{"id":3116,"date":"2023-07-19T03:05:15","date_gmt":"2023-07-19T03:05:15","guid":{"rendered":"https:\/\/statorials.org\/ja\/sklearn%e5%88%86%e9%a1%9e%e3%83%ac%e3%83%9b%e3%82%9a%e3%83%bc%e3%83%88\/"},"modified":"2023-07-19T03:05:15","modified_gmt":"2023-07-19T03:05:15","slug":"sklearn%e5%88%86%e9%a1%9e%e3%83%ac%e3%83%9b%e3%82%9a%e3%83%bc%e3%83%88","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/sklearn%e5%88%86%e9%a1%9e%e3%83%ac%e3%83%9b%e3%82%9a%e3%83%bc%e3%83%88\/","title":{"rendered":"Sklearn \u3067\u5206\u985e\u30ec\u30dd\u30fc\u30c8\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5 (\u4f8b\u3042\u308a)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u6a5f\u68b0\u5b66\u7fd2\u3067<a href=\"https:\/\/statorials.org\/ja\/\u56de\u5e30\u3068\u5206\u985e\/\" target=\"_blank\" rel=\"noopener\">\u5206\u985e\u30e2\u30c7\u30eb<\/a>\u3092\u4f7f\u7528\u3059\u308b\u5834\u5408\u3001\u6b21\u306e 3 \u3064\u306e\u4e00\u822c\u7684\u306a\u6307\u6a19\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e\u54c1\u8cea\u3092\u8a55\u4fa1\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \u7cbe\u5ea6<\/strong>: \u967d\u6027\u4e88\u6e2c\u306e\u5408\u8a08\u3068\u6bd4\u8f03\u3057\u305f\u3001\u6b63\u3057\u3044\u967d\u6027\u4e88\u6e2c\u306e\u5272\u5408\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. Recall<\/strong> : \u5b9f\u969b\u306e\u967d\u6027\u4e88\u6e2c\u306e\u5408\u8a08\u3068\u6bd4\u8f03\u3057\u305f\u3001\u6b63\u3057\u3044\u967d\u6027\u4e88\u6e2c\u306e\u5272\u5408\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>3. F1 \u30b9\u30b3\u30a2<\/strong>: \u7cbe\u5ea6\u3068\u518d\u73fe\u7387\u306e\u52a0\u91cd\u8abf\u548c\u5e73\u5747\u3002\u30e2\u30c7\u30eb\u304c 1 \u306b\u8fd1\u3065\u304f\u307b\u3069\u3001\u30e2\u30c7\u30eb\u306f\u512a\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">F1 \u30b9\u30b3\u30a2: 2* (\u7cbe\u5ea6 * \u518d\u73fe\u7387) \/ (\u7cbe\u5ea6 + \u518d\u73fe\u7387)<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u3053\u308c\u3089 3 \u3064\u306e\u6307\u6a19\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u7279\u5b9a\u306e\u5206\u985e\u30e2\u30c7\u30eb\u304c\u7279\u5b9a\u306e<a href=\"https:\/\/statorials.org\/ja\/\u5909\u6570\u306e\u8aac\u660e\u5fdc\u7b54\/\" target=\"_blank\" rel=\"noopener\">\u5fdc\u7b54\u5909\u6570\u306e<\/a>\u7d50\u679c\u3092\u3069\u306e\u7a0b\u5ea6\u4e88\u6e2c\u3067\u304d\u308b\u304b\u3092\u7406\u89e3\u3067\u304d\u307e\u3059\u3002<\/span><\/span><\/p>\n<p><span style=\"color: #000000;\">\u5e78\u3044\u306a\u3053\u3068\u306b\u3001Python \u3067\u5206\u985e\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b\u5834\u5408\u3001 <strong>sklearn<\/strong>\u30e9\u30a4\u30d6\u30e9\u30ea\u306e<strong>classification_report()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3053\u308c\u3089 3 \u3064\u306e\u30e1\u30c8\u30ea\u30af\u30b9\u3092\u751f\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b: sklearn \u3067\u5206\u985e\u30ec\u30dd\u30fc\u30c8\u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001\u30dd\u30a4\u30f3\u30c8\u3068\u30a2\u30b7\u30b9\u30c8\u3092\u4f7f\u7528\u3059\u308b\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u3066\u30011,000 \u4eba\u306e\u3055\u307e\u3056\u307e\u306a\u5927\u5b66\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb\u9078\u624b\u304c NBA \u306b\u30c9\u30e9\u30d5\u30c8\u3055\u308c\u308b\u304b\u3069\u3046\u304b\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001Python \u3067\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306b\u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LogisticRegression\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">metrics<\/span> <span style=\"color: #008000;\">import<\/span> classification_report<\/strong>\n<\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u30011,000 \u4eba\u306e\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb\u9078\u624b\u306e\u60c5\u5831\u3092\u542b\u3080\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">points<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (30, size=1000),\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (12, size=1000),\n                   ' <span style=\"color: #ff0000;\">drafted<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (2, size=1000)})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n\tpoints assists drafted\n0 5 1 1\n1 11 8 0\n2 12 4 1\n3 8 7 0\n4 9 0 0\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u6ce8<\/strong>: \u5024<strong>0<\/strong>\u306f\u30d7\u30ec\u30fc\u30e4\u30fc\u304c\u30c9\u30e9\u30d5\u30c8\u5916\u3055\u308c\u305f\u3053\u3068\u3092\u793a\u3057\u3001\u5024<strong>1<\/strong>\u306f\u30d7\u30ec\u30fc\u30e4\u30fc\u304c\u30c9\u30e9\u30d5\u30c8\u3055\u308c\u305f\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u30c7\u30fc\u30bf\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u5206\u5272\u3057\u3001\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define the predictor variables and the response variable\n<\/span>X = df[[' <span style=\"color: #ff0000;\">points<\/span> ', ' <span style=\"color: #ff0000;\">assists<\/span> ']]\ny = df[' <span style=\"color: #ff0000;\">drafted<\/span> ']\n\n<span style=\"color: #008080;\">#split the dataset into training (70%) and testing (30%) sets\n<\/span>X_train,X_test,y_train,y_test = <span style=\"color: #3366ff;\">train_test_split<\/span> (X,y,test_size=0.3,random_state=0)  \n\n<\/strong><strong><span style=\"color: #008080;\">#instantiate the model\n<\/span>logistic_regression = LogisticRegression()\n\n<span style=\"color: #008080;\">#fit the model using the training data\n<\/span>logistic_regression. <span style=\"color: #3366ff;\">fit<\/span> (X_train,y_train)\n\n<span style=\"color: #008080;\">#use model to make predictions on test data\n<\/span>y_pred = logistic_regression. <span style=\"color: #3366ff;\">predict<\/span> (X_test)<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6700\u5f8c\u306b\u3001 <strong>classification_report()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u306e\u5206\u985e\u30e1\u30c8\u30ea\u30c3\u30af\u3092\u51fa\u529b\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#print classification report for model\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">print<\/span> (classification_report(y_test, y_pred))\n\n              precision recall f1-score support\n\n           0 0.51 0.58 0.54 160\n           1 0.43 0.36 0.40 140\n\n    accuracy 0.48 300\n   macro avg 0.47 0.47 0.47 300\nweighted avg 0.47 0.48 0.47 300\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u8aac\u660e<\/strong>: \u30e2\u30c7\u30eb\u304c\u30c9\u30e9\u30d5\u30c8\u3055\u308c\u308b\u3068\u4e88\u6e2c\u3057\u305f\u5168\u9078\u624b\u306e\u3046\u3061\u3001\u5b9f\u969b\u306b\u6307\u540d\u3055\u308c\u305f\u306e\u306f<strong>43%<\/strong>\u3060\u3051\u3067\u3057\u305f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>: \u5b9f\u969b\u306b\u30c9\u30e9\u30d5\u30c8\u3055\u308c\u305f\u3059\u3079\u3066\u306e\u9078\u624b\u306e\u3046\u3061\u3001\u30e2\u30c7\u30eb\u304c\u3053\u306e\u7d50\u679c\u3092\u6b63\u3057\u304f\u4e88\u6e2c\u3057\u305f\u306e\u306f\u305d\u306e\u3046\u3061\u306e<strong>36%<\/strong>\u3060\u3051\u3067\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>F1 \u30b9\u30b3\u30a2<\/strong>: \u3053\u306e\u5024\u306f\u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">F1 \u30b9\u30b3\u30a2: 2* (\u7cbe\u5ea6 * \u518d\u73fe\u7387) \/ (\u7cbe\u5ea6 + \u518d\u73fe\u7387)<\/span><\/li>\n<li> <span style=\"color: #000000;\">F1 \u30b9\u30b3\u30a2: 2*(.43*.36)\/(.43+.36)<\/span><\/li>\n<li> <span style=\"color: #000000;\">F1 \u8a55\u4fa1: <strong>0.40<\/strong> \u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u306e\u5024\u306f 1 \u306b\u3042\u307e\u308a\u8fd1\u304f\u306a\u3044\u305f\u3081\u3001\u3053\u306e\u30e2\u30c7\u30eb\u3067\u306f\u9078\u624b\u304c\u30c9\u30e9\u30d5\u30c8\u3055\u308c\u308b\u304b\u3069\u3046\u304b\u306e\u4e88\u6e2c\u304c\u4e0d\u5341\u5206\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u30b5\u30dd\u30fc\u30c8<\/strong>: \u3053\u308c\u3089\u306e\u5024\u306f\u3001\u30c6\u30b9\u30c8 \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u5404\u30af\u30e9\u30b9\u306b\u6240\u5c5e\u3059\u308b\u30d7\u30ec\u30fc\u30e4\u30fc\u306e\u6570\u3092\u5358\u7d14\u306b\u793a\u3057\u307e\u3059\u3002\u30c6\u30b9\u30c8 \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u9078\u624b\u306e\u3046\u3061\u3001 <strong>160 \u4eba<\/strong>\u304c\u30c9\u30e9\u30d5\u30c8\u5916\u3001 <strong>140 \u4eba<\/strong>\u304c\u30c9\u30e9\u30d5\u30c8\u5916\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8<\/strong>\uff1a <strong>classification_report()<\/strong>\u95a2\u6570\u306e\u5b8c\u5168\u306a\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8\u306f<a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.classification_report.html\" target=\"_blank\" rel=\"noopener\">\u3053\u3053\u3067<\/a>\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u306e\u5206\u985e\u30e2\u30c7\u30eb\u306e\u4f7f\u7528\u306b\u95a2\u3059\u308b\u8ffd\u52a0\u60c5\u5831\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ja\/\u30ed\u30b7\u3099\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30python\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/python\u884c\u5217\u306e\u6df7\u4e71\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u6df7\u540c\u884c\u5217\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u30cf\u3099\u30e9\u30f3\u30b9\u306e\u3068\u308c\u305f\u7cbe\u5ea6\u306e-python-sklearn\/\">Python \u3067\u30d0\u30e9\u30f3\u30b9\u306e\u3068\u308c\u305f\u7cbe\u5ea6\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6a5f\u68b0\u5b66\u7fd2\u3067\u5206\u985e\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3059\u308b\u5834\u5408\u3001\u6b21\u306e 3 \u3064\u306e\u4e00\u822c\u7684\u306a\u6307\u6a19\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e\u54c1\u8cea\u3092\u8a55\u4fa1\u3057\u307e\u3059\u3002 1. \u7cbe\u5ea6: \u967d\u6027\u4e88\u6e2c\u306e\u5408\u8a08\u3068\u6bd4\u8f03\u3057\u305f\u3001\u6b63\u3057\u3044\u967d\u6027\u4e88\u6e2c\u306e\u5272\u5408\u3002 2. Recall : \u5b9f\u969b\u306e\u967d\u6027\u4e88\u6e2c\u306e\u5408\u8a08\u3068\u6bd4\u8f03\u3057\u305f\u3001 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-3116","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>sklearn \u3067\u5206\u985e\u30ec\u30dd\u30fc\u30c8\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - Statology<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u4f8b\u3092\u793a\u3057\u3066 Python \u3067\u306e criteria_report() \u95a2\u6570\u306e\u4f7f\u7528\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ja\/sklearn\u5206\u985e\u30ec\u30db\u309a\u30fc\u30c8\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"sklearn \u3067\u5206\u985e\u30ec\u30dd\u30fc\u30c8\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d) - 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