect-ratio: 641\/510;\" \/><noscript><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-2969\" class=\"size-full wp-image-2969\" src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/webgraphviz.png\" alt=\"graphviz web portal\" width=\"641\" height=\"510\" srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/webgraphviz.png 641w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/webgraphviz-300x239.png 300w\" sizes=\"(max-width: 641px) 100vw, 641px\" \/><\/noscript><\/a><p id=\"caption-attachment-2969\" class=\"wp-caption-text\">graphviz web portal<\/p><\/div>\n<p>Once the graphviz web portal opened. Remove the already presented text in the text box and paste the text in the created txt file and click on the<strong> generate-graph<\/strong>\u00a0button.<\/p>\n<p>For the modeled fruit classifier, we will get the below decision tree visualization.<\/p>\n<div id=\"attachment_2971\" style=\"width: 646px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-2971\" class=\"size-full wp-image-2971 lazyload\" data-src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/decision-tree-visualization-with-graphviz.png\" alt=\"decision tree visualization with graphviz\" width=\"636\" height=\"738\" data-srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/decision-tree-visualization-with-graphviz.png 636w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/decision-tree-visualization-with-graphviz-259x300.png 259w\" data-sizes=\"(max-width: 636px) 100vw, 636px\" src=\"data:image\/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\" style=\"--smush-placeholder-width: 636px; --smush-placeholder-aspect-ratio: 636\/738;\" \/><noscript><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-2971\" class=\"size-full wp-image-2971\" src=\"http:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/decision-tree-visualization-with-graphviz.png\" alt=\"decision tree visualization with graphviz\" width=\"636\" height=\"738\" srcset=\"https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/decision-tree-visualization-with-graphviz.png 636w, https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/decision-tree-visualization-with-graphviz-259x300.png 259w\" sizes=\"(max-width: 636px) 100vw, 636px\" \/><\/noscript><p id=\"caption-attachment-2971\" class=\"wp-caption-text\">decision tree visualization with graphviz<\/p><\/div>\n<p>Now let&#8217;s look at how to visualize the trained decision tree as pdf<\/p>\n<h3>Visualize the decision tree as pdf<\/h3>\n<p style=\"text-align: justify;\">The visualization of the trained decision tree as pdf will be the same as the above. the only change is instead on copy and pastes the contents of the converted txt file to the web portal, you will be converting it into a pdf file.<\/p>\n<pre class=\"lang:python decode:true\" title=\"Converting into pdf file\"># converting into the pdf file\nwith open(\"fruit_classifier.dot\", \"w\") as f:\n    f = tree.export_graphviz(fruit_classifier, out_file=f)<\/pre>\n<p>The above code will convert the trained decision tree classifier into graphviz object and then store the contents into the fruit_classifier.dot file. Next to convert the dot file into pdf file you can use the below command.<\/p>\n<pre class=\"lang:default decode:true\" title=\"odt to pdf\">dot -Tpdf fruit_classifier.dot -o fruit_classifier.pdf<\/pre>\n<p>To preview the created pdf file you can use the below command.<\/p>\n<pre class=\"lang:default decode:true\" title=\"open pdf\">open -a preview fruit_classifier.pdf<\/pre>\n<p>You can get the complete code of this article on our <a href=\"https:\/\/github.com\/saimadhu-polamuri\/DataAspirant_codes\/tree\/master\/decision_tree_visualization\/scripts\" target=\"_blank\" rel=\"noopener noreferrer\">Github account<\/a>.<\/p>\n<h4>Follow us:<\/h4>\n<h5><strong><span style=\"color: #0000ff;\"><a style=\"color: #0000ff;\" title=\"dataaspirant\" href=\"https:\/\/www.facebook.com\/dataaspirant\" target=\"_blank\">FACEBOOK<\/a><\/span>|\u00a0<span style=\"color: #ff0000;\"><a style=\"color: #ff0000;\" title=\"dataaspirant.quora.com\" href=\"http:\/\/dataaspirant.quora.com\/\" target=\"_blank\">QUORA<\/a><\/span>\u00a0|<span style=\"color: #00ccff;\"><a style=\"color: #00ccff;\" title=\"twitter.com\/dataaspirant\" href=\"https:\/\/twitter.com\/dataaspirant\" target=\"_blank\">TWITTER<\/a><\/span>|\u00a0<span style=\"color: #808000;\"><a style=\"color: #808000;\" href=\"https:\/\/plus.google.com\/104087213513583475222\" target=\"_blank\">GOOGLE+<\/a><\/span>\u00a0|\u00a0<span style=\"color: #000080;\"><a style=\"color: #000080;\" href=\"https:\/\/www.linkedin.com\/company\/dataaspirant\" target=\"_blank\">LINKEDIN<\/a><\/span>|\u00a0<span style=\"color: #ff9900;\"><a style=\"color: #ff9900;\" title=\"reddit dataaspirant\" href=\"http:\/\/www.reddit.com\/user\/dataaspirant\/\" target=\"_blank\">REDDIT<\/a>\u00a0<\/span>|\u00a0<span style=\"color: #ff0000;\"><a style=\"color: #ff0000;\" title=\"flipboard dataaspirant\" href=\"https:\/\/flipboard.com\/@dataaspirant\/\" target=\"_blank\">FLIPBOARD<\/a><\/span>\u00a0|\u00a0<span style=\"color: #00ff00;\"><a style=\"color: #00ff00;\" title=\"medium dataaspirant\" href=\"https:\/\/medium.com\/@dataaspirant\" target=\"_blank\">MEDIUM<\/a>\u00a0|\u00a0<a href=\"https:\/\/github.com\/saimadhu-polamuri\/DataAspirant_codes\" target=\"_blank\"><span style=\"color: #008080;\">GITHUB<\/span><\/a><\/span><\/strong><\/h5>\n<p style=\"text-align: justify;\">I hope you like this post. If you have any questions, then feel free to comment below.\u00a0 If you want me to write on one particular topic, then do tell it to me in the comments below.<\/p>\n<h4 style=\"text-align: justify;\">Related Courses:<\/h4>\n<ul>\n<li><a href=\"http:\/\/click.linksynergy.com\/link?id=d1QCig2q3qI&amp;offerid=323058.950390&amp;type=2&amp;murl=https%3A%2F%2Fwww.udemy.com%2Fmachinelearning%2F\" target=\"_blank\">Machine Learning A-Z: Hands-On Python &amp; R In Data Science<\/a><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/ad.linksynergy.com\/fs-bin\/show?id=d1QCig2q3qI&amp;bids=323058.950390&amp;type=2&amp;subid=0\" width=\"1\" height=\"1\" border=\"0\" \/><\/li>\n<li><a href=\"http:\/\/click.linksynergy.com\/link?id=d1QCig2q3qI&amp;offerid=479491.1560515878&amp;type=2&amp;murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml-classification\" target=\"_blank\">Machine Learning: Classification<\/a><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/ad.linksynergy.com\/fs-bin\/show?id=d1QCig2q3qI&amp;bids=479491.1560515878&amp;type=2&amp;subid=0\" width=\"1\" height=\"1\" border=\"0\" \/><\/li>\n<li><a href=\"http:\/\/click.linksynergy.com\/link?id=d1QCig2q3qI&amp;offerid=479491.1560519184&amp;type=2&amp;murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fpractical-machine-learning\" target=\"_blank\">Machine Learning with practical applications<\/a><img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/ad.linksynergy.com\/fs-bin\/show?id=d1QCig2q3qI&amp;bids=479491.1560519184&amp;type=2&amp;subid=0\" width=\"1\" height=\"1\" border=\"0\" \/><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>How to visualize a decision tree in Python The decision tree classifier is the most popularly used supervised learning algorithm. Unlike other classification algorithms, the decision tree classifier is not a black box in the modeling phase. \u00a0What that&#8217;s means, we can visualize the trained decision tree to understand how the decision tree gonna work<br \/><a class=\"moretag\" href=\"https:\/\/dataaspirant.com\/visualize-decision-tree-python-graphviz\/\">+ Read More<\/a><\/p>\n","protected":false},"author":2,"featured_media":2893,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_jetpack_memberships_contains_paid_content":false},"categories":[6,8],"tags":[39,567],"aioseo_notices":[],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"https:\/\/dataaspirant.com\/wp-content\/uploads\/2017\/04\/visualize-Decision-Tree-1.jpg","_links":{"self":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/posts\/2891"}],"collection":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/comments?post=2891"}],"version-history":[{"count":0,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/posts\/2891\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/media\/2893"}],"wp:attachment":[{"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/media?parent=2891"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/categories?post=2891"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dataaspirant.com\/wp-json\/wp\/v2\/tags?post=2891"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}