{"id":2168,"date":"2023-07-23T09:57:36","date_gmt":"2023-07-23T09:57:36","guid":{"rendered":"https:\/\/statorials.org\/ko\/python-%e1%84%80%e1%85%a9%e1%86%a8%e1%84%89%e1%85%a5%e1%86%ab-%e1%84%8c%e1%85%a5%e1%86%bc%e1%84%86%e1%85%b5%e1%86%af-%e1%84%8f%e1%85%a9%e1%86%af%e1%84%87%e1%85%a2%e1%86%a8\/"},"modified":"2023-07-23T09:57:36","modified_gmt":"2023-07-23T09:57:36","slug":"python-%e1%84%80%e1%85%a9%e1%86%a8%e1%84%89%e1%85%a5%e1%86%ab-%e1%84%8c%e1%85%a5%e1%86%bc%e1%84%86%e1%85%b5%e1%86%af-%e1%84%8f%e1%85%a9%e1%86%af%e1%84%87%e1%85%a2%e1%86%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/python-%e1%84%80%e1%85%a9%e1%86%a8%e1%84%89%e1%85%a5%e1%86%ab-%e1%84%8c%e1%85%a5%e1%86%bc%e1%84%86%e1%85%b5%e1%86%af-%e1%84%8f%e1%85%a9%e1%86%af%e1%84%87%e1%85%a2%e1%86%a8\/","title":{"rendered":"Python\uc5d0\uc11c \uc815\ubc00 \ub9ac\ucf5c \uace1\uc120\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\uae30\uacc4 \ud559\uc2b5\uc5d0\uc11c <a href=\"https:\/\/statorials.org\/ko\/\u1112\u116c\u1100\u1171-\u1103\u1162-\u1107\u116e\u11ab\u1105\u1172\/\" target=\"_blank\" rel=\"noopener\">\ubd84\ub958 \ubaa8\ub378\uc744<\/a> \uc0ac\uc6a9\ud560 \ub54c \ubaa8\ub378 \ud488\uc9c8\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \uc790\uc8fc \uc0ac\uc6a9\ud558\ub294 \ub450 \uac00\uc9c0 \uc9c0\ud45c\ub294 \uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\uc728\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\uc815\ud655\ub3c4<\/strong> : \uc804\uccb4 \uae0d\uc815\uc801 \uc608\uce21\uc744 \uae30\uc900\uc73c\ub85c \uae0d\uc815\uc801 \uc608\uce21\uc744 \uc218\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uacc4\uc0b0\ub429\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uc815\ud655\ub3c4 = \ucc38\uc591\uc131 \/ (\ucc38\uc591\uc131 + \uac70\uc9d3\uc591\uc131)<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>\uc54c\ub9bc<\/strong> : \uc804\uccb4 \uc2e4\uc81c \uae0d\uc815\uc5d0 \ub300\ud55c \uae0d\uc815\uc801 \uc608\uce21 \uc218\uc815<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uacc4\uc0b0\ub429\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\ubbf8\ub9ac \uc54c\ub9bc = \ucc38 \uae0d\uc815 \/ (\ucc38 \uae0d\uc815 + \uac70\uc9d3 \ubd80\uc815)<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ud2b9\uc815 \ubaa8\ub378\uc758 \uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\uc728\uc744 \uc2dc\uac01\ud654\ud558\uae30 \uc704\ud574 <strong>\uc815\ubc00\ub3c4-\uc7ac\ud604\uc728 \uace1\uc120\uc744<\/strong> \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <span style=\"color: #000000;\">\uc774 \uace1\uc120\uc740 \ub2e4\uc591\ud55c \uc784\uacc4\uac12\uc5d0 \ub300\ud55c \uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\uc728 \uac04\uc758 \uade0\ud615\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-20068\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/precisionrecall2.png\" alt=\"Python\uc758 \uc815\ubc00 \ub9ac\ucf5c \uace1\uc120\" width=\"523\" height=\"416\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ub2e8\uacc4\ubcc4 \uc608\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc5d0 \ub300\ud55c \uc815\ubc00 \uc7ac\ud604\uc728 \uace1\uc120\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>1\ub2e8\uacc4: \ud328\ud0a4\uc9c0 \uac00\uc838\uc624\uae30<br \/><\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uba3c\uc800 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uac00\uc838\uc635\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> datasets\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> precision_recall_curve\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>2\ub2e8\uacc4: \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378 \uc801\ud569<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c \ub370\uc774\ud130\uc138\ud2b8\ub97c \uc0dd\uc131\ud558\uace0 \uc5ec\uae30\uc5d0 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#create dataset with 5 predictor variables\n<\/span>X, y = datasets. <span style=\"color: #3366ff;\">make_classification<\/span> (n_samples= <span style=\"color: #008000;\">1000<\/span> ,\n                                    n_features= <span style=\"color: #008000;\">4<\/span> ,\n                                    n_informative= <span style=\"color: #008000;\">3<\/span> ,\n                                    n_redundant= <span style=\"color: #008000;\">1<\/span> ,\n                                    random_state= <span style=\"color: #008000;\">0<\/span> )\n\n<span style=\"color: #008080;\">#split dataset into training and testing set\n<\/span>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= <span style=\"color: #008000;\">.3<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\n\n<span style=\"color: #008080;\">#fit logistic regression model to dataset\n<\/span>classifier = LogisticRegression()\nclassify. <span style=\"color: #3366ff;\">fit<\/span> (X_train, y_train)\n\n<span style=\"color: #008080;\">#use logistic regression model to make predictions\n<\/span>y_score = classify. <span style=\"color: #3366ff;\">predict_proba<\/span> (X_test)[:, <span style=\"color: #008000;\">1<\/span> ]<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>3\ub2e8\uacc4: \uc815\ubc00\ub3c4-\uc7ac\ud604\uc728 \uace1\uc120 \ub9cc\ub4e4\uae30<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c \ubaa8\ub378\uc758 \uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\uc728\uc744 \uacc4\uc0b0\ud558\uace0 \uc815\ubc00\ub3c4-\uc7ac\ud604\uc728 \uace1\uc120\uc744 \ub9cc\ub4ed\ub2c8\ub2e4.<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#calculate precision and recall\n<\/span>precision, recall, thresholds = precision_recall_curve(y_test, y_score)\n\n<span style=\"color: #008080;\">#create precision recall curve\n<\/span>fig, ax = plt. <span style=\"color: #3366ff;\">subplots<\/span> ()\nax. <span style=\"color: #3366ff;\">plot<\/span> (recall, precision, color=' <span style=\"color: #ff0000;\">purple<\/span> ')\n\n<span style=\"color: #008080;\">#add axis labels to plot\n<\/span>ax. <span style=\"color: #3366ff;\">set_title<\/span> (' <span style=\"color: #ff0000;\">Precision-Recall Curve<\/span> ')\nax. <span style=\"color: #3366ff;\">set_ylabel<\/span> (' <span style=\"color: #ff0000;\">Precision<\/span> ')\nax. <span style=\"color: #3366ff;\">set_xlabel<\/span> (' <span style=\"color: #ff0000;\">Recall<\/span> ')\n\n<span style=\"color: #008080;\">#displayplot<\/span>\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/strong><\/span> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-20068\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/precisionrecall2.png\" alt=\"Python\uc758 \uc815\ubc00 \ub9ac\ucf5c \uace1\uc120\" width=\"548\" height=\"437\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">x\ucd95\uc740 \uc7ac\ud604\uc728\uc744 \ub098\ud0c0\ub0b4\uace0 y\ucd95\uc740 \ub2e4\uc591\ud55c \uc784\uacc4\uac12\uc5d0 \ub300\ud55c \uc815\ubc00\ub3c4\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc7ac\ud604\uc728\uc774 \uc99d\uac00\ud558\uba74 \uc815\ubc00\ub3c4\uac00 \uac10\uc18c\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774\ub294 \ub450 \uce21\uc815\ud56d\ubaa9 \uac04\uc758 \uc808\ucda9\uc548\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4. \ubaa8\ub378\uc758 \uc7ac\ud604\uc728\uc744 \ub192\uc774\ub824\uba74 \uc815\ubc00\ub3c4\uac00 \uac10\uc18c\ud574\uc57c \ud558\uba70 \uadf8 \ubc18\ub300\uc758 \uacbd\uc6b0\ub3c4 \ub9c8\ucc2c\uac00\uc9c0\uc785\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/ko\/\u1105\u1169\u110c\u1175\u1109\u1173\u1110\u1175\u11a8-\u1112\u116c\u1100\u1171-\u1111\u1161\u110b\u1175\u110a\u1165\u11ab\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1111\u1161\u110b\u1175\u110a\u1165\u11ab-\u1106\u1162\u1110\u1173\u1105\u1175\u11a8\u1109\u1173-\u1112\u1169\u11ab\u1105\u1161\u11ab\/\" target=\"_blank\" rel=\"noopener\">Python\uc5d0\uc11c \ud63c\ub3d9 \ud589\ub82c\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u110b\u1161\u11b7\u1109\u1165\u11a8-\u1100\u1169\u11a8\u1109\u1165\u11ab\u110b\u1173\u11af-\u1112\u1162\u1109\u1165\u11a8\u1112\u1161\u1103\u1161\/\" target=\"_blank\" rel=\"noopener\">ROC \uace1\uc120\uc744 \ud574\uc11d\ud558\ub294 \ubc29\ubc95(\uc608\uc81c \ud3ec\ud568)<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\uae30\uacc4 \ud559\uc2b5\uc5d0\uc11c \ubd84\ub958 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud560 \ub54c \ubaa8\ub378 \ud488\uc9c8\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \uc790\uc8fc \uc0ac\uc6a9\ud558\ub294 \ub450 \uac00\uc9c0 \uc9c0\ud45c\ub294 \uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\uc728\uc785\ub2c8\ub2e4. \uc815\ud655\ub3c4 : \uc804\uccb4 \uae0d\uc815\uc801 \uc608\uce21\uc744 \uae30\uc900\uc73c\ub85c \uae0d\uc815\uc801 \uc608\uce21\uc744 \uc218\uc815\ud569\ub2c8\ub2e4. \uc774\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uacc4\uc0b0\ub429\ub2c8\ub2e4. \uc815\ud655\ub3c4 = \ucc38\uc591\uc131 \/ (\ucc38\uc591\uc131 + \uac70\uc9d3\uc591\uc131) \uc54c\ub9bc : \uc804\uccb4 \uc2e4\uc81c \uae0d\uc815\uc5d0 \ub300\ud55c \uae0d\uc815\uc801 \uc608\uce21 \uc218\uc815 \uc774\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uacc4\uc0b0\ub429\ub2c8\ub2e4. \ubbf8\ub9ac \uc54c\ub9bc = \ucc38 \uae0d\uc815 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-2168","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python\uc5d0\uc11c \uc815\ubc00 \ub9ac\ucf5c \uace1\uc120\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e8\uacc4\ubcc4 \uc608\uc81c\ub97c \ud1b5\ud574 Python\uc5d0\uc11c 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