{"id":6514,"date":"2022-04-24T16:07:26","date_gmt":"2022-04-24T21:07:26","guid":{"rendered":"https:\/\/www.sharpsightlabs.com\/?p=6514"},"modified":"2024-02-06T15:31:36","modified_gmt":"2024-02-06T21:31:36","slug":"sklearn-fit","status":"publish","type":"post","link":"https:\/\/www.sharpsightlabs.com\/blog\/sklearn-fit\/","title":{"rendered":"A Quick Introduction to the Sklearn Fit Method"},"content":{"rendered":"<p>In this tutorial, I&#8217;ll show you how to use the Sklearn Fit method to &#8220;fit&#8221; a machine learning model in Python.<\/p>\n<p>So I&#8217;ll quickly review what the method does, I&#8217;ll explain the syntax, and I&#8217;ll show you a step-by-step example of how to use the technique.<\/p>\n<p>If you need something specific, just click on any of the following links.  The link will take you to the appropriate section in the tutorial.<\/p>\n<p><strong>Table of Contents:<\/strong><\/p>\n<ul>\n<li><a href=\"#intro-sklearn-fit\">Introduction<\/a><\/li>\n<li><a href=\"#sklearn-fit-syntax\">Syntax<\/a><\/li>\n<li><a href=\"#sklearn-fit-examples\">Examples<\/a><\/li>\n<\/ul>\n<p><a id = \"intro-sklearn-fit\"><\/a><\/p>\n<h2>A Quick Introduction to Model Fitting with Sklearn Fit<\/h2>\n<p>To understand what the sklearn fit function does, you need to know a little bit about the <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/introduction-to-machine-learning\/\" target=\"_blank\" rel=\"noopener\">machine learning process<\/a>.<\/p>\n<p>Typically, when we build a machine learning model, we have a machine learning algorithm and a training data set.<\/p>\n<p>Remember that a machine learning algorithm is type of algorithm that <em>learns<\/em> as we expose it to data.  To paraphrase Tom Mitchel: a machine learning algorithm is an algorithm that improves performance on a task as it is exposed to data.<\/p>\n<p>So in order for a machine learning algorithm to <em>learn<\/em>, it must be exposed to some data.  <\/p>\n<h3>We need to &#8216;train&#8217; a machine learning algorithm with data<\/h3>\n<p>That&#8217;s where we use the training data.  <\/p>\n<p>The training dataset is an input that we use to enable the machine learning algorithm to &#8220;learn&#8221;, so it can improve its performance on the task.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/machine-learning-training_simple-visual-example.png\" alt=\"An image that shows how we &quot;train&quot; a machine learning model by inputing training data into a machine learning algorithm.\" width=\"700\" class=\"aligncenter size-full wp-image-6519\" srcset=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/machine-learning-training_simple-visual-example.png 1400w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/machine-learning-training_simple-visual-example-600x322.png 600w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/machine-learning-training_simple-visual-example-1024x550.png 1024w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/machine-learning-training_simple-visual-example-768x413.png 768w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/machine-learning-training_simple-visual-example-1080x580.png 1080w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/machine-learning-training_simple-visual-example-610x328.png 610w\" sizes=\"(max-width: 1400px) 100vw, 1400px\" \/><\/p>\n<p>So we have our training data, we feed it into the machine learning algorithm, and the algorithm &#8220;learns&#8221; how to improve its performance on the basis of that training data.<\/p>\n<p>Later, once the model is &#8220;trained&#8221; then we can use it to do things, like make predictions.<\/p>\n<h3>The Sklearn Fit Method &#8216;Trains&#8217; the Model<\/h3>\n<p>So now that we&#8217;ve reviewed the machine learning process at a high level, let&#8217;s bring this back to scikit learn.<\/p>\n<p>Scikit learn is a machine learning toolkit for <a href=\"https:\/\/www.python.org\" target=\"_blank\" rel=\"noopener\">Python<\/a>.  As such, it has tools for performing steps of the machine learning process, like training a model.<\/p>\n<p>The scikit learn &#8216;fit&#8217; method is one of those tools. The &#8216;fit&#8217; method trains the algorithm on the training data, after the model is initialized.  That&#8217;s really all it does.<\/p>\n<p>So the sklearn fit method uses the training data as an input to train the machine learning model.<\/p>\n<p>Then once it&#8217;s trained, we can use other scikit learn methods \u2013 like predict and score \u2013 to continue with the machine learning process.<\/p>\n<!-- This site is converting visitors into subscribers and customers with OptinMonster - https:\/\/optinmonster.com :: Campaign Title: ML: get AI playbook (inline) -->\n<div id=\"om-v51bezokmgzzx7iskuel-holder\"><\/div>\n<script>(function(d,u,ac){var s=d.createElement('script');s.type='text\/javascript';s.src='https:\/\/a.omappapi.com\/app\/js\/api.min.js';s.async=true;s.dataset.user=u;s.dataset.campaign=ac;d.getElementsByTagName('head')[0].appendChild(s);})(document,9896,'v51bezokmgzzx7iskuel');<\/script>\n<!-- \/ OptinMonster -->\n<p><a id = \"sklearn-fit-syntax\"><\/a><\/p>\n<h2>The Syntax of the Sklearn Fit Method<\/h2>\n<p>Now that we&#8217;ve reviewed what the sklearn fit method does, let&#8217;s look at the syntax.<\/p>\n<p>Keep in mind that the syntax explanation here assumes that you&#8217;ve imported scikit-learn and you already have a model initialized, such as <code>LinearRegression<\/code>, <code>RandomForestRegressor<\/code>, etc.<\/p>\n<h3>&#8216;Fit&#8217; syntax<\/h3>\n<p>Ok.  Let&#8217;s look at the syntax.<\/p>\n<p>When we call the <code>fit<\/code> method, we need to call it from an existing instance of a machine learning model (for example, <code>LinearRegression<\/code>, <code>LogisticRegression<\/code>, <code>DecisionTreeRegressor<\/code>, <code>SVM<\/code>).<\/p>\n<p>Once you&#8217;ve initialized an instance of a model, then you can call the method.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/sklearn-fit_syntax-explanation.png\" alt=\"An image that explains the syntax for how to train a machine learning model in Python the sklearn fit method.\" width=\"700\" class=\"aligncenter size-full wp-image-6520\" srcset=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/sklearn-fit_syntax-explanation.png 1400w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/sklearn-fit_syntax-explanation-600x310.png 600w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/sklearn-fit_syntax-explanation-1024x530.png 1024w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/sklearn-fit_syntax-explanation-768x397.png 768w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/sklearn-fit_syntax-explanation-1080x559.png 1080w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/sklearn-fit_syntax-explanation-610x315.png 610w\" sizes=\"(max-width: 1400px) 100vw, 1400px\" \/><\/p>\n<p>Then, inside the parenthesis, you provide the features and the target vector (or label vector) of the training dataset.  These datasets are sometimes called <code>X_train<\/code> and <code>y_train<\/code>.<\/p>\n<p>So for example, if you&#8217;re doing linear regression with an instance of the <code>LinearRegression<\/code> model called <code>my_linear_regressor<\/code>, you might have the code:<\/p>\n<pre>\r\nmy_linear_regressor.fit(X_train, y_train)\r\n<\/pre>\n<p>For the most part, that&#8217;s all there is to it.<\/p>\n<h4>The format of the input data<\/h4>\n<p>The X-input to the <code>fit()<\/code> method, <code>X_train<\/code>, needs to be in a 2-dimensional format, such as a 2-dimensional numpy array.<\/p>\n<p>If <code>X_train<\/code> is not in a 2D format, you might get an error.  In that case, you&#8217;ll need to reshape the <code>X_test<\/code> data to 2 dimensions.  <\/p>\n<p>(I&#8217;ll show you this in the upcoming examples section.)<\/p>\n<h4>Calling the sklearn fit method more than once<\/h4>\n<p>One last note.<\/p>\n<p>If you call the sklearn fit method more than once, then the second time you call the fit method will overwrite anything that was learned the first time you called the method.<\/p>\n<p>Sometimes, you will intentionally want to do this, but be careful.  Training a model can take a lot of time and computer processing.  Calling the fit method multiple times may be expensive in terms of time and resources.  And at the very least, it will remove anything learned by the algorithm in the past.<\/p>\n<p><a id = \"sklearn-fit-examples\"><\/a><\/p>\n<h2>Example: How to Use Sklearn Fit<\/h2>\n<p>Now that we&#8217;ve looked at the syntax, let&#8217;s look at an example of how to use sklearn fit.<\/p>\n<p>Here, I&#8217;ll show you an example of how to use the sklearn fit method to train a model.<\/p>\n<p>There are several things you need to do in the example, including running some setup code, and then fitting the model.<\/p>\n<p><strong>Steps:<\/strong><\/p>\n<ul>\n<li><a href=\"#run-setup\">Run setup code<\/a><\/li>\n<li><a href=\"#fit-model\">Fit the model<\/a><\/li>\n<li><a href=\"#predict\">Predict new values<\/a><\/li>\n<\/ul>\n<p><a id = \"run-setup\"><\/a><\/p>\n<h3>Run Setup Code<\/h3>\n<p>Before you fit the model, you&#8217;ll need to do a few things.  <\/p>\n<p>We need to:<\/p>\n<ul>\n<li>import scikit-learn and other packages<\/li>\n<li>create some training data<\/li>\n<li>initialize a model<\/li>\n<\/ul>\n<p>Let&#8217;s quickly do each of those.<\/p>\n<h4>Import Scikit Learn and other packages<\/h4>\n<p>First, let&#8217;s import the packages that we&#8217;ll use<\/p>\n<p>We&#8217;re going to import scikit learn.  <\/p>\n<p>And we&#8217;ll also import Numpy and Seaborn.  We&#8217;ll <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/numpy-array-python\/\" target=\"_blank\" rel=\"noopener\">use Numpy<\/a> to create some dummy training data, and we&#8217;ll <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/best-python-package-for-data-visualization\/\" target=\"_blank\" rel=\"noopener\">use Seaborn<\/a> to plot the data.<\/p>\n<p>You can import these packages with the following code:<\/p>\n<pre>\r\nimport sklearn \r\nimport numpy as np\r\nimport seaborn as sns\r\n<\/pre>\n<h4>Create Training Data<\/h4>\n<p>Next, we&#8217;ll create some training data.<\/p>\n<p>Specifically, we&#8217;re going to create some data that&#8217;s roughly linear, with a little noise built in.<\/p>\n<p>To do this, we&#8217;ll:<\/p>\n<ul>\n<li>create 51 <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/numpy-linspace\/\" target=\"_blank\" rel=\"noopener\">evenly spaced numbers<\/a> for the x-axis variable<\/li>\n<li>create a y-axis variable that&#8217;s linearly related to the x-axis variable, with some <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/numpy-random-normal\/\" target=\"_blank\" rel=\"noopener\">normally distributed noise<\/a><\/li>\n<\/ul>\n<p>So here, we&#8217;ll use Numpy linspace and Numpy random normal to create our variables <code>x_var<\/code> and <code>y_var<\/code>.<\/p>\n<pre>\r\nobservation_count = 51\r\nx_var = np.linspace(start = 0, stop = 10, num = observation_count)\r\n\r\nnp.random.seed(22)\r\ny_var = x_var + np.random.normal(size = observation_count, loc = 1, scale = 2)\r\n<\/pre>\n<p>Notice that we&#8217;re also using Numpy random seed, to <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/numpy-random-seed\/\" target=\"_blank\" rel=\"noopener\">set the seed<\/a> for Numpy&#8217;s pseudo-random number generator, which is used by np.random.normal.<\/p>\n<p>Let&#8217;s also plot the data with Seaborn:<\/p>\n<pre>\r\nsns.scatterplot(x = x_var, y = y_var)\r\n<\/pre>\n<p>OUT:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/linearly-distributed-data_seaborn-scatterplot.png\" alt=\"An scatterplot of linearly distributed data.  We will be able to use scikit learn to train a model on this data using sklearn fit.\" width=\"545\" class=\"aligncenter size-full wp-image-6526\" srcset=\"https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/linearly-distributed-data_seaborn-scatterplot.png 1090w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/linearly-distributed-data_seaborn-scatterplot-600x403.png 600w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/linearly-distributed-data_seaborn-scatterplot-1024x688.png 1024w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/linearly-distributed-data_seaborn-scatterplot-768x516.png 768w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/linearly-distributed-data_seaborn-scatterplot-1080x725.png 1080w, https:\/\/www.sharpsightlabs.com\/wp-content\/uploads\/2022\/04\/linearly-distributed-data_seaborn-scatterplot-610x410.png 610w\" sizes=\"(max-width: 1090px) 100vw, 1090px\" \/><\/p>\n<h4>Split data<\/h4>\n<p>We&#8217;ll also split the dataset, <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/scikit-train_test_split\/\" target=\"_blank\" rel=\"noopener\">using the train-test split function<\/a> from scikit learn.<\/p>\n<pre>\r\nfrom sklearn.model_selection import train_test_split\r\n(X_train, X_test, y_train, y_test) = train_test_split(x_var, y_var, test_size = .2)\r\n<\/pre>\n<p>This gives us 4 datasets: <\/p>\n<ul>\n<li>training features (X_train)<\/li>\n<li>training target (y_train)<\/li>\n<li>test features (X_test)<\/li>\n<li>test target (y_test)<\/li>\n<\/ul>\n<h4>Initialize Model<\/h4>\n<p>Now, we&#8217;ll initialize a model object.<\/p>\n<p>Here, we&#8217;ll use DummyRegressor for the sake of simplicity.<\/p>\n<pre>\r\nfrom sklearn.dummy import DummyRegressor\r\ndummy_regressor = DummyRegressor()\r\n<\/pre>\n<p>Once you run this, <code>dummy_regressor<\/code> is an sklearn model object, from which we can call the <code>fit<\/code> method.<\/p>\n<p><a id = \"fit-model\"><\/a><\/p>\n<h3>Fit the Model<\/h3>\n<p>Now, we&#8217;ll fit the model:<\/p>\n<pre>\r\ndummy_regressor.fit(X_train.reshape(-1,1), y_train)\r\n<\/pre>\n<p>Here, we&#8217;re fitting the model with <code>X_train<\/code> and <code>y_train<\/code>.  As you can see, the first argument to fit is <code>X_train<\/code> and the second argument is <code>y_train<\/code>.<\/p>\n<p>That&#8217;s typically what we do when we fit a machine learning model.  We commonly fit the model with the &#8220;training&#8221; data.  <\/p>\n<p>Note that X_train <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/numpy-reshape-python\/\" target=\"_blank\" rel=\"noopener\">has been reshaped<\/a> into a 2-dimensional format.<\/p>\n<p><a id = \"predict\"><\/a><\/p>\n<h3>Predict<\/h3>\n<p>Commonly, after we fit a model, we then predict new output values, based on the test features (<code>X_test<\/code>). (Note that <code>X_test<\/code> needs to be in a 2D format, so we&#8217;ll <a href=\"https:\/\/www.sharpsightlabs.com\/blog\/numpy-reshape-python\/\">reshape it with Numpy reshape<\/a>.)<\/p>\n<p>Let&#8217;s quickly do that:<\/p>\n<pre>\r\ndummy_regressor.predict(X_test.reshape(-1,1))\r\n<\/pre>\n<p>OUT:<\/p>\n<pre>\r\narray([5.5831811, 5.5831811, 5.5831811, 5.5831811, 5.5831811, 5.5831811,\r\n       5.5831811, 5.5831811, 5.5831811, 5.5831811, 5.5831811])\r\n<\/pre>\n<p>Here, the model predicts the value 5.5831811 for any input, which may seem strange.  That&#8217;s because we&#8217;re using the DummyRegressor model, for which the prediction is the average of the training y values (the mean of y_train).<\/p>\n<p>Again: this might seem strange, but it&#8217;s useful to use as a baseline, against which you can judge the performance of other machine learning models.<\/p>\n<p>And in this case, it&#8217;s simply a simple example that we can use when trying to learn how to fit a model with sklearn fit.<\/p>\n<h5>Leave your other questions in the comments below<\/h5>\n<p>Do you have other questions about the sklearn fit method?<\/p>\n<p>Is there something that I&#8217;ve missed?<\/p>\n<p>If so, leave your questions in the comments section near the bottom of the page.<\/p>\n<h2>For more machine learning tutorials, sign up for our email list<\/h2>\n<p>In this tutorial, I&#8217;ve shown you how to use the sklearn fit method.<\/p>\n<p>But if you want to master machine learning in Python, there&#8217;s a lot more to learn.<\/p>\n<p>That said, if you want to master scikit learn and machine learning in Python, then sign up for our email list.<\/p>\n<p>When you sign up, you\u2019ll get free tutorials on:<\/p>\n<ul>\n<li>Scikit learn<\/li>\n<li>Machine learning<\/li>\n<li>Deep learning<\/li>\n<li>&#8230; 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} <\/style>\n\t\t\t\t<div class=\"et_bloom_form_container  with_edge carrot_edge et_bloom_form_text_dark et_bloom_form_bottom et_bloom_inline_1_field\">\n\t\t\t\t\t\n\t\t\t<div class=\"et_bloom_form_container_wrapper clearfix\">\n\t\t\t\t<div class=\"et_bloom_header_outer\">\n\t\t\t\t\t<div class=\"et_bloom_form_header et_bloom_header_text_light\">\n\t\t\t\t\t\t\n\t\t\t\t\t\t<div class=\"et_bloom_form_text\">\n\t\t\t\t\t\t<h2>Sign up for FREE data science tutorials<\/h2><p>If you want to master data science fast, sign up for our email list.<\/p>\r\n<p>When you sign up, you'll receive FREE weekly tutorials on how to do data science in R and Python.\u00a0<\/p>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"et_bloom_form_content et_bloom_1_field et_bloom_bottom_inline\">\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t<form method=\"post\" class=\"clearfix\">\n\t\t\t\t\t\t<div class=\"et_bloom_fields\">\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t<p class=\"et_bloom_popup_input et_bloom_subscribe_email\">\n\t\t\t\t\t\t\t\t<input placeholder=\"Enter your best email address\">\n\t\t\t\t\t\t\t<\/p>\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t<button data-optin_id=\"optin_7\" data-service=\"aweber\" data-list_id=\"3573443\" data-page_id=\"6514\" data-account=\"josh@sharpsightlabscom\" data-ip_address=\"true\" class=\"et_bloom_submit_subscription\">\n\t\t\t\t\t\t\t\t<span class=\"et_bloom_subscribe_loader\"><\/span>\n\t\t\t\t\t\t\t\t<span class=\"et_bloom_button_text et_bloom_button_text_color_light\">Give me free tutorials!<\/span>\n\t\t\t\t\t\t\t<\/button>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/form>\n\t\t\t\t\t<div class=\"et_bloom_success_container\">\n\t\t\t\t\t\t<span class=\"et_bloom_success_checkmark\"><\/span>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t<h2 class=\"et_bloom_success_message\">Check your email inbox to confirm your subscription ...<\/h2>\n\t\t\t\t\t\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t<span class=\"et_bloom_close_button\"><\/span>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n<div id=\"mediavine-settings\" data-blocklist-leaderboard=\"1\" data-blocklist-sidebar-atf=\"1\" data-blocklist-sidebar-btf=\"1\" data-blocklist-content-desktop=\"1\" data-blocklist-content-mobile=\"1\" data-blocklist-adhesion-mobile=\"1\" data-blocklist-adhesion-tablet=\"1\" data-blocklist-adhesion-desktop=\"1\" data-blocklist-recipe=\"1\" data-blocklist-auto-insert-sticky=\"1\" data-blocklist-in-image=\"1\" data-blocklist-chicory=\"1\" data-blocklist-zergnet=\"1\" data-blocklist-interstitial-mobile=\"1\" data-blocklist-interstitial-desktop=\"1\" data-blocklist-universal-player-desktop=\"1\" data-blocklist-universal-player-mobile=\"1\" ><\/div>\n","protected":false},"excerpt":{"rendered":"<p>In this tutorial, I&#8217;ll show you how to use the Sklearn Fit method to &#8220;fit&#8221; a machine learning model in Python. So I&#8217;ll quickly review what the method does, I&#8217;ll explain the syntax, and I&#8217;ll show you a step-by-step example of how to use the technique. If you need something specific, just click on any &#8230; <a title=\"A Quick Introduction to the Sklearn Fit Method\" class=\"read-more\" href=\"https:\/\/www.sharpsightlabs.com\/blog\/sklearn-fit\/\" aria-label=\"Read more about A Quick Introduction to the Sklearn Fit Method\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":6521,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","om_disable_all_campaigns":false,"footnotes":""},"categories":[19,84],"tags":[],"publishpress_future_action":{"enabled":false,"date":"2024-05-18 14:51:30","action":"change-status","newStatus":"draft","terms":[],"taxonomy":"category"},"_links":{"self":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts\/6514"}],"collection":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/comments?post=6514"}],"version-history":[{"count":4,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts\/6514\/revisions"}],"predecessor-version":[{"id":8098,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/posts\/6514\/revisions\/8098"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/media\/6521"}],"wp:attachment":[{"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/media?parent=6514"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/categories?post=6514"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sharpsightlabs.com\/wp-json\/wp\/v2\/tags?post=6514"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}