{"id":258,"date":"2020-01-24T15:40:51","date_gmt":"2020-01-24T06:40:51","guid":{"rendered":"http:\/\/aiacademy.jp\/media\/?p=258"},"modified":"2024-08-08T16:24:10","modified_gmt":"2024-08-08T07:24:10","slug":"%e6%a9%9f%e6%a2%b0%e5%ad%a6%e7%bf%92%e3%81%ae%e5%88%86%e9%a1%9e%e3%81%ae%e8%a9%95%e4%be%a1%e6%8c%87%e6%a8%99%e3%81%ab%e9%96%a2%e3%81%97%e3%81%a6%e5%ad%a6%e3%81%bc%e3%81%86%ef%bc%81","status":"publish","type":"post","link":"https:\/\/aiacademy.jp\/media\/?p=258","title":{"rendered":"\u6a5f\u68b0\u5b66\u7fd2\u306e\u5206\u985e\u306b\u304a\u3051\u308b\u8a55\u4fa1\u6307\u6a19\u306b\u95a2\u3057\u3066\u5b66\u307c\u3046\uff01"},"content":{"rendered":"<div id=\"sgb-css-id-1\">\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-font-size is-style-fill has-medium-font-size\"><a class=\"wp-block-button__link has-vivid-green-cyan-background-color has-background wp-element-button\" href=\"https:\/\/lin.ee\/3E4GzWk\" rel=\"nofollow noopener\" target=\"_blank\">LINE\u53cb\u3060\u3061\u767b\u9332 \u25b6 \u6a5f\u68b0\u5b66\u7fd2\u30b3\u30fc\u30b9 \u25b6 \u53d7\u8b1b\u5272\u5f15\u30af\u30fc\u30dd\u30f3 \u7121\u6599\u52d5\u753b<\/a><\/div>\n<\/div>\n<\/div>\n\n\n<h1 class=\"wp-block-heading\" id=\"i-0\">\u672c\u8a18\u4e8b\u306f\u4ee5\u4e0b\u306e\u30c6\u30ad\u30b9\u30c8\u306b\u79fb\u884c\u3057\u3066\u304a\u308a\u307e\u3059\u3002<\/h1>\n\n\n\n<p><\/p>\n\n\n\n<p><a href=\"https:\/\/aiacademy.jp\/texts\/show\/?id=34&amp;context=subject-metrics\">https:\/\/aiacademy.jp\/texts\/show\/?id=34&amp;context=subject-metrics<\/a><\/p>\n\n\n<div class=\"toc\">    <div id=\"toc_container\" class=\"sgb-toc--no-bullets js-smooth-scroll\" data-dialog-title=\"Table of Contents\">\n      <p class=\"toc_title\">\u76ee\u6b21 <\/p>\n      <ul class=\"toc_list\">  <li class=\"first last\">    <a href=\"#i-0\">\u672c\u8a18\u4e8b\u306f\u4ee5\u4e0b\u306e\u30c6\u30ad\u30b9\u30c8\u306b\u79fb\u884c\u3057\u3066\u304a\u308a\u307e\u3059\u3002<\/a>    <ul class=\"menu_level_1\">      <li class=\"first\">        <a href=\"#i-1\">\u306f\u3058\u3081\u306b<\/a>      <\/li>      <li>        <a href=\"#i-2\">\u5206\u985e\u3067\u306e\u8a55\u4fa1\u65b9\u6cd5<\/a>      <\/li>      <li>        <a href=\"#i-3\">\u6df7\u540c\u884c\u5217\u3068\u306f<\/a>      <\/li>      <li>        <a href=\"#i-4\">\u6b63\u89e3\u7387(Accuracy)<\/a>      <\/li>      <li>        <a href=\"#i-5\">\u9069\u5408\u7387\uff08Precision\uff09<\/a>      <\/li>      <li>        <a href=\"#i-6\">\u518d\u73fe\u7387\uff08Recall\uff09<\/a>      <\/li>      <li>        <a href=\"#i-7\">F\u5024\uff08F-measure\uff09<\/a>      <\/li>      <li class=\"last\">        <a href=\"#i-8\">\u307e\u3068\u3081<\/a>      <\/li>    <\/ul>  <\/li><\/ul>\n      \n    <\/div><\/div><div class=\"toc\"><p><\/p>\n<\/div><h2 id=\"i-1\">\u306f\u3058\u3081\u306b<\/h2>\n<p>\u3053\u306e\u7ae0\u3067\u306f\u6a5f\u68b0\u5b66\u7fd2\u306e\u7d50\u679c\u3092\u8a55\u4fa1\u3059\u308b\u65b9\u6cd5\u306b\u95a2\u3057\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<br>\u4e00\u90e8\u6570\u5f0f\u304c\u51fa\u3066\u304d\u305f\u308a\u3057\u307e\u3059\u304c\u3001\u3044\u304d\u306a\u308a\u7406\u89e3\u3057\u3088\u3046\u3068\u305b\u305a\u3001\u5f90\u3005\u306b\u7406\u89e3\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308c\u3070\u5927\u4e08\u592b\u3067\u3059\u3002<br>\u3053\u306e\u30c6\u30ad\u30b9\u30c8\u3067\u306f\u3001Scikit-learn\u3092\u7528\u3044\u3066\u3001\u8a55\u4fa1\u306e\u65b9\u6cd5\u3092\u5b66\u3073\u307e\u3059\u306e\u3067\u3001\u5b9f\u969b\u306b\u624b\u3092\u52d5\u304b\u3057\u306a\u304c\u3089\u9032\u3081\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<h2 id=\"i-2\">\u5206\u985e\u3067\u306e\u8a55\u4fa1\u65b9\u6cd5<\/h2>\n<p>\u5206\u985e\u306b\u304a\u3044\u3066\u4f7f\u3046\u4e3b\u306a\u8a55\u4fa1\u6307\u6a19\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<br><strong>\u30fb\u6df7\u540c\u884c\u5217\uff08Confusion matrix\uff09<\/strong><br><strong>\u30fb\u6b63\u89e3\u7387(Accuracy)<\/strong><br><strong>\u30fb\u9069\u5408\u7387\uff08Precision\uff09<\/strong><br><strong>\u30fb\u518d\u73fe\u7387\uff08Recall\uff09<\/strong><br><strong>\u30fbF\u5024\uff08F-measure\uff09<\/strong><br>\u4e00\u3064\u305a\u3064\u898b\u3066\u3044\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\n<h2 id=\"i-3\">\u6df7\u540c\u884c\u5217\u3068\u306f<\/h2>\n<p><strong>\u6df7\u540c\u884c\u5217\u306f\u4e8c\u5024\u5206\u985e\uff08\u6b63\u4e8b\u4f8b\u3068\u8ca0\u4e8b\u4f8b\u306e\u4e88\u6e2c\uff09\u306e\u7d50\u679c\u3092\u307e\u3068\u3081\u305f\u8868\u3067\u3059\u3002<\/strong><br>\u5206\u985e\u7d50\u679c\u3092\u8868\u5f62\u5f0f\u306b\u307e\u3068\u3081\u308b\u3053\u3068\u3067\u3069\u306e\u30e9\u30d9\u30eb\u3092\u6b63\u3057\u304f\u5206\u985e\u3067\u304d\u3001\u3069\u306e\u30e9\u30d9\u30eb\u3092\u8aa4\u3063\u3066\u5206\u985e\u3057\u305f\u304b\u3092\u8abf\u3079\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u3002<br>\u771f\u306e\u5024\u3068\u4e88\u6e2c\u3057\u305f\u5024\u306e\u7d44\u307f\u5408\u308f\u305b\u306b\u306f\u3001\u305d\u308c\u305e\u308c\u540d\u79f0\u304c\u3042\u308a\u4ee5\u4e0b\u306e\u56f3\u3088\u3046\u306b\u547c\u3070\u308c\u307e\u3059\u3002<br><em>\u203b\u4e0b\u306e\u6df7\u540c\u884c\u5217\u3067\u306f\u7e26\u8ef8\u3068\u6a2a\u8ef8\u306f\u4ee5\u4e0b\u306e\u753b\u50cf\u306e\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u307e\u3059\u304c\u3001\u4ed6\u306e\u6587\u732e\u7b49\u3067\u306f\u9006\u306b\u306a\u3063\u3066\u3044\u308b\u5834\u5408\u3082\u3042\u308a\u307e\u3059\u3002\u3069\u3061\u3089\u306e\u6df7\u540c\u884c\u5217\u306e\u5f62\u3067\u3082\u8aad\u3081\u308b\u3088\u3046\u306b\u3057\u307e\u3057\u3087\u3046\u3002<\/em><\/p>\n<p><img decoding=\"async\" src=\"\/assets\/images_test\/34_16bf47ea3d3.png\" alt=\"\"><\/p>\n<p><strong>True Positive(TP) \u30fb\u30fb\u30fb\u771f\u306e\u5024\u304c\u6b63\u4e8b\u4f8b\u306e\u3082\u306e\u306b\u5bfe\u3057\u3066\u3001\u6b63\u4e8b\u4f8b\u3068\u4e88\u6e2c\u3057\u305f\u3082\u306e (<em>\u771f\u967d\u6027<\/em>)<\/strong><\/p>\n<p><strong>False Positive(FP) \u30fb\u30fb\u30fb\u771f\u306e\u5024\u304c\u8ca0\u4e8b\u4f8b\u306e\u3082\u306e\u306b\u5bfe\u3057\u3066\u3001\u6b63\u4e8b\u4f8b\u3068\u4e88\u6e2c\u3057\u305f\u3082\u306e(<em>\u507d\u967d\u6027<\/em>)<\/strong><\/p>\n<p><strong>False Negative(FN) \u30fb\u30fb\u30fb\u771f\u306e\u5024\u304c\u6b63\u4e8b\u4f8b\u306e\u3082\u306e\u306b\u5bfe\u3057\u3066\u3001\u8ca0\u4e8b\u4f8b\u3068\u4e88\u6e2c\u3057\u305f\u3082\u306e(<em>\u507d\u9670\u6027<\/em>)<\/strong><\/p>\n<p><strong>True Negative(TN) \u30fb\u30fb\u30fb\u771f\u306e\u5024\u304c\u8ca0\u4e8b\u4f8b\u306e\u3082\u306e\u306b\u5bfe\u3057\u3066\u3001\u8ca0\u4e8b\u4f8b\u3068\u4e88\u6e2c\u3057\u305f\u3082\u306e(<em>\u771f\u9670\u6027<\/em>)<\/strong><\/p>\n<p>\u3053\u306e<strong>TP\u3001FP\u3001FN\u3001TN\u306e\u5024\u3092\u53ef\u8996\u5316\u3057\u305f\u3082\u306e\u304c\u6df7\u540c\u884c\u5217\u3067\u3059\u3002<\/strong><br>\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u5165\u529b\u3057\u3066\u3001\u6df7\u540c\u884c\u5217\u3092\u307f\u3066\u307f\u307e\u3057\u3087\u3046\u3002<br>\u307e\u305a\u3001\u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/p>\n<pre><code class=\"python:\">y_test = [0,0,0,0,0,1,1,1,1,1] # 0\u3092Negative 1\u3092Positive\u3068\u3059\u308b\ny_pred = [0,1,0,0,0,0,0,1,1,1]\n<\/code><\/pre>\n<p>y_test\u306f\u771f\u306e\u5024\u3001y_pred\u306f\u6a5f\u68b0\u5b66\u7fd2\u306b\u3088\u3063\u3066\u4e88\u6e2c\u3057\u305f\u5024\u3060\u3068\u8003\u3048\u3066\u304f\u3060\u3055\u3044\u3002<br>\u307e\u305f0\u3092\u9670\u6027\uff08Negative\uff09\u30011\u3092\u967d\u6027\uff08Positive\uff09\u3068\u3059\u308b\u3068\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u5bfe\u5fdc\u95a2\u4fc2\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<pre><code class=\"shell:\">0 = Negative, 1 = Positive\n\n          \u3000\u3000\u3000Predicted\n        \u3000\u3000\u3000\u3000\u3000 0    \u3000\u3000\u3000\u3000\u3000\u30001\nActual 0  TN   \u3000\u3000FP\n\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\u30001  \u3000FN\u3000\u3000\u3000\u3000\u3000TP\n<\/code><\/pre>\n<p>\u6b21\u306b\u3001<strong>scikit-learn\u306econfusion_matrix\u306b\u3088\u3063\u3066\u6df7\u540c\u884c\u5217\u3092\u53ef\u8996\u5316\u3057\u307e\u3059\u3002<\/strong><\/p>\n<pre><code class=\"python:\">from sklearn.metrics import confusion_matrix\n\ncmatrix = confusion_matrix(y_test,y_pred)\nprint(cmatrix )\n<\/code><\/pre>\n<p>\u5b9f\u884c\u3059\u308b\u3068\u3001\u4ee5\u4e0b\u304c\u51fa\u529b\u3055\u308c\u307e\u3059\u3002<\/p>\n<pre><code class=\"shell:\">[[4 1]\n[2 3]]\n<\/code><\/pre>\n<p>\u305d\u308c\u305e\u308c\u306e\u5024\u3092\u53d6\u5f97\u3057\u3066\u307f\u307e\u3059\u3002<\/p>\n<pre><code class=\"python:\">tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel() #\u3000\u6df7\u540c\u884c\u5217\u306e\u305d\u308c\u305e\u308c\u306e\u7d50\u679c\u3092\u53d6\u5f97\nprint(\"TN\", tn)\nprint(\"FP\", fp)\nprint(\"FN\", fn)\nprint(\"TP\", tp)\n<\/code><\/pre>\n<p>\u51fa\u529b\u7d50\u679c\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<pre><code class=\"shell:\">TN 4\nFP 1\nFN 2\nTP 3\n<\/code><\/pre>\n<h2 id=\"i-4\">\u6b63\u89e3\u7387(Accuracy)<\/h2>\n<p>\u6b63\u89e3\u7387\u3068\u306f\u3001\u4e88\u6e2c\u7d50\u679c\u5168\u4f53\u304c\u3069\u308c\u304f\u3089\u3044\u771f\u306e\u5024\u3068\u4e00\u81f4\u3057\u3066\u3044\u308b\u304b\u3092\u8868\u3059\u6307\u6a19\u3067\u3059\u3002<br>\u4ee5\u4e0b\u306e\u5f0f\u3067\u6c42\u3081\u3089\u308c\u307e\u3059\u3002<\/p>\n<p>$$<br>Accuracy = \\displaystyle\\frac{TP+TN}{TP+FP+FN+TN}<br>$$<\/p>\n<p>\u6b63\u89e3\u7387\u306f\u3001accuracy_score\u3067\u6c42\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<pre><code class=\"python:\">from sklearn.metrics import accuracy_score\nprint('Accuracy:',accuracy_score(y_test,y_pred))\n<\/code><\/pre>\n<p>\u5b9f\u884c\u3059\u308b\u3068\u3001\u6b21\u306e\u3088\u3046\u306a\u5024\u304c\u51fa\u529b\u3055\u308c\u307e\u3059\u3002<\/p>\n<pre><code class=\"shell:\">Accuracy: 0.7\n<\/code><\/pre>\n<p>\u4e0a\u306eAccuracy\u306e\u5f0f\u306e\u3088\u3046\u306b\u3001\u6b21\u306e\u3088\u3046\u306b\u3057\u3066\u3082\u540c\u3058\u51fa\u529b\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<pre><code class=\"python:\">tp, fn, fp, tn = confusion_matrix(y_test, y_pred).ravel() \nprint((tp + tn) \/ (tp + fp + fn + tn))\u3000# 0.7\n<\/code><\/pre>\n<p><em>\u6ce8\u610f\u3059\u3079\u304d\u70b9\u306f\u3001\u6b63\u89e3\u7387\u304c\u9ad8\u3051\u308c\u3070\u3088\u3044\u30e2\u30c7\u30eb\u3068\u3044\u3046\u308f\u3051\u3067\u306f\u306a\u3044\u3068\u3044\u3046\u3053\u3068\u3067\u3059\u3002<\/em><br>\u4f8b\u3048\u3070\u3001<br>\u771f\u306e\u5024\u304c[1, 1, 1, 1, 1, 1, 1, 1, 1, 0]<br>\u3068\u8ca0\u4e8b\u4f8b\u304c1\u3064\u306e\u3082\u306e\u3060\u3068\u3057\u307e\u3059\u3002<br>\u6a5f\u68b0\u5b66\u7fd2\u3092\u3057\u305f\u7d50\u679c\u3001<br>\u4e88\u6e2c\u306e\u5024\u304c[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]<br>\u3068\u306a\u3063\u305f\u3068\u304d\u6b63\u89e3\u7387\u306f90%\u3068\u8a08\u7b97\u3055\u308c\u4e00\u898b\u3088\u3044\u30e2\u30c7\u30eb\u3068\u601d\u3048\u307e\u3059\u3002<br>\u3057\u304b\u3057\u30011\u500b\u306e\u8ca0\u4e8b\u4f8b\u3092\u4e88\u6e2c\u3067\u304d\u3066\u3044\u306a\u3044\u305f\u3081\u3053\u306e\u30e2\u30c7\u30eb\u306f\u610f\u5473\u304c\u306a\u3044\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<br>\u3053\u306e\u3088\u3046\u306b\u6b63\u4e8b\u4f8b\u3068\u8ca0\u4e8b\u4f8b\u304c\u4e0d\u5747\u8861\u306a\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3001\u6b63\u89e3\u7387\u3092\u4f7f\u3063\u3066\u8a55\u4fa1\u3059\u308b\u306e\u306f\u96e3\u3057\u3044\u305f\u3081\u3001\u6b63\u89e3\u7387\u4ee5\u5916\u306b\u3082\u9069\u5408\u7387\u3084\u518d\u73fe\u7387\u306a\u3069\u306e\u69d8\u3005\u306a\u6307\u6a19\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<h2 id=\"i-5\">\u9069\u5408\u7387\uff08Precision\uff09<\/h2>\n<p><strong>\u9069\u5408\u7387\u3068\u306f\u3001\u6b63\u4e8b\u4f8b\u3068\u4e88\u6e2c\u3057\u305f\u3082\u306e\u306e\u306a\u304b\u3067\u771f\u306e\u5024\u304c\u6b63\u4e8b\u4f8b\u306e\u5272\u5408\u3092\u8868\u3059\u6307\u6a19\u3067\u3059\u3002<\/strong><br>\uff08\u51fa\u529b\u3057\u305f\u7d50\u679c\u304c\u3069\u306e\u7a0b\u5ea6\u6b63\u89e3\u3057\u3066\u3044\u305f\u306e\u304b\u3092\u8868\u3059\u6307\u6a19\uff09<\/p>\n<p>\u4ee5\u4e0b\u306e\u5f0f\u3067\u6c42\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>$$<br>Precision = \\displaystyle\\frac{TP}{TP+FP}<br>$$<\/p>\n<p>\uff0a\u307e\u305f\u9069\u5408\u7387\u306f\u7cbe\u5ea6\u3068\u3082\u547c\u3070\u308c\u307e\u3059\u3002\uff08\u3053\u306e\u30c6\u30ad\u30b9\u30c8\u3067\u306f\u9069\u5408\u7387\u3092\u8868\u3059\u5834\u5408\u3001<strong>\u9069\u5408\u7387<\/strong>\u3068\u8868\u8a18\u3057\u307e\u3059\u3002\uff09<br><strong>\u9069\u5408\u7387\u306f\u3001precision_score\u3067\u6c42\u3081\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u3002<\/strong><\/p>\n<pre><code class=\"python:\">from sklearn.metrics import precision_score\nprint('Precision:', precision_score(y_test,y_pred)) # 0.75\n<\/code><\/pre>\n<h2 id=\"i-6\">\u518d\u73fe\u7387\uff08Recall\uff09<\/h2>\n<p><strong>\u518d\u73fe\u7387\u3068\u306f\u3001\u771f\u306e\u5024\u304c\u6b63\u4e8b\u4f8b\u306e\u3082\u306e\u306e\u306a\u304b\u3067\u6b63\u4e8b\u4f8b\u3068\u4e88\u6e2c\u3057\u305f\u5272\u5408\u3092\u8868\u3059\u6307\u6a19\u3067\u3059\u3002<\/strong><br>\u4ee5\u4e0b\u306e\u5f0f\u3067\u6c42\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>$$<br>Recall = \\displaystyle\\frac{TP}{TP + FN}<br>$$<\/p>\n<p><strong>\u518d\u73fe\u7387\u306f\u3001recall_score\u3067\u6c42\u3081\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u3002<\/strong><\/p>\n<pre><code class=\"python:\">from sklearn.metrics import recall_score\nprint('Recall:', recall_score(y_test,y_pred)) # 0.6\n<\/code><\/pre>\n<h2 id=\"i-7\">F\u5024\uff08F-measure\uff09<\/h2>\n<p><strong>\u9069\u5408\u7387\u3068\u518d\u73fe\u7387\u306f\u30c8\u30ec\u30fc\u30c9\u30aa\u30d5\u306e\u95a2\u4fc2\u306b\u3042\u308b\u306e\u3067\u30012\u3064\u306e\u6307\u6a19\u3092\u307e\u3068\u3081\u305f\u6307\u6a19\u3068\u3057\u3066F\u5024\u304c\u3042\u308a\u307e\u3059\u3002<\/strong><br>F\u5024\u306f\u3001\u9069\u5408\u7387\u3068\u518d\u73fe\u7387\u306e\u8abf\u548c\u5e73\u5747\u306b\u3088\u3063\u3066\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002<\/p>\n<p>$$<br>F \u2013 measure = \\displaystyle\\frac{2Precision * Recall}{Precision + Recall}<br>$$<\/p>\n<p><strong>F\u5024\u306f\u3001f1_score\u3067\u6c42\u3081\u308b\u3053\u3068\u304c\u51fa\u6765\u307e\u3059\u3002<\/strong><\/p>\n<pre><code class=\"python:\">from sklearn.metrics import f1_score\nprint('F1 score:', f1_score(y_test,y_pred)) # 0.67 \n<\/code><\/pre>\n<p>\u307e\u305f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u9069\u5408\u7387\u3001\u518d\u73fe\u7387\u3001F\u5024\u306fscikit-learn\u306eclassification_report\u306b\u3088\u3063\u3066\u307e\u3068\u3081\u3066\u8a08\u7b97\u3059\u308b\u4e8b\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n<pre><code class=\"python:\">from sklearn.metrics import classification_report\n\nprint(\"Classification report\")\nprint(classification_report(y_test, y_pred))\n<\/code><\/pre>\n<p>\u5b9f\u884c\u3059\u308b\u3068\u4ee5\u4e0b\u304c\u51fa\u529b\u3055\u308c\u3001\u8a08\u7b97\u304c\u3067\u304d\u3066\u3044\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002<\/p>\n<pre><code class=\"shell\">Classification report\nprecision    recall  f1-score   support\n0       0.67      0.80      0.73         5\n1       0.75      0.60      0.67         5\navg \/ total       0.71      0.70      0.70        10\n<\/code><\/pre>\n<p>F\u5024\u306f\u3001f1-score\u306eavg\/total\u306e\u90e8\u5206\u3067\u3059\u306e\u3067\u3053\u306e\u5834\u54080.70\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<h2 id=\"i-8\">\u307e\u3068\u3081<\/h2>\n<p>\u3053\u306e\u7ae0\u3067\u306f\u3001\u5206\u985e\u306b\u304a\u3051\u308b\u8a55\u4fa1\u6307\u6a19\u306b\u3064\u3044\u3066\u5b66\u3073\u307e\u3057\u305f\u3002<br>\u5206\u985e\u306e\u8a55\u4fa1\u6307\u6a19\u306b\u306f\u3001\u6b63\u89e3\u7387\u3001\u9069\u5408\u7387\u3001\u518d\u73fe\u7387\u3001F\u5024\u306a\u3069\u304c\u3042\u308a\u307e\u3059\u3002<br>\u4eca\u5f8c\u3053\u306e\u30c6\u30ad\u30b9\u30c8\u3067\u5b66\u3093\u3060\u3053\u3068\u3092\u6d3b\u304b\u3057\u3001\u30b7\u30b9\u30c6\u30e0\u306b\u7d44\u307f\u8fbc\u3080\u524d\u306b\u6a5f\u68b0\u5b66\u7fd2\u306e\u7d50\u679c\u3092\u8a55\u4fa1\u3057\u3066\u9802\u3051\u308c\u3070\u3068\u601d\u3044\u307e\u3059\u3002<\/p>\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/aiacademy.jp\/bootcamp\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"341\" src=\"https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi-1024x341.png\" alt=\"\" class=\"wp-image-691\" srcset=\"https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi-1024x341.png 1024w, https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi-300x100.png 300w, https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi-768x256.png 768w, https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi-940x313.png 940w, https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\"><\/a><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>\u672c\u8a18\u4e8b\u306f\u4ee5\u4e0b\u306e\u30c6\u30ad\u30b9\u30c8\u306b\u79fb\u884c\u3057\u3066\u304a\u308a\u307e\u3059\u3002 https:\/\/aiacademy.jp\/texts\/show\/?id=34&amp;context=subject-metrics \u76ee\u6b21 \u672c\u8a18\u4e8b\u306f\u4ee5\u4e0b\u306e\u30c6\u30ad\u30b9\u30c8\u306b\u79fb\u884c\u3057\u3066\u304a\u308a &#8230; <\/p>\n","protected":false},"author":1,"featured_media":484,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[130],"tags":[],"class_list":{"0":"post-258","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-130"},"_links":{"self":[{"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/posts\/258","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=258"}],"version-history":[{"count":8,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/posts\/258\/revisions"}],"predecessor-version":[{"id":4874,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/posts\/258\/revisions\/4874"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/media\/484"}],"wp:attachment":[{"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=258"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=258"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=258"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}