{"id":66991,"date":"2026-10-13T11:04:33","date_gmt":"2026-10-13T08:04:33","guid":{"rendered":"https:\/\/wpcalc.com\/en\/?p=66991"},"modified":"2026-10-13T11:52:00","modified_gmt":"2026-10-13T08:52:00","slug":"confusion-matrix-calculator","status":"publish","type":"post","link":"https:\/\/wpcalc.com\/en\/statistics\/confusion-matrix-calculator\/","title":{"rendered":"Confusion Matrix Calculator"},"content":{"rendered":"<section class=\"not-prose calculator-box\" aria-labelledby=\"calculator-title\"><div class=\"flex items-baseline sm:items-center justify-between gap-2 sm:gap-3\"><div class=\"flex flex-col sm:flex-row sm:items-center gap-2\"><span class=\"icon icon-calculator text-primary-700! text-base! dark:text-primary-300!\" aria-hidden=\"true\"><\/span><h2 id=\"calculator-title\" class=\"text-lg font-display font-bold\">Accuracy, Precision, Recall and F1 from a Confusion Matrix<\/h2><\/div><div class=\"calcdash-favorite group\">\n  <button type=\"button\" class=\"favorite\" id=\"favorite\" data-favorite-id=\"66991\" data-favorite-title=\"Confusion Matrix\" data-favorite-url=\"https:\/\/wpcalc.com\/en\/statistics\/confusion-matrix-calculator\/\" data-favorite-excerpt=\"Enter the four counts of a confusion matrix (TP, FP, FN, TN) to instantly get accuracy, precision, recall, specificity and F1 score, shown as proportions or percentages.\" aria-label=\"Add to Favorites\" data-favorite-icon=\"icon icon-wpcalc-bell-curve\">\n    <span class=\"icon icon-shape-star-empty\" aria-hidden=\"true\"><\/span>\n  <\/button>\n  <div class=\"calcdash-favorite__tip\" aria-hidden=\"true\">\n   <span class=\"favorite-tooltip\" id=\"favorite-tooltip\"><\/span>\n  <\/div>\n  <span class=\"sr-only\" role=\"status\" id=\"favorite-status\"><\/span>\n<\/div><\/div><form action=\"https:\/\/wpcalc.com\/en\/statistics\/confusion-matrix-calculator\/\" method=\"POST\" class=\"calculator\" id=\"calculator-66991\" data-post=\"66991\"><fieldset class=\"fieldset-input is-1\"><legend class=\"sr-only\">Input Fields<\/legend><div class=\"is-single field\" id=\"input-1\"><span class=\"font-semibold\">Confusion matrix counts<\/span><\/div><div class=\"field has-term\" id=\"input-2\"><label for=\"field-2\">True positives<\/label><div class=\"term\">TP<\/div>  <div class=\"absolute bottom-0 left-16 top-8 w-2 h-0.5 bg-blue-500\"><\/div><input type=\"text\" name=\"true_positives\" id=\"field-2\" aria-describedby=\"calc-info-2\" data-number inputmode=\"decimal\" autocomplete=\"off\" spellcheck=\"false\" placeholder=\"e.g. 45\" min=\"0\" value=\"45\"\/><small id=\"calc-info-2\">Positive cases correctly classified as positive.<\/small><\/div><div class=\"field has-term\" id=\"input-3\"><label for=\"field-3\">False positives<\/label><div class=\"term\">FP<\/div>  <div class=\"absolute bottom-0 left-16 top-8 w-2 h-0.5 bg-blue-500\"><\/div><input type=\"text\" name=\"false_positives\" id=\"field-3\" aria-describedby=\"calc-info-3\" data-number inputmode=\"decimal\" autocomplete=\"off\" spellcheck=\"false\" placeholder=\"e.g. 10\" min=\"0\" value=\"10\"\/><small id=\"calc-info-3\">Negative cases incorrectly classified as positive (type I errors).<\/small><\/div><div class=\"field has-term\" id=\"input-4\"><label for=\"field-4\">False negatives<\/label><div class=\"term\">FN<\/div>  <div class=\"absolute bottom-0 left-16 top-8 w-2 h-0.5 bg-blue-500\"><\/div><input type=\"text\" name=\"false_negatives\" id=\"field-4\" aria-describedby=\"calc-info-4\" data-number inputmode=\"decimal\" autocomplete=\"off\" spellcheck=\"false\" placeholder=\"e.g. 15\" min=\"0\" value=\"15\"\/><small id=\"calc-info-4\">Positive cases incorrectly classified as negative (type II errors).<\/small><\/div><div class=\"field has-term\" id=\"input-5\"><label for=\"field-5\">True negatives<\/label><div class=\"term\">TN<\/div>  <div class=\"absolute bottom-0 left-16 top-8 w-2 h-0.5 bg-blue-500\"><\/div><input type=\"text\" name=\"true_negatives\" id=\"field-5\" aria-describedby=\"calc-info-5\" data-number inputmode=\"decimal\" autocomplete=\"off\" spellcheck=\"false\" placeholder=\"e.g. 80\" min=\"0\" value=\"80\"\/><small id=\"calc-info-5\">Negative cases correctly classified as negative.<\/small><\/div><div class=\"is-single field\" id=\"input-6\"><span class=\"font-semibold\">Output options<\/span><\/div><div class=\"field\" id=\"input-7\"><label for=\"field-7\">Show metrics as<\/label><div class=\"grid grid-cols-1\"><select id=\"field-7\" name=\"show_metrics_as\"><option value=\"prop\">Proportions (0\u20131)<\/option><option value=\"pct\" selected>Percentages (0\u2013100%)<\/option><\/select><svg class=\"pointer-events-none col-start-1 row-start-1 mr-2 size-5 self-center justify-self-end text-gray-500 sm:size-4\" viewBox=\"0 0 16 16\" fill=\"currentColor\" aria-hidden=\"true\" data-slot=\"icon\">\n              <path fill-rule=\"evenodd\" d=\"M4.22 6.22a.75.75 0 0 1 1.06 0L8 8.94l2.72-2.72a.75.75 0 1 1 1.06 1.06l-3.25 3.25a.75.75 0 0 1-1.06 0L4.22 7.28a.75.75 0 0 1 0-1.06Z\" clip-rule=\"evenodd\" \/>\n            <\/svg><\/div><\/div><\/fieldset><div class=\"buttons\"><button type=\"submit\" data-text=\"Re-Calculate\" id=\"calculate-button\" data-post=\"66991\">Calculate<\/button><button type=\"reset\">Reset<\/button><\/div><div class=\"field is-checkbox hidden!\" id=\"field-auto-calc\"><input type=\"checkbox\" id=\"auto-calc\"><label for=\"auto-calc\">Calculate automatically<\/label><small>If enabled, the result will update automatically when you change any value.<\/small><\/div><div class=\"fieldset-result is-hidden\" aria-labelledby=\"result-title\" aria-live=\"polite\" role=\"region\"> <h3 class=\"result-title bg-gradient-to-r from-primary-50 to-gray-50 dark:from-primary-900 dark:to-gray-800\" id=\"result-title\"><span class=\"icon icon-s-pulse\" aria-hidden=\"true\"><\/span> Classification Performance Metrics<\/h3><div class=\"result-box\"><div class=\"field-result\" id=\"output-1\"><span class=\"field-title\"><span>Total predictions<\/span><span class=\"result-term\">n<\/span><\/span><span class=\"field-value\" id=\"result-1\"><\/span><button type=\"button\" class=\"copy-result\" data-tooltip=\"Copy Result\" aria-label=\"Copy Result\"><span class=\"copy-icon icon icon-document-copy\" aria-hidden=\"true\"><\/span><\/button><\/div><div class=\"field-result\" id=\"output-2\"><span class=\"field-title\"><span>Accuracy<\/span><span class=\"result-term\">ACC<\/span><\/span><span class=\"field-value\" id=\"result-2\"><\/span><button type=\"button\" class=\"copy-result\" data-tooltip=\"Copy Result\" aria-label=\"Copy Result\"><span class=\"copy-icon icon icon-document-copy\" aria-hidden=\"true\"><\/span><\/button><\/div><div class=\"field-result\" id=\"output-3\"><span class=\"field-title\"><span>Precision (positive predictive value)<\/span><span class=\"result-term\">PPV<\/span><\/span><span class=\"field-value\" id=\"result-3\"><\/span><button type=\"button\" class=\"copy-result\" data-tooltip=\"Copy Result\" aria-label=\"Copy Result\"><span class=\"copy-icon icon icon-document-copy\" aria-hidden=\"true\"><\/span><\/button><\/div><div class=\"field-result\" id=\"output-4\"><span class=\"field-title\"><span>Recall (sensitivity, true positive rate)<\/span><span class=\"result-term\">TPR<\/span><\/span><span class=\"field-value\" id=\"result-4\"><\/span><button type=\"button\" class=\"copy-result\" data-tooltip=\"Copy Result\" aria-label=\"Copy Result\"><span class=\"copy-icon icon icon-document-copy\" aria-hidden=\"true\"><\/span><\/button><\/div><div class=\"field-result\" id=\"output-5\"><span class=\"field-title\"><span>Specificity (true negative rate)<\/span><span class=\"result-term\">TNR<\/span><\/span><span class=\"field-value\" id=\"result-5\"><\/span><button type=\"button\" class=\"copy-result\" data-tooltip=\"Copy Result\" aria-label=\"Copy Result\"><span class=\"copy-icon icon icon-document-copy\" aria-hidden=\"true\"><\/span><\/button><\/div><div class=\"field-result\" id=\"output-6\"><span class=\"field-title\"><span>F1 score<\/span><span class=\"result-term\">F\u2081<\/span><\/span><span class=\"field-value\" id=\"result-6\"><\/span><button type=\"button\" class=\"copy-result\" data-tooltip=\"Copy Result\" aria-label=\"Copy Result\"><span class=\"copy-icon icon icon-document-copy\" aria-hidden=\"true\"><\/span><\/button><\/div><div class=\"field-result is-column\" id=\"output-7\"><span class=\"field-title\"><span>How the metrics are computed and how to read them<\/span><\/span><span class=\"field-value\" id=\"result-7\"><\/span><button type=\"button\" class=\"copy-result\" data-tooltip=\"Copy Result\" aria-label=\"Copy Result\"><span class=\"copy-icon icon icon-document-copy\" aria-hidden=\"true\"><\/span><\/button><\/div><\/div><div class=\"result-actions\"><button type=\"button\" class=\"copy-link\"><svg viewBox=\"0 0 20 20\" fill=\"currentColor\" aria-hidden=\"true\"><path d=\"M12.232 4.232a2.5 2.5 0 0 1 3.536 3.536l-1.225 1.224a.75.75 0 0 0 1.061 1.06l1.224-1.224a4 4 0 0 0-5.656-5.656l-3 3a4 4 0 0 0 .225 5.865.75.75 0 0 0 .977-1.138 2.5 2.5 0 0 1-.142-3.667l3-3Z\"\/><path d=\"M11.603 7.963a.75.75 0 0 0-.977 1.138 2.5 2.5 0 0 1 .142 3.667l-3 3a2.5 2.5 0 0 1-3.536-3.536l1.225-1.224a.75.75 0 0 0-1.061-1.06l-1.224 1.224a4 4 0 1 0 5.656 5.656l3-3a4 4 0 0 0-.225-5.865Z\"\/><\/svg><span class=\"copy-link__text\">Copy link to this calculation<\/span><\/button><span class=\"sr-only\" role=\"status\" id=\"copy-link-status\"><\/span><\/div><\/div><a href=\"#respond\" class=\"hidden transition-opacity duration-300 opacity-0 w-50 text-sm justify-center items-center gap-2 px-4 py-2 rounded bg-gray-200 text-gray-700 hover:bg-gray-300\" id=\"leave-comment\"><span class=\"icon icon-comments\" aria-hidden=\"true\"><\/span>Leave a Comment<\/a><\/form><\/section><section id=\"calc-reactions\" class=\"not-prose hidden my-12 bg-gradient-to-r from-primary-50 to-gray-50 border border-indigo-100 rounded-xl px-6 py-4 shadow-sm\" data-post=\"66991\" aria-live=\"polite\" aria-labelledby=\"calc-reactions-title\"><p id=\"calc-reactions-title\" class=\"text-sm text-gray-500 text-center mb-2\">How did this result make you feel?<\/p><div role=\"group\" aria-labelledby=\"calc-reactions-title\" class=\"grid grid-cols-3 sm:flex sm:flex-row sm:justify-around gap-4 sm:items-center sm:flex-wrap\"><button type=\"button\" aria-pressed=\"false\" class=\"reaction-btn flex flex-col items-center gap-1 text-sm text-gray-700 hover:text-primary-600 cursor-pointer transition-transform hover:scale-105\" data-reaction=\"like\"><span class=\"reaction-count\" data-reaction-count=\"like\">0<\/span><span class=\"reaction text-3xl\" aria-hidden=\"true\">\ud83d\ude00 <\/span><span class=\"reaction-description font-medium\">Like <\/span><\/button><button type=\"button\" aria-pressed=\"false\" class=\"reaction-btn flex flex-col items-center gap-1 text-sm text-gray-700 hover:text-primary-600 cursor-pointer transition-transform hover:scale-105\" data-reaction=\"helpful\"><span class=\"reaction-count\" data-reaction-count=\"helpful\">0<\/span><span class=\"reaction text-3xl\" aria-hidden=\"true\">\ud83d\udca1 <\/span><span class=\"reaction-description font-medium\">Helpful <\/span><\/button><button type=\"button\" aria-pressed=\"false\" class=\"reaction-btn flex flex-col items-center gap-1 text-sm text-gray-700 hover:text-primary-600 cursor-pointer transition-transform hover:scale-105\" data-reaction=\"confused\"><span class=\"reaction-count\" data-reaction-count=\"confused\">0<\/span><span class=\"reaction text-3xl\" aria-hidden=\"true\">\ud83d\ude15 <\/span><span class=\"reaction-description font-medium\">Confused <\/span><\/button><button type=\"button\" aria-pressed=\"false\" class=\"reaction-btn flex flex-col items-center gap-1 text-sm text-gray-700 hover:text-primary-600 cursor-pointer transition-transform hover:scale-105\" data-reaction=\"disappointed\"><span class=\"reaction-count\" data-reaction-count=\"disappointed\">0<\/span><span class=\"reaction text-3xl\" aria-hidden=\"true\">\ud83d\ude1e <\/span><span class=\"reaction-description font-medium\">Disappointed <\/span><\/button><button type=\"button\" aria-pressed=\"false\" class=\"reaction-btn flex flex-col items-center gap-1 text-sm text-gray-700 hover:text-primary-600 cursor-pointer transition-transform hover:scale-105\" data-reaction=\"inaccurate\"><span class=\"reaction-count\" data-reaction-count=\"inaccurate\">0<\/span><span class=\"reaction text-3xl\" aria-hidden=\"true\">\u274c <\/span><span class=\"reaction-description font-medium\">Inaccurate <\/span><\/button><\/div><div id=\"reaction-message\" class=\"hidden mt-8 rounded-md border border-primary-100 bg-white\/50 backdrop-blur-sm px-4 py-2 text-sm text-center shadow-sm\"><\/div><\/section><div class=\"calcdash-ad not-prose calcdash-ad--calc_result calcdash-ad--in-article\"><ins class=\"adsbygoogle\" style=\"display:block\" data-ad-client=\"ca-pub-1721569815777345\" data-ad-slot=\"6317458308\" data-ad-layout=\"in-article\" data-ad-format=\"fluid\"><\/ins><script>(adsbygoogle = window.adsbygoogle || []).push({});<\/script><\/div>\n<style id=\"wpcoderpro-inline-1\">.formula-box {\r\n\tborder-bottom: none !important;\r\n}<\/style><section class=\"formula-box\" aria-labelledby=\"formula-title\">\r\n\t<div id=\"formula-title\" class=\"not-prose font-display mb-4\"><span class=\"icon icon-formula\" aria-hidden=\"true\"><\/span><h2>Confusion Matrix<\/h2><\/div>\r\n\t<figure class=\"not-prose formula\">\r\n\t\t<figcaption class=\"formula-title\">Formula<\/figcaption>\r\n\t\t<div class=\"text-base text-gray-800\" id=\"formula\">\n\\[ n = TP + FP + FN + TN \\\\ \\text{Accuracy} = \\frac{TP + TN}{n} \\\\ \\text{Precision} = \\frac{TP}{TP + FP} \\\\ \\text{Recall} = \\frac{TP}{TP + FN} \\\\ \\text{Specificity} = \\frac{TN}{TN + FP} \\\\ F_1 = \\frac{2TP}{2TP + FP + FN} = \\frac{2 \\cdot \\text{Precision} \\cdot \\text{Recall}}{\\text{Precision} + \\text{Recall}} \\]\n<\/div>\r\n\t<\/figure>\r\n\t<\/p>\n<p><strong>Where:<\/strong><\/p>\n<ul>\n<li>\\(TP\\) \u2014 true positives: positive cases correctly classified as positive<\/li>\n<li>\\(FP\\) \u2014 false positives: negative cases incorrectly classified as positive (type I errors)<\/li>\n<li>\\(FN\\) \u2014 false negatives: positive cases incorrectly classified as negative (type II errors)<\/li>\n<li>\\(TN\\) \u2014 true negatives: negative cases correctly classified as negative<\/li>\n<li>\\(n\\) \u2014 total number of classified cases<\/li>\n<\/ul>\n<p>These five metrics all range from 0 (worst) to 1 (perfect) and summarize different aspects of a binary classifier's performance. When a denominator is zero, the corresponding metric is undefined and the calculator shows \"\u2014\".<\/p>\n<p>\r\n<\/section>\n<style id=\"wpcoderpro-inline-459\">.calculation-box {\r\n\tborder-bottom: none !important;\r\n}<\/style><section id=\"calculation\" class=\"calculation-box\" aria-labelledby=\"calculation-title\">\r\n\t<div id=\"calculation-title\" class=\"not-prose font-display\"><span class=\"icon icon-unordered-list\" aria-hidden=\"true\"><\/span><h2>Confusion Matrix \u2013 Calculation Example<\/h2><\/div>\r\n\r\n<\/p>\n<p><strong>Example:<\/strong> a model screened 150 cases. It correctly flagged 45 positive cases (TP) and missed 15 (FN), while correctly passing 80 negative cases (TN) and raising 10 false alarms (FP).<\/p>\n<p>\\[ n = 45 + 10 + 15 + 80 = 150 \\]<\/p>\n<p>\\[ \\text{Accuracy} = \\frac{45 + 80}{150} = 0.8333 \\quad (83.33\\%) \\]<\/p>\n<p>\\[ \\text{Precision} = \\frac{45}{45 + 10} = 0.8182 \\quad (81.82\\%) \\]<\/p>\n<p>\\[ \\text{Recall} = \\frac{45}{45 + 15} = 0.75 \\quad (75\\%) \\]<\/p>\n<p>\\[ \\text{Specificity} = \\frac{80}{80 + 10} = 0.8889 \\quad (88.89\\%) \\]<\/p>\n<p>\\[ F_1 = \\frac{2 \\times 45}{2 \\times 45 + 10 + 15} = \\frac{90}{115} = 0.7826 \\quad (78.26\\%) \\]<\/p>\n<p><strong>Result:<\/strong> Accuracy 83.33%, precision 81.82%, recall 75%, specificity 88.89%, F1 score 78.26% (with the output set to percentages).<\/p>\n<p>\n\r\n<\/section>\n<h2>How to use the calculator<\/h2>\n<ol>\n<li>Count how many cases fall in each cell of your confusion matrix: enter them under <strong>True positives<\/strong>, <strong>False positives<\/strong>, <strong>False negatives<\/strong> and <strong>True negatives<\/strong>. All four values must be whole numbers \\(\\ge 0\\).<\/li>\n<li>Choose <strong>Show metrics as<\/strong>: \"Proportions (0\u20131)\" for decimal values or \"Percentages (0\u2013100%)\" for percent values.<\/li>\n<li>Read the results: <strong>Total predictions<\/strong>, <strong>Accuracy<\/strong>, <strong>Precision (positive predictive value)<\/strong>, <strong>Recall (sensitivity, true positive rate)<\/strong>, <strong>Specificity (true negative rate)<\/strong> and <strong>F1 score<\/strong>, followed by a summary of how the metrics were computed and how to interpret them.<\/li>\n<li>If a metric is shown as \"\u2014\", its denominator was zero \u2014 for example, precision is undefined when the model never predicted a positive case (\\(TP + FP = 0\\)).<\/li>\n<\/ol>\n<h2>What is a confusion matrix?<\/h2>\n<p>A confusion matrix is the basic report card of a binary classifier. It is a 2\u00d72 table that cross-tabulates the true class of each case (positive or negative) against the class the model predicted. The four cells \u2014 true positives, false positives, false negatives and true negatives \u2014 contain every possible outcome, so their sum \\(n\\) is the total number of classified cases. From these four numbers alone you can derive every standard measure of classification quality.<\/p>\n<p>The five metrics computed here each answer a different question. <strong>Accuracy<\/strong> asks: overall, what fraction of predictions were correct? <strong>Precision<\/strong> asks: when the model says \"positive\", how often is it right? Low precision means many false alarms. <strong>Recall<\/strong> (also called sensitivity or true positive rate) asks: of all cases that really are positive, how many did the model catch? Low recall means missed cases. <strong>Specificity<\/strong> is the recall of the negative class \u2014 how well the model recognizes true negatives. <strong>F1<\/strong> is the harmonic mean of precision and recall, which balances the two: it is high only when both are high.<\/p>\n<p>Why not just use accuracy? Because accuracy is misleading when classes are imbalanced. If only 1% of cases are positive, a trivial model that labels everything negative scores 99% accuracy while catching zero positives. Precision, recall and F1 focus on the positive class and expose such failures immediately. Which metric matters most depends on the cost of errors: in cancer screening, a missed tumor (false negative) is far worse than a false alarm, so recall is prioritized; in spam filtering, a lost legitimate email (false positive) may be worse, so precision matters more.<\/p>\n<p>In medical and epidemiological contexts the same numbers carry different names: recall is <em>sensitivity<\/em>, precision is the <em>positive predictive value<\/em> (PPV), and the share of actual positives \\((TP + FN)\/n\\) is the <em>prevalence<\/em>. If you know the sensitivity, specificity and prevalence of a test, you can work forward to the predictive values with <a href=\"https:\/\/wpcalc.com\/en\/statistics\/bayes-theorem-calculator\/\">Bayes' Theorem<\/a>.<\/p>\n<h2>Assumptions, limitations and practical tips<\/h2>\n<ul>\n<li>The calculator treats the four counts as exact integers from a single classification run. It assumes each case belongs to exactly one cell and that the \"positive\" class is the one you care about detecting.<\/li>\n<li>All metrics are computed on the entered sample, so they are estimates subject to sampling error. With small counts (say, under 30 positives), report a confidence interval \u2014 a Wilson interval is a good choice for proportions \u2014 rather than a bare percentage.<\/li>\n<li>Metrics with a zero denominator are undefined and shown as \"\u2014\": precision when no positives were predicted, recall when there are no actual positives, specificity when there are no actual negatives, and F1 when both precision and recall are zero.<\/li>\n<li>F1 weights precision and recall equally. If one matters more in your application, use the \\(F_\\beta\\) score instead (e.g. \\(F_2\\) favors recall, \\(F_{0.5}\\) favors precision).<\/li>\n<li>The confusion matrix says nothing about <em>why<\/em> the model errs or how it compares to another model on the same data. To test whether two classifiers (or two raters) differ significantly on paired outcomes, use <a href=\"https:\/\/wpcalc.com\/en\/statistics\/mcnemars-test-calculator\/\">McNemar's Test<\/a>; for independent proportions, the <a href=\"https:\/\/wpcalc.com\/en\/statistics\/two-proportion-z-test-calculator\/\">Two-Proportion Z-Test<\/a> or <a href=\"https:\/\/wpcalc.com\/en\/statistics\/fishers-exact-test-calculator\/\">Fisher's Exact Test<\/a> applies.<\/li>\n<li>A confusion matrix requires hard (binary) decisions. If your model outputs probabilities, the counts \u2014 and therefore every metric \u2014 depend on the threshold you choose to convert scores into labels, so always report the threshold alongside the metrics.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Enter the four counts of a confusion matrix (TP, FP, FN, TN) to instantly get accuracy, precision, recall, specificity and F1 score, shown as proportions or percentages.<\/p>\n","protected":false},"author":3168,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[74],"tags":[75],"class_list":["post-66991","post","type-post","status-publish","format-standard","hentry","category-statistics","tag-descriptive-statistics"],"acf":[],"_links":{"self":[{"href":"https:\/\/wpcalc.com\/en\/wp-json\/wp\/v2\/posts\/66991","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wpcalc.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wpcalc.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wpcalc.com\/en\/wp-json\/wp\/v2\/users\/3168"}],"replies":[{"embeddable":true,"href":"https:\/\/wpcalc.com\/en\/wp-json\/wp\/v2\/comments?post=66991"}],"version-history":[{"count":2,"href":"https:\/\/wpcalc.com\/en\/wp-json\/wp\/v2\/posts\/66991\/revisions"}],"predecessor-version":[{"id":67084,"href":"https:\/\/wpcalc.com\/en\/wp-json\/wp\/v2\/posts\/66991\/revisions\/67084"}],"wp:attachment":[{"href":"https:\/\/wpcalc.com\/en\/wp-json\/wp\/v2\/media?parent=66991"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wpcalc.com\/en\/wp-json\/wp\/v2\/categories?post=66991"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wpcalc.com\/en\/wp-json\/wp\/v2\/tags?post=66991"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}