AI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30c1\u30e3\u30ec\u30f3\u30b8 \u2013 \u5b66\u751f\u30fb\u533b\u7642\u95a2\u4fc2\u8005\u306e\u305f\u3081\u306e\u30b9\u30c6\u30c3\u30d7\u30a2\u30c3\u30d7\u30ac\u30a4\u30c9<\/title>\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n <style>\n body { font-family: \"Yu Gothic\", \"Hiragino Kaku Gothic ProN\", Meiryo, sans-serif; background: #f6f6fa; margin: 0; padding: 0; }\n header { background: #3c6ea5; color: #fff; padding: 2rem 1rem; text-align: center; }\n main { max-width: 960px; margin: 2rem auto; background: #fff; padding: 2rem 1.5rem 4rem 1.5rem; border-radius: 12px; box-shadow: 0 2px 18px #a9b8c3; }\n section { margin-bottom: 2.5rem; }\n h1, h2, h3 { color: #2c4467; }\n h1 { font-size: 2.2rem; }\n h2 { font-size: 1.5rem; border-bottom: 2px solid #d4e0f4; padding-bottom: 0.3em; margin-top: 2rem; }\n h3 { font-size: 1.1rem; }\n ul, ol { margin-left: 2em; }\n code, pre { background: #eee; padding: 2px 6px; border-radius: 4px; }\n .playlist { background: #f3f9ff; padding: 1rem 1.5rem; border-left: 4px solid #8ac6d1; border-radius: 6px; margin-bottom: 1.5rem; }\n .tip { background: #f6ffd5; padding: 0.5rem 1rem; border-left: 4px solid #e9e76b; margin: 1rem 0; }\n .youtube-embed { width: 100%; max-width: 640px; height: 360px; border: none; display: block; margin: 1.2rem auto; }\n footer { text-align: center; color: #89a1bc; font-size: 0.9em; padding: 1.5rem 0 1rem 0; }\n <\/style>\n<\/head>\n<body>\n <header>\n \n <p>\u301c Python\u3067\u30d0\u30a4\u30a2\u30b9\u3092\u691c\u51fa\u3057\u3001\u6280\u8853\u306e\u9762\u767d\u3055\u3068\u793e\u4f1a\u7684\u610f\u7fa9\u3092\u4f53\u9a13\u3057\u3088\u3046 \u301c<\/p>\n <\/header>\n <main>\n <section>\n <h2>\u30a4\u30f3\u30c8\u30ed\u30c0\u30af\u30b7\u30e7\u30f3\uff1a\u306a\u305c\u4eca\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\uff1f<\/h2>\n <p>\n AI\uff08\u4eba\u5de5\u77e5\u80fd\uff09\u306f\u3001\u753b\u50cf\u8a3a\u65ad\u3084\u30c7\u30fc\u30bf\u89e3\u6790\u306e\u73fe\u5834\u3067\u65e5\u3005\u6d3b\u8e8d\u3057\u3066\u3044\u307e\u3059\u304c\u3001\u300c\u504f\u308a\uff08\u30d0\u30a4\u30a2\u30b9\uff09\u300d\u304c\u7121\u610f\u8b58\u306b\u5165\u308a\u8fbc\u3080\u3053\u3068\u306f\u907f\u3051\u3089\u308c\u307e\u305b\u3093\u3002\n <br>\n \u3053\u306e\u30da\u30fc\u30b8\u3067\u306f\u3001<strong>\u5b66\u751f\u3084\u533b\u7642\u95a2\u4fc2\u8005\u3001AI\u306b\u8208\u5473\u3092\u6301\u3064\u5168\u3066\u306e\u65b9<\/strong>\u306b\u5411\u3051\u3066\u3001\u300cAI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30c1\u30e3\u30ec\u30f3\u30b8\u300d\u30d7\u30ec\u30a4\u30ea\u30b9\u30c8\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\n <br>\n Python\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u4f8b\u3084\u3001\u5f79\u7acb\u3064YouTube\/\u8a18\u4e8b\u30ea\u30f3\u30af\u3001\u793e\u4f1a\u3084\u73fe\u5834\u3067\u3069\u3046\u751f\u304b\u305b\u308b\u304b\u306e\u30d2\u30f3\u30c8\u3082\u4ea4\u3048\u3066\u3001\u5b9f\u8df5\u7684\u306a\u201c\u30b9\u30c6\u30c3\u30d7\u30a2\u30c3\u30d7\u201d\u3092\u4f53\u9a13\u3067\u304d\u307e\u3059\uff01\n <\/p>\n <\/section>\n\n <section>\n <h2>\u30d7\u30ec\u30a4\u30ea\u30b9\u30c8\u306e\u69cb\u6210<\/h2>\n <ol>\n <li><a href=\"#playlist1\">\u30d0\u30a4\u30a2\u30b9\u3068\u306f\u4f55\u304b\uff1f\u301c\u533b\u7642\u30fb\u65e5\u5e38\u30fbAI\u306e\u8996\u70b9\u3067\u301c<\/a><\/li>\n <li><a href=\"#playlist2\">\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3068\u30d0\u30a4\u30a2\u30b9 \u301c\u5b9f\u4f8b\u3068\u691c\u51fa\u65b9\u6cd5\u301c<\/a><\/li>\n <li><a href=\"#playlist3\">Python\u3067\u753b\u50cfAI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u306e\u5b9f\u8df5<\/a><\/li>\n <li><a href=\"#playlist4\">\u767a\u5c55\u7de8\uff1a\u591a\u69d8\u6027\u3068\u516c\u5e73\u6027\u3092\u5b88\u308b\u5de5\u592b<\/a><\/li>\n <li><a href=\"#playlist5\">\u793e\u4f1a\u306b\u6d3b\u304b\u3059\u30d2\u30f3\u30c8\u30fb\u30d6\u30ed\u30b0\u30cd\u30bf\u96c6<\/a><\/li>\n <\/ol>\n <\/section>\n\n <section id=\"playlist1\" class=\"playlist\">\n <h2>1. \u30d0\u30a4\u30a2\u30b9\u3068\u306f\u4f55\u304b\uff1f\u301c\u533b\u7642\u30fb\u65e5\u5e38\u30fbAI\u306e\u8996\u70b9\u3067\u301c<\/h2>\n <ul>\n <li>\n <strong>\u57fa\u790e\u77e5\u8b58\uff1a<\/strong>\n <ul>\n <li>\u30d0\u30a4\u30a2\u30b9\uff08\u504f\u308a\uff09\uff1d\u7279\u5b9a\u306e\u6761\u4ef6\u3084\u80cc\u666f\u306b\u3088\u3063\u3066\u3001\u5224\u65ad\u3084\u7d50\u679c\u304c\u4e00\u65b9\u5411\u306b\u5bc4\u308b\u3053\u3068\u3002<\/li>\n <li>\u533b\u7642\u73fe\u5834\u3067\u306f\u3001AI\u304c\u300c\u3042\u308b\u4eba\u7a2e\u3084\u6027\u5225\u3001\u5e74\u9f62\u5c64\u3060\u3051\u300d\u306b\u9ad8\u7cbe\u5ea6\u3060\u3063\u305f\u3001\u306a\u3069\u306e\u554f\u984c\u304c\u5b9f\u969b\u306b\u767a\u751f\u3002<\/li>\n <\/ul>\n <\/li>\n <\/ul>\n <div class=\"tip\">\n <strong>\u30dd\u30a4\u30f3\u30c8\uff1a<\/strong>\u533b\u7642AI\u3060\u3051\u3067\u306a\u304f\u3001\u5c31\u8077\u9762\u63a5\u30fb\u30cb\u30e5\u30fc\u30b9\u63a8\u85a6\u30fb\u97f3\u697d\u30b5\u30d6\u30b9\u30af\u7b49\u3082\u30d0\u30a4\u30a2\u30b9\u554f\u984c\u3068\u7121\u95a2\u4fc2\u3067\u306f\u3042\u308a\u307e\u305b\u3093\uff01\n <\/div>\n <\/section>\n\n <section id=\"playlist2\" class=\"playlist\">\n <h2>2. \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3068\u30d0\u30a4\u30a2\u30b9 \u301c\u5b9f\u4f8b\u3068\u691c\u51fa\u65b9\u6cd5\u301c<\/h2>\n <ul>\n <li>\n <strong>\u5b9f\u4f8b\uff1a<\/strong>\n <ul>\n <li>\u533b\u7642\u753b\u50cf\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u300c\u7279\u5b9a\u306e\u75c5\u9662\u300d\u300c\u9650\u3089\u308c\u305f\u6a5f\u7a2e\u300d\u306e\u3082\u306e\u3060\u3051\u2192 \u4ed6\u5730\u57df\u3084\u65b0\u578b\u88c5\u7f6e\u3067\u306f\u7cbe\u5ea6\u304c\u843d\u3061\u308b<\/li>\n <li>\u82f1\u8a9e\u30cb\u30e5\u30fc\u30b9\u3067\u5b66\u3076\uff1a<a href=\"https:\/\/physicsworld.com\/a\/ai-algorithms-in-radiology-how-to-identify-and-prevent-inadvertent-bias\/\" target=\"_blank\">AI algorithms in radiology: how to identify and prevent inadvertent bias<\/a><\/li>\n <\/ul>\n <\/li>\n <li>\n <strong>\u691c\u51fa\u65b9\u6cd5\u5165\u9580\uff1a<\/strong>\n <ul>\n <li>\u5404\u30b0\u30eb\u30fc\u30d7\uff08\u5e74\u9f62\/\u6027\u5225\/\u75c5\u9662\u306a\u3069\uff09\u3054\u3068\u306b\u6b63\u7b54\u7387\u3092\u6bd4\u8f03<\/li>\n <li>\u6df7\u540c\u884c\u5217\uff08confusion matrix\uff09\u3092\u53ef\u8996\u5316\u3059\u308b<\/li>\n <li>\u300c\u30b7\u30e3\u30c3\u30d5\u30eb\u30c6\u30b9\u30c8\u300d\u3067\u30e9\u30f3\u30c0\u30e0\u6027\u3092\u30c1\u30a7\u30c3\u30af<\/li>\n <\/ul>\n <\/li>\n\n <\/ul>\n <\/section>\n\n <section id=\"playlist3\" class=\"playlist\">\n <h2>3. Python\u3067\u753b\u50cfAI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u306e\u5b9f\u8df5<\/h2>\n <ul>\n <li>\n <strong>\u5b9f\u8df5\u4f8b1\uff1a<\/strong> \n \u300cA\u75c5\u9662\u300d\u3068\u300cB\u75c5\u9662\u300d\u305d\u308c\u305e\u308c\u306e\u753b\u50cf\u304b\u3089AI\u8a3a\u65ad\u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u3092\u6bd4\u8f03\u3057\u3066\u307f\u3088\u3046\u3002\n <\/li>\n <li>\n <strong>\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\uff08\u7591\u4f3c\u30c7\u30fc\u30bf\uff09:<\/strong>\n <pre>\nimport pandas as pd\n\n# \u7591\u4f3c\u30c7\u30fc\u30bf: 2\u3064\u306e\u75c5\u9662\u306e\u5224\u5b9a\u6b63\u89e3\u7387\ndata = {\n \"hospital\": [\"A\"]*50 + [\"B\"]*50,\n \"correct\": [1]*40 + [0]*10 + [1]*30 + [0]*20\n}\ndf = pd.DataFrame(data)\n\n# \u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306e\u6b63\u7b54\u7387\nresult = df.groupby(\"hospital\")[\"correct\"].mean()\nprint(result)\n <\/pre>\n <\/li>\n <li>\n <strong>\u5b9f\u8df5\u4f8b2\uff1a<\/strong> \n \u6df7\u540c\u884c\u5217\u3067\u300c\u8aa4\u5224\u5b9a\u306e\u50be\u5411\u300d\u3092\u5206\u6790\u3002\n <pre>\nfrom sklearn.metrics import confusion_matrix\n# \u4f8b: \u6b63\u89e3\u30e9\u30d9\u30eb\u3068AI\u306e\u5224\u5b9a\u7d50\u679c\ny_true = [1,0,1,1,0,0,1,1,0,1]\ny_pred = [1,0,1,0,0,1,1,0,0,1]\nprint(confusion_matrix(y_true, y_pred))\n <\/pre>\n <\/li>\n \n <\/ul>\n <div class=\"tip\">\n <strong>\u88dc\u8db3\uff1a<\/strong>\u73fe\u5b9f\u306e\u30c7\u30fc\u30bf\u306f\u500b\u4eba\u60c5\u5831\u306e\u89b3\u70b9\u3067\u4e00\u822c\u516c\u958b\u3055\u308c\u307e\u305b\u3093\u304c\u3001\u300cKaggle\u300d\u3084\u300cUCI Machine Learning Repository\u300d\u306b\u306f\u7df4\u7fd2\u7528\u30c7\u30fc\u30bf\u3082\u3042\u308a\u307e\u3059\u3002\n <\/div>\n <\/section>\n\n <section id=\"playlist4\" class=\"playlist\">\n <h2>4. \u767a\u5c55\u7de8\uff1a\u591a\u69d8\u6027\u3068\u516c\u5e73\u6027\u3092\u5b88\u308b\u5de5\u592b<\/h2>\n <ul>\n <li>\n <strong>\u30c6\u30af\u30cb\u30c3\u30af\u4f8b\uff1a<\/strong>\n <ul>\n <li>\u300c\u91cd\u307f\u4ed8\u304d\u640d\u5931\u95a2\u6570\u300d\u3067\u30de\u30a4\u30ce\u30ea\u30c6\u30a3\u30c7\u30fc\u30bf\u3092\u91cd\u8996<\/li>\n <li>\u30c7\u30fc\u30bf\u62e1\u5f35\uff08augmentation\uff09\u3067\u5c11\u6570\u6d3e\u753b\u50cf\u3092\u5897\u3084\u3059<\/li>\n <li>\u8907\u6570\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u5408\u6210\u3057\u3066\u591a\u69d8\u6027\u3092\u78ba\u4fdd<\/li>\n <\/ul>\n <\/li>\n <li>\n <strong>\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\uff08Keras\uff09:<\/strong>\n <pre>\nfrom tensorflow.keras.losses import CategoricalCrossentropy\n\n# \u30af\u30e9\u30b90: \u901a\u5e38\u3001\u30af\u30e9\u30b91: \u5c11\u6570\u6d3e\nloss = CategoricalCrossentropy()\nclass_weights = {0: 1.0, 1: 3.0}\nmodel.fit(X_train, y_train, epochs=10, class_weight=class_weights)\n <\/pre>\n <\/li>\n <li>\n <strong>\u89e3\u8aac\u8a18\u4e8b\uff1a<\/strong>\n <a href=\"https:\/\/www.blueprism.com\/japan\/resources\/blog\/bias-fairness-ai\/\" target=\"_blank\">\u6a5f\u68b0\u5b66\u7fd2\u306b\u304a\u3051\u308b\u516c\u5e73\u6027\u3068\u30d0\u30a4\u30a2\u30b9\uff08\u65e5\u672c\u8a9e\u8a18\u4e8b\uff09<\/a>\n <\/li>\n <\/ul>\n <\/section>\n\n <section id=\"playlist5\" class=\"playlist\">\n <h2>5. \u793e\u4f1a\u306b\u6d3b\u304b\u3059\u30d2\u30f3\u30c8\u30fb\u30cd\u30bf\u96c6<\/h2>\n <ul>\n <li>\u533b\u7642AI\u306e\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u4f53\u9a13\u3092\u300c\u60a3\u8005\u76ee\u7dda\u3067\u30ec\u30dd\u30fc\u30c8\u300d<\/li>\n <li>\u300c\u8a3a\u65adAI\u306e\u516c\u5e73\u6027\u5411\u4e0a\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u300d\u59cb\u52d5\u306e\u4f53\u9a13\u8a18<\/li>\n <li>\u5b66\u751f\u306b\u3088\u308b\u300c\u30c7\u30fc\u30bf\u62e1\u5f35\u30d0\u30c8\u30eb\u300d\u30ec\u30dd\u30fc\u30c8\uff08\u7cbe\u5ea6\u3068\u516c\u5e73\u6027\u306e\u4e21\u7acb\u306b\u6311\u6226\uff09<\/li>\n <li>\u73fe\u5834\u306e\u533b\u7642\u5f93\u4e8b\u8005\u304c\u5b9f\u8df5\u3059\u308b\u300c\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7\u300d\u6848\u5185<\/li>\n <li>\u8da3\u5473AI\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3068\u3057\u3066\u306e\u300c\u8eab\u306e\u56de\u308a\u306e\u30d0\u30a4\u30a2\u30b9\u300d\u767a\u898b\u65e5\u8a18<\/li>\n <\/ul>\n\n <\/section>\n\n <section>\n <h2>\u304a\u308f\u308a\u306b<\/h2>\n <p>\n \u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u306f\u3001<strong>\u300c\u793e\u4f1a\u8ab2\u984c \u00d7 \u6280\u8853\u63a2\u7a76 \u00d7 \u597d\u5947\u5fc3\u300d<\/strong>\u304c\u4ea4\u5dee\u3059\u308b\u9762\u767d\u3044\u30c6\u30fc\u30de\u3067\u3059\u3002\n <br>\n \u305c\u3072\u3001Python\u3084Jupyter Notebook\u3092\u4f7f\u3063\u3066<br>\n <b>\u300c\u81ea\u5206\u3060\u3051\u306e\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30c1\u30e3\u30ec\u30f3\u30b8\u300d<\/b>\u306b\u6311\u6226\u3057\u3001\u793e\u4f1a\u3084\u533b\u7642\u306e\u73fe\u5834\u3067\u6d3b\u304b\u3059\u30d2\u30f3\u30c8\u3092\u30d6\u30ed\u30b0\u306a\u3069\u3067\u767a\u4fe1\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\uff01\n <br>\n <\/p>\n <\/section>\n <\/main>\n <footer>\n \u00a9 2025 \u682a\u5f0f\u4f1a\u793e\u30d3\u30fc\u30fb\u30ca\u30ec\u30c3\u30b8\u30fb\u30c7\u30b6\u30a4\u30f3\u3000AI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30c1\u30e3\u30ec\u30f3\u30b8\uff5c\u8da3\u5473\u3068\u5b66\u3073\u306e\u305f\u3081\u306e\u5b9f\u8df5\u30ac\u30a4\u30c9\n <\/footer>\n<\/body>\n<\/html>\n\n","protected":false},"excerpt":{"rendered":"<p>AI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30c1\u30e3\u30ec\u30f3\u30b8 \u2013 \u5b66\u751f\u30fb\u533b\u7642\u95a2\u4fc2\u8005\u306e\u305f\u3081\u306e\u30b9\u30c6\u30c3\u30d7\u30a2\u30c3\u30d7\u30ac\u30a4\u30c9 \u301c Python\u3067\u30d0\u30a4\u30a2\u30b9\u3092\u691c\u51fa\u3057\u3001\u6280\u8853\u306e\u9762\u767d\u3055\u3068\u793e\u4f1a\u7684\u610f\u7fa9\u3092\u4f53\u9a13\u3057\u3088\u3046 \u301c \u30a4\u30f3\u30c8\u30ed\u30c0\u30af\u30b7\u30e7\u30f3\uff1a\u306a\u305c\u4eca\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\uff1f AI\uff08\u4eba\u5de5\u77e5\u80fd\uff09\u306f\u3001\u753b […]<\/p>\n","protected":false},"author":1,"featured_media":1386,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"saved_in_kubio":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"sns_share_botton_hide":"","vkExUnit_sns_title":"","_vk_print_noindex":"","sitemap_hide":"","vkExUnit_EyeCatch_disable":"","_veu_custom_css":"","veu_display_promotion_alert":"common","vkexunit_cta_each_option":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[5,6,54],"tags":[],"class_list":["post-1378","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-programing","category-54"],"aioseo_notices":[],"veu_head_title_object":{"title":"","add_site_title":""},"jetpack_featured_media_url":"https:\/\/beeknowledge.co.jp\/wp-content\/uploads\/2025\/06\/r5i0lyr5.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1378","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1378"}],"version-history":[{"count":7,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1378\/revisions"}],"predecessor-version":[{"id":1385,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1378\/revisions\/1385"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/media\/1386"}],"wp:attachment":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1378"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1378"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1378"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

$\"\"$ <\/div>\n\n\n\n \n AI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30c1\u30e3\u30ec\u30f3\u30b8 \u2013 \u5b66\u751f\u30fb\u533b\u7642\u95a2\u4fc2\u8005\u306e\u305f\u3081\u306e\u30b9\u30c6\u30c3\u30d7\u30a2\u30c3\u30d7\u30ac\u30a4\u30c9<\/title>\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n <style>\n body { font-family: \"Yu Gothic\", \"Hiragino Kaku Gothic ProN\", Meiryo, sans-serif; background: #f6f6fa; margin: 0; padding: 0; }\n header { background: #3c6ea5; color: #fff; padding: 2rem 1rem; text-align: center; }\n main { max-width: 960px; margin: 2rem auto; background: #fff; padding: 2rem 1.5rem 4rem 1.5rem; border-radius: 12px; box-shadow: 0 2px 18px #a9b8c3; }\n section { margin-bottom: 2.5rem; }\n h1, h2, h3 { color: #2c4467; }\n h1 { font-size: 2.2rem; }\n h2 { font-size: 1.5rem; border-bottom: 2px solid #d4e0f4; padding-bottom: 0.3em; margin-top: 2rem; }\n h3 { font-size: 1.1rem; }\n ul, ol { margin-left: 2em; }\n code, pre { background: #eee; padding: 2px 6px; border-radius: 4px; }\n .playlist { background: #f3f9ff; padding: 1rem 1.5rem; border-left: 4px solid #8ac6d1; border-radius: 6px; margin-bottom: 1.5rem; }\n .tip { background: #f6ffd5; padding: 0.5rem 1rem; border-left: 4px solid #e9e76b; margin: 1rem 0; }\n .youtube-embed { width: 100%; max-width: 640px; height: 360px; border: none; display: block; margin: 1.2rem auto; }\n footer { text-align: center; color: #89a1bc; font-size: 0.9em; padding: 1.5rem 0 1rem 0; }\n <\/style>\n<\/head>\n<body>\n <header>\n \n <p>\u301c Python\u3067\u30d0\u30a4\u30a2\u30b9\u3092\u691c\u51fa\u3057\u3001\u6280\u8853\u306e\u9762\u767d\u3055\u3068\u793e\u4f1a\u7684\u610f\u7fa9\u3092\u4f53\u9a13\u3057\u3088\u3046 \u301c<\/p>\n <\/header>\n <main>\n <section>\n <h2>\u30a4\u30f3\u30c8\u30ed\u30c0\u30af\u30b7\u30e7\u30f3\uff1a\u306a\u305c\u4eca\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\uff1f<\/h2>\n <p>\n AI\uff08\u4eba\u5de5\u77e5\u80fd\uff09\u306f\u3001\u753b\u50cf\u8a3a\u65ad\u3084\u30c7\u30fc\u30bf\u89e3\u6790\u306e\u73fe\u5834\u3067\u65e5\u3005\u6d3b\u8e8d\u3057\u3066\u3044\u307e\u3059\u304c\u3001\u300c\u504f\u308a\uff08\u30d0\u30a4\u30a2\u30b9\uff09\u300d\u304c\u7121\u610f\u8b58\u306b\u5165\u308a\u8fbc\u3080\u3053\u3068\u306f\u907f\u3051\u3089\u308c\u307e\u305b\u3093\u3002\n <br>\n \u3053\u306e\u30da\u30fc\u30b8\u3067\u306f\u3001<strong>\u5b66\u751f\u3084\u533b\u7642\u95a2\u4fc2\u8005\u3001AI\u306b\u8208\u5473\u3092\u6301\u3064\u5168\u3066\u306e\u65b9<\/strong>\u306b\u5411\u3051\u3066\u3001\u300cAI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30c1\u30e3\u30ec\u30f3\u30b8\u300d\u30d7\u30ec\u30a4\u30ea\u30b9\u30c8\u3092\u7d39\u4ecb\u3057\u307e\u3059\u3002\n <br>\n Python\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u4f8b\u3084\u3001\u5f79\u7acb\u3064YouTube\/\u8a18\u4e8b\u30ea\u30f3\u30af\u3001\u793e\u4f1a\u3084\u73fe\u5834\u3067\u3069\u3046\u751f\u304b\u305b\u308b\u304b\u306e\u30d2\u30f3\u30c8\u3082\u4ea4\u3048\u3066\u3001\u5b9f\u8df5\u7684\u306a\u201c\u30b9\u30c6\u30c3\u30d7\u30a2\u30c3\u30d7\u201d\u3092\u4f53\u9a13\u3067\u304d\u307e\u3059\uff01\n <\/p>\n <\/section>\n\n <section>\n <h2>\u30d7\u30ec\u30a4\u30ea\u30b9\u30c8\u306e\u69cb\u6210<\/h2>\n <ol>\n <li><a href=\"#playlist1\">\u30d0\u30a4\u30a2\u30b9\u3068\u306f\u4f55\u304b\uff1f\u301c\u533b\u7642\u30fb\u65e5\u5e38\u30fbAI\u306e\u8996\u70b9\u3067\u301c<\/a><\/li>\n <li><a href=\"#playlist2\">\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3068\u30d0\u30a4\u30a2\u30b9 \u301c\u5b9f\u4f8b\u3068\u691c\u51fa\u65b9\u6cd5\u301c<\/a><\/li>\n <li><a href=\"#playlist3\">Python\u3067\u753b\u50cfAI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u306e\u5b9f\u8df5<\/a><\/li>\n <li><a href=\"#playlist4\">\u767a\u5c55\u7de8\uff1a\u591a\u69d8\u6027\u3068\u516c\u5e73\u6027\u3092\u5b88\u308b\u5de5\u592b<\/a><\/li>\n <li><a href=\"#playlist5\">\u793e\u4f1a\u306b\u6d3b\u304b\u3059\u30d2\u30f3\u30c8\u30fb\u30d6\u30ed\u30b0\u30cd\u30bf\u96c6<\/a><\/li>\n <\/ol>\n <\/section>\n\n <section id=\"playlist1\" class=\"playlist\">\n <h2>1. \u30d0\u30a4\u30a2\u30b9\u3068\u306f\u4f55\u304b\uff1f\u301c\u533b\u7642\u30fb\u65e5\u5e38\u30fbAI\u306e\u8996\u70b9\u3067\u301c<\/h2>\n <ul>\n <li>\n <strong>\u57fa\u790e\u77e5\u8b58\uff1a<\/strong>\n <ul>\n <li>\u30d0\u30a4\u30a2\u30b9\uff08\u504f\u308a\uff09\uff1d\u7279\u5b9a\u306e\u6761\u4ef6\u3084\u80cc\u666f\u306b\u3088\u3063\u3066\u3001\u5224\u65ad\u3084\u7d50\u679c\u304c\u4e00\u65b9\u5411\u306b\u5bc4\u308b\u3053\u3068\u3002<\/li>\n <li>\u533b\u7642\u73fe\u5834\u3067\u306f\u3001AI\u304c\u300c\u3042\u308b\u4eba\u7a2e\u3084\u6027\u5225\u3001\u5e74\u9f62\u5c64\u3060\u3051\u300d\u306b\u9ad8\u7cbe\u5ea6\u3060\u3063\u305f\u3001\u306a\u3069\u306e\u554f\u984c\u304c\u5b9f\u969b\u306b\u767a\u751f\u3002<\/li>\n <\/ul>\n <\/li>\n <\/ul>\n <div class=\"tip\">\n <strong>\u30dd\u30a4\u30f3\u30c8\uff1a<\/strong>\u533b\u7642AI\u3060\u3051\u3067\u306a\u304f\u3001\u5c31\u8077\u9762\u63a5\u30fb\u30cb\u30e5\u30fc\u30b9\u63a8\u85a6\u30fb\u97f3\u697d\u30b5\u30d6\u30b9\u30af\u7b49\u3082\u30d0\u30a4\u30a2\u30b9\u554f\u984c\u3068\u7121\u95a2\u4fc2\u3067\u306f\u3042\u308a\u307e\u305b\u3093\uff01\n <\/div>\n <\/section>\n\n <section id=\"playlist2\" class=\"playlist\">\n <h2>2. \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3068\u30d0\u30a4\u30a2\u30b9 \u301c\u5b9f\u4f8b\u3068\u691c\u51fa\u65b9\u6cd5\u301c<\/h2>\n <ul>\n <li>\n <strong>\u5b9f\u4f8b\uff1a<\/strong>\n <ul>\n <li>\u533b\u7642\u753b\u50cf\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u300c\u7279\u5b9a\u306e\u75c5\u9662\u300d\u300c\u9650\u3089\u308c\u305f\u6a5f\u7a2e\u300d\u306e\u3082\u306e\u3060\u3051\u2192 \u4ed6\u5730\u57df\u3084\u65b0\u578b\u88c5\u7f6e\u3067\u306f\u7cbe\u5ea6\u304c\u843d\u3061\u308b<\/li>\n <li>\u82f1\u8a9e\u30cb\u30e5\u30fc\u30b9\u3067\u5b66\u3076\uff1a<a href=\"https:\/\/physicsworld.com\/a\/ai-algorithms-in-radiology-how-to-identify-and-prevent-inadvertent-bias\/\" target=\"_blank\">AI algorithms in radiology: how to identify and prevent inadvertent bias<\/a><\/li>\n <\/ul>\n <\/li>\n <li>\n <strong>\u691c\u51fa\u65b9\u6cd5\u5165\u9580\uff1a<\/strong>\n <ul>\n <li>\u5404\u30b0\u30eb\u30fc\u30d7\uff08\u5e74\u9f62\/\u6027\u5225\/\u75c5\u9662\u306a\u3069\uff09\u3054\u3068\u306b\u6b63\u7b54\u7387\u3092\u6bd4\u8f03<\/li>\n <li>\u6df7\u540c\u884c\u5217\uff08confusion matrix\uff09\u3092\u53ef\u8996\u5316\u3059\u308b<\/li>\n <li>\u300c\u30b7\u30e3\u30c3\u30d5\u30eb\u30c6\u30b9\u30c8\u300d\u3067\u30e9\u30f3\u30c0\u30e0\u6027\u3092\u30c1\u30a7\u30c3\u30af<\/li>\n <\/ul>\n <\/li>\n\n <\/ul>\n <\/section>\n\n <section id=\"playlist3\" class=\"playlist\">\n <h2>3. Python\u3067\u753b\u50cfAI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u306e\u5b9f\u8df5<\/h2>\n <ul>\n <li>\n <strong>\u5b9f\u8df5\u4f8b1\uff1a<\/strong> \n \u300cA\u75c5\u9662\u300d\u3068\u300cB\u75c5\u9662\u300d\u305d\u308c\u305e\u308c\u306e\u753b\u50cf\u304b\u3089AI\u8a3a\u65ad\u30e2\u30c7\u30eb\u306e\u7cbe\u5ea6\u3092\u6bd4\u8f03\u3057\u3066\u307f\u3088\u3046\u3002\n <\/li>\n <li>\n <strong>\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\uff08\u7591\u4f3c\u30c7\u30fc\u30bf\uff09:<\/strong>\n <pre>\nimport pandas as pd\n\n# \u7591\u4f3c\u30c7\u30fc\u30bf: 2\u3064\u306e\u75c5\u9662\u306e\u5224\u5b9a\u6b63\u89e3\u7387\ndata = {\n \"hospital\": [\"A\"]*50 + [\"B\"]*50,\n \"correct\": [1]*40 + [0]*10 + [1]*30 + [0]*20\n}\ndf = pd.DataFrame(data)\n\n# \u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306e\u6b63\u7b54\u7387\nresult = df.groupby(\"hospital\")[\"correct\"].mean()\nprint(result)\n <\/pre>\n <\/li>\n <li>\n <strong>\u5b9f\u8df5\u4f8b2\uff1a<\/strong> \n \u6df7\u540c\u884c\u5217\u3067\u300c\u8aa4\u5224\u5b9a\u306e\u50be\u5411\u300d\u3092\u5206\u6790\u3002\n <pre>\nfrom sklearn.metrics import confusion_matrix\n# \u4f8b: \u6b63\u89e3\u30e9\u30d9\u30eb\u3068AI\u306e\u5224\u5b9a\u7d50\u679c\ny_true = [1,0,1,1,0,0,1,1,0,1]\ny_pred = [1,0,1,0,0,1,1,0,0,1]\nprint(confusion_matrix(y_true, y_pred))\n <\/pre>\n <\/li>\n \n <\/ul>\n <div class=\"tip\">\n <strong>\u88dc\u8db3\uff1a<\/strong>\u73fe\u5b9f\u306e\u30c7\u30fc\u30bf\u306f\u500b\u4eba\u60c5\u5831\u306e\u89b3\u70b9\u3067\u4e00\u822c\u516c\u958b\u3055\u308c\u307e\u305b\u3093\u304c\u3001\u300cKaggle\u300d\u3084\u300cUCI Machine Learning Repository\u300d\u306b\u306f\u7df4\u7fd2\u7528\u30c7\u30fc\u30bf\u3082\u3042\u308a\u307e\u3059\u3002\n <\/div>\n <\/section>\n\n <section id=\"playlist4\" class=\"playlist\">\n <h2>4. \u767a\u5c55\u7de8\uff1a\u591a\u69d8\u6027\u3068\u516c\u5e73\u6027\u3092\u5b88\u308b\u5de5\u592b<\/h2>\n <ul>\n <li>\n <strong>\u30c6\u30af\u30cb\u30c3\u30af\u4f8b\uff1a<\/strong>\n <ul>\n <li>\u300c\u91cd\u307f\u4ed8\u304d\u640d\u5931\u95a2\u6570\u300d\u3067\u30de\u30a4\u30ce\u30ea\u30c6\u30a3\u30c7\u30fc\u30bf\u3092\u91cd\u8996<\/li>\n <li>\u30c7\u30fc\u30bf\u62e1\u5f35\uff08augmentation\uff09\u3067\u5c11\u6570\u6d3e\u753b\u50cf\u3092\u5897\u3084\u3059<\/li>\n <li>\u8907\u6570\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u5408\u6210\u3057\u3066\u591a\u69d8\u6027\u3092\u78ba\u4fdd<\/li>\n <\/ul>\n <\/li>\n <li>\n <strong>\u30b5\u30f3\u30d7\u30eb\u30b3\u30fc\u30c9\uff08Keras\uff09:<\/strong>\n <pre>\nfrom tensorflow.keras.losses import CategoricalCrossentropy\n\n# \u30af\u30e9\u30b90: \u901a\u5e38\u3001\u30af\u30e9\u30b91: \u5c11\u6570\u6d3e\nloss = CategoricalCrossentropy()\nclass_weights = {0: 1.0, 1: 3.0}\nmodel.fit(X_train, y_train, epochs=10, class_weight=class_weights)\n <\/pre>\n <\/li>\n <li>\n <strong>\u89e3\u8aac\u8a18\u4e8b\uff1a<\/strong>\n <a href=\"https:\/\/www.blueprism.com\/japan\/resources\/blog\/bias-fairness-ai\/\" target=\"_blank\">\u6a5f\u68b0\u5b66\u7fd2\u306b\u304a\u3051\u308b\u516c\u5e73\u6027\u3068\u30d0\u30a4\u30a2\u30b9\uff08\u65e5\u672c\u8a9e\u8a18\u4e8b\uff09<\/a>\n <\/li>\n <\/ul>\n <\/section>\n\n <section id=\"playlist5\" class=\"playlist\">\n <h2>5. \u793e\u4f1a\u306b\u6d3b\u304b\u3059\u30d2\u30f3\u30c8\u30fb\u30cd\u30bf\u96c6<\/h2>\n <ul>\n <li>\u533b\u7642AI\u306e\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u4f53\u9a13\u3092\u300c\u60a3\u8005\u76ee\u7dda\u3067\u30ec\u30dd\u30fc\u30c8\u300d<\/li>\n <li>\u300c\u8a3a\u65adAI\u306e\u516c\u5e73\u6027\u5411\u4e0a\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u300d\u59cb\u52d5\u306e\u4f53\u9a13\u8a18<\/li>\n <li>\u5b66\u751f\u306b\u3088\u308b\u300c\u30c7\u30fc\u30bf\u62e1\u5f35\u30d0\u30c8\u30eb\u300d\u30ec\u30dd\u30fc\u30c8\uff08\u7cbe\u5ea6\u3068\u516c\u5e73\u6027\u306e\u4e21\u7acb\u306b\u6311\u6226\uff09<\/li>\n <li>\u73fe\u5834\u306e\u533b\u7642\u5f93\u4e8b\u8005\u304c\u5b9f\u8df5\u3059\u308b\u300c\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7\u300d\u6848\u5185<\/li>\n <li>\u8da3\u5473AI\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3068\u3057\u3066\u306e\u300c\u8eab\u306e\u56de\u308a\u306e\u30d0\u30a4\u30a2\u30b9\u300d\u767a\u898b\u65e5\u8a18<\/li>\n <\/ul>\n\n <\/section>\n\n <section>\n <h2>\u304a\u308f\u308a\u306b<\/h2>\n <p>\n \u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u306f\u3001<strong>\u300c\u793e\u4f1a\u8ab2\u984c \u00d7 \u6280\u8853\u63a2\u7a76 \u00d7 \u597d\u5947\u5fc3\u300d<\/strong>\u304c\u4ea4\u5dee\u3059\u308b\u9762\u767d\u3044\u30c6\u30fc\u30de\u3067\u3059\u3002\n <br>\n \u305c\u3072\u3001Python\u3084Jupyter Notebook\u3092\u4f7f\u3063\u3066<br>\n <b>\u300c\u81ea\u5206\u3060\u3051\u306e\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30c1\u30e3\u30ec\u30f3\u30b8\u300d<\/b>\u306b\u6311\u6226\u3057\u3001\u793e\u4f1a\u3084\u533b\u7642\u306e\u73fe\u5834\u3067\u6d3b\u304b\u3059\u30d2\u30f3\u30c8\u3092\u30d6\u30ed\u30b0\u306a\u3069\u3067\u767a\u4fe1\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\uff01\n <br>\n <\/p>\n <\/section>\n <\/main>\n <footer>\n \u00a9 2025 \u682a\u5f0f\u4f1a\u793e\u30d3\u30fc\u30fb\u30ca\u30ec\u30c3\u30b8\u30fb\u30c7\u30b6\u30a4\u30f3\u3000AI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30c1\u30e3\u30ec\u30f3\u30b8\uff5c\u8da3\u5473\u3068\u5b66\u3073\u306e\u305f\u3081\u306e\u5b9f\u8df5\u30ac\u30a4\u30c9\n <\/footer>\n<\/body>\n<\/html>\n\n","protected":false},"excerpt":{"rendered":"<p>AI\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\u30c1\u30e3\u30ec\u30f3\u30b8 \u2013 \u5b66\u751f\u30fb\u533b\u7642\u95a2\u4fc2\u8005\u306e\u305f\u3081\u306e\u30b9\u30c6\u30c3\u30d7\u30a2\u30c3\u30d7\u30ac\u30a4\u30c9 \u301c Python\u3067\u30d0\u30a4\u30a2\u30b9\u3092\u691c\u51fa\u3057\u3001\u6280\u8853\u306e\u9762\u767d\u3055\u3068\u793e\u4f1a\u7684\u610f\u7fa9\u3092\u4f53\u9a13\u3057\u3088\u3046 \u301c \u30a4\u30f3\u30c8\u30ed\u30c0\u30af\u30b7\u30e7\u30f3\uff1a\u306a\u305c\u4eca\u30d0\u30a4\u30a2\u30b9\u691c\u51fa\uff1f AI\uff08\u4eba\u5de5\u77e5\u80fd\uff09\u306f\u3001\u753b […]<\/p>\n","protected":false},"author":1,"featured_media":1386,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"saved_in_kubio":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"sns_share_botton_hide":"","vkExUnit_sns_title":"","_vk_print_noindex":"","sitemap_hide":"","vkExUnit_EyeCatch_disable":"","_veu_custom_css":"","veu_display_promotion_alert":"common","vkexunit_cta_each_option":"","_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[5,6,54],"tags":[],"class_list":["post-1378","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-programing","category-54"],"aioseo_notices":[],"veu_head_title_object":{"title":"","add_site_title":""},"jetpack_featured_media_url":"https:\/\/beeknowledge.co.jp\/wp-content\/uploads\/2025\/06\/r5i0lyr5.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1378","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1378"}],"version-history":[{"count":7,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1378\/revisions"}],"predecessor-version":[{"id":1385,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/posts\/1378\/revisions\/1385"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=\/wp\/v2\/media\/1386"}],"wp:attachment":[{"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1378"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1378"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/beeknowledge.co.jp\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1378"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}