{"id":1154,"date":"2023-07-27T11:31:28","date_gmt":"2023-07-27T11:31:28","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%85%e1%85%a9%e1%84%8c%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%b5%e1%86%a8-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab\/"},"modified":"2023-07-27T11:31:28","modified_gmt":"2023-07-27T11:31:28","slug":"%e1%84%85%e1%85%a9%e1%84%8c%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%b5%e1%86%a8-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%85%e1%85%a9%e1%84%8c%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%b5%e1%86%a8-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab\/","title":{"rendered":"Python\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95(\ub2e8\uacc4\ubcc4)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ko\/\u1105\u1169\u110c\u1175\u1109\u1173\u1110\u1175\u11a8-\u1112\u116c\u1100\u1171-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294<\/a> <a href=\"https:\/\/statorials.org\/ko\/\u1107\u1167\u11ab\u1109\u116e-\u1109\u1165\u11af\u1106\u1167\u11bc-\u110b\u1173\u11bc\u1103\u1161\u11b8\/\" target=\"_blank\" rel=\"noopener noreferrer\">\uc751\ub2f5 \ubcc0\uc218\uac00<\/a> \uc774\uc9c4\uc77c \ub54c \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubd84\uc11d\uc5d0\uc11c\ub294 <em>\ucd5c\ub300 \uc6b0\ub3c4 \ucd94\uc815<\/em> \uc774\ub77c\ub294 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc74c \ud615\uc2dd\uc758 \ubc29\uc815\uc2dd\uc744 \ucc3e\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\ub85c\uadf8[p(X) \/ ( <sub>1<\/sub> -p(X))] = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> X <sub>1<\/sub> + \u03b2 <sub>2<\/sub> X <sub>2<\/sub> + \u2026 + \u03b2 <sub>p<\/sub><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uae08:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>X <sub>j<\/sub><\/strong> : j <sup>\ubc88\uc9f8<\/sup> \uc608\uce21\ubcc0\uc218<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u03b2 <sub>j<\/sub><\/strong> : j <sup>\ubc88\uc9f8<\/sup> \uc608\uce21\ubcc0\uc218\uc5d0 \ub300\ud55c \uacc4\uc218 \ucd94\uc815<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ubc29\uc815\uc2dd \uc624\ub978\ucabd\uc758 \uacf5\uc2dd\uc740 \uc751\ub2f5 \ubcc0\uc218\uac00 \uac12 1\uc744 \ucde8\ud560 <strong>\ub85c\uadf8 \ud655\ub960\uc744<\/strong> \uc608\uce21\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub530\ub77c\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\uc6a9\ud560 \ub54c \ub2e4\uc74c \ubc29\uc815\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc5b4\uc9c4 \uad00\uce21\uac12\uc774 \uac12 1\uc744 \uac00\uc9c8 \ud655\ub960\uc744 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">p(X) = e <sup>\u03b2 <sub>0<\/sub> + <sub>\u03b2<\/sub> <sub>1<\/sub> <sub>X<\/sub> <sub>1<\/sub> <sub>+<\/sub> <sub>\u03b2<\/sub><\/sup> <sup><sub>2<\/sub> <sub>X<\/sub> <sub>2<\/sub> <sub>+<\/sub> <sub>\u2026<\/sub> <sub>+<\/sub> <sub>\u03b2<\/sub><\/sup> p<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uadf8\ub7f0 \ub2e4\uc74c \ud2b9\uc815 \ud655\ub960 \uc784\uacc4\uac12\uc744 \uc0ac\uc6a9\ud558\uc5ec \uad00\uce21\uce58\ub97c 1 \ub610\ub294 0\uc73c\ub85c \ubd84\ub958\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc608\ub97c \ub4e4\uc5b4, \ud655\ub960\uc774 0.5\ubcf4\ub2e4 \ud06c\uac70\ub098 \uac19\uc740 \uad00\uce21\uce58\ub294 &#8220;1&#8221;\ub85c \ubd84\ub958\ub418\uace0 \ub2e4\ub978 \ubaa8\ub4e0 \uad00\uce21\uce58\ub294 &#8220;0&#8221;\uc73c\ub85c \ubd84\ub958\ub420 \uac83\uc774\ub77c\uace0 \ub9d0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>1\ub2e8\uacc4: \ud544\uc694\ud55c \ud328\ud0a4\uc9c0 \uac00\uc838\uc624\uae30<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uba3c\uc800 Python\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ub370 \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;\">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: #008000;\">import<\/span> metrics\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: \ub370\uc774\ud130 \ub85c\ub4dc<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uc774 \uc608\uc5d0\uc11c\ub294 <a href=\"https:\/\/www.ime.unicamp.br\/~dias\/Intoduction%20to%20Statistical%20Learning.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Introduction to Statistical Learning \ucc45<\/a> \uc758 <strong>\uae30\ubcf8<\/strong> \ub370\uc774\ud130\uc138\ud2b8\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc138\ud2b8 \uc694\uc57d\uc744 \ub85c\ub4dc\ud558\uace0 \ud45c\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#import dataset from CSV file on Github\n<span style=\"color: #000000;\">url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/default.csv\"\ndata = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n<\/span><\/span>\n<span style=\"color: #008080;\">#view first six rows of dataset\n<\/span>data[0:6]\n\n        default student balance income\n0 0 0 729.526495 44361.625074\n1 0 1 817.180407 12106.134700\n2 0 0 1073.549164 31767.138947\n3 0 0 529.250605 35704.493935\n4 0 0 785.655883 38463.495879\n5 0 1 919.588530 7491.558572  \n\n<span style=\"color: #008080;\">#find total observations in dataset<\/span>\nlen( <span style=\"color: #3366ff;\">data.index<\/span> )\n\n10000\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0\ub294 10,000\uba85\uc758 \uac1c\uc778\uc5d0 \ub300\ud55c \ub2e4\uc74c \uc815\ubcf4\uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\uae30\ubcf8\uac12:<\/strong> \uac1c\uc778\uc774 \ucc44\ubb34 \ubd88\uc774\ud589\uc744 \ud588\ub294\uc9c0 \uc5ec\ubd80\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\ud559\uc0dd:<\/strong> \uac1c\uc778\uc774 \ud559\uc0dd\uc778\uc9c0 \uc5ec\ubd80\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\uc794\uc561:<\/strong> \uac1c\uc778\uc774 \ubcf4\uc720\ud558\uace0 \uc788\ub294 \ud3c9\uade0 \uc794\uc561\uc785\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\uc18c\ub4dd:<\/strong> \uac1c\uc778\uc758 \uc18c\ub4dd.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uc6b0\ub9ac\ub294 \ud559\uc0dd \uc0c1\ud0dc, \uc740\ud589 \uc794\uace0, \uc18c\ub4dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud2b9\uc815 \uac1c\uc778\uc758 \ucc44\ubb34 \ubd88\uc774\ud589 \uac00\ub2a5\uc131\uc744 \uc608\uce21\ud558\ub294 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>3\ub2e8\uacc4: \ud559\uc2b5 \ubc0f \ud14c\uc2a4\ud2b8 \uc0d8\ud50c \ub9cc\ub4e4\uae30<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c, \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ubaa8\ub378\uc744 <em>\ud6c8\ub828\ud558\uae30<\/em> \uc704\ud55c \ud6c8\ub828 \uc138\ud2b8\uc640 \ubaa8\ub378\uc744 <em>\ud14c\uc2a4\ud2b8\ud558\uae30 \uc704\ud55c<\/em> \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ubd84\ud560\ud569\ub2c8\ub2e4.<\/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 = data[[' <span style=\"color: #008000;\">student<\/span> ',' <span style=\"color: #008000;\">balance<\/span> ',' <span style=\"color: #008000;\">income<\/span> ']]\ny = data[' <span style=\"color: #008000;\">default<\/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)<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>4\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 <b>LogisticRegression()<\/b> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \ub9de\ucda5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#instantiate the model\n<\/span>log_regression = LogisticRegression()\n\n<span style=\"color: #008080;\">#fit the model using the training data\n<\/span>log_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 = log_regression. <span style=\"color: #3366ff;\">predict<\/span> (X_test)\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>5\ub2e8\uacc4: \ubaa8\ub378 \uc9c4\ub2e8<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\uc6a9\ud55c \ud6c4\uc5d0\ub294 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ubd84\uc11d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> \uba3c\uc800 <span style=\"color: #000000;\">\ubaa8\ub378\uc5d0 \ub300\ud55c<\/span> <span style=\"color: #000000;\">\ud63c\ub3d9 \ud589\ub82c\uc744 \ub9cc\ub4ed\ub2c8\ub2e4<\/span> .<\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong>cnf_matrix = metrics. <span style=\"color: #3366ff;\">confusion_matrix<\/span> (y_test, y_pred)\ncnf_matrix\n\narray([[2886, 1],\n       [113,0]])\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\ud63c\ub3d9 \ud589\ub82c\uc744 \ud1b5\ud574 \ub2e4\uc74c\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">#\uc9c4\uc815\uc131 \uc608\uce21: 2886<\/span><\/li>\n<li> <span style=\"color: #000000;\">#\uc9c4\uc74c\uc131 \uc608\uce21: 0<\/span><\/li>\n<li> <span style=\"color: #000000;\">#\uc624\ud0d0\uc9c0 \uc608\uce21: 113<\/span><\/li>\n<li> <span style=\"color: #000000;\">#\uac70\uc9d3\uc74c\uc131 \uc608\uce21: 1<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub610\ud55c \ubaa8\ub378\uc5d0 \uc758\ud574 \uc218\ud589\ub41c \uc218\uc815 \uc608\uce21\uc758 \ubc31\ubd84\uc728\uc744 \uc54c\ub824\uc8fc\ub294 \uc815\ud655\ub3c4 \ubaa8\ub378\uc744 \uc5bb\uc744 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>print(\" <span style=\"color: #008000;\">Accuracy:<\/span> \", <span style=\"color: #3366ff;\">metrics.accuracy_score<\/span> (y_test, y_pred))l\n\nAccuracy: 0.962\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774\ub294 \ubaa8\ub378\uc774 \uac1c\uc778\uc758 \ucc44\ubb34 \ubd88\uc774\ud589 \uc5ec\ubd80\uc5d0 \ub300\ud574 <strong>96.2%<\/strong> \uc758 \ud655\ub960\ub85c \uc62c\ubc14\ub978 \uc608\uce21\uc744 \ud588\ub2e4\ub294 \uac83\uc744 \ub9d0\ud574\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub9c8\uc9c0\ub9c9\uc73c\ub85c \uc608\uce21 \ud655\ub960 \uc784\uacc4\uac12\uc774 1\uc5d0\uc11c 0\uc73c\ub85c \ub0ae\uc544\uc9c8 \ub54c \ubaa8\ub378\uc5d0\uc11c \uc608\uce21\ud55c \ucc38 \uae0d\uc815\uc758 \ube44\uc728\uc744 \ud45c\uc2dc\ud558\ub294 ROC(\uc218\uc2e0\uae30 \uc791\ub3d9 \ud2b9\uc131) \uace1\uc120\uc744 \uadf8\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">AUC(\uace1\uc120 \uc544\ub798 \uc601\uc5ed)\uac00 \ub192\uc744\uc218\ub85d \ubaa8\ub378\uc774 \uacb0\uacfc\ub97c \ub354 \uc815\ud655\ud558\uac8c \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define metrics<\/span>\ny_pred_proba = log_regression. <span style=\"color: #3366ff;\">predict_proba<\/span> (X_test)[::,1]\nfpr, tpr, _ = metrics. <span style=\"color: #3366ff;\">roc_curve<\/span> (y_test, y_pred_proba)\nauc = metrics. <span style=\"color: #3366ff;\">roc_auc_score<\/span> (y_test, y_pred_proba)\n\n<span style=\"color: #008080;\">#create ROC curve\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (fpr,tpr,label=\" <span style=\"color: #008000;\">AUC=<\/span> \"+str(auc))\nplt. <span style=\"color: #3366ff;\">legend<\/span> (loc=4)\nplt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/strong><\/span><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11591 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/auc1.png\" alt=\"Python\uc758 ROC \uace1\uc120\" width=\"389\" height=\"262\" srcset=\"\" sizes=\"auto, \"><\/p>\n<div class=\"entry-content entry-content-single\" data-content-ads-inserted=\"true\">\n<p> <em><span style=\"color: #000000;\">\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0 \uc0ac\uc6a9\ub41c \uc804\uccb4 Python \ucf54\ub4dc\ub294 <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/logistic_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">\uc5ec\uae30\uc5d0\uc11c<\/a> \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/em><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \uc751\ub2f5 \ubcc0\uc218\uac00 \uc774\uc9c4\uc77c \ub54c \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubd84\uc11d\uc5d0\uc11c\ub294 \ucd5c\ub300 \uc6b0\ub3c4 \ucd94\uc815 \uc774\ub77c\ub294 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc74c \ud615\uc2dd\uc758 \ubc29\uc815\uc2dd\uc744 \ucc3e\uc2b5\ub2c8\ub2e4. \ub85c\uadf8[p(X) \/ ( 1 -p(X))] = \u03b2 0 + \u03b2 1 X 1 + \u03b2 2 X 2 + \u2026 + \u03b2 p \uae08: X j : j [&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-1154","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 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95(\ub2e8\uacc4\ubcc4) - \ud1b5\uacc4<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e8\uacc4\ubcc4 \uc608\uc81c\ub97c \ud3ec\ud568\ud558\uc5ec Python\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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