{"id":742,"date":"2024-07-11T08:18:22","date_gmt":"2024-07-11T08:18:22","guid":{"rendered":"http:\/\/www.ohsumin.com\/?p=742"},"modified":"2025-06-11T01:23:00","modified_gmt":"2025-06-11T01:23:00","slug":"%eb%a1%9c%ec%a7%93%eb%b6%84%ec%84%9dlogit-analysis","status":"publish","type":"post","link":"http:\/\/www.inmining.co.kr\/?p=742","title":{"rendered":"\ub85c\uc9d3\ubd84\uc11d(Logit Analysis)"},"content":{"rendered":"\n

\ub85c\uc9d3 \ubd84\uc11d(Logit Analysis)\uc740 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0(Logistic Regression)\ub77c\uace0\ub3c4 \ubd88\ub9ac\uba70, \ud63c\uc6a9\ud574\uc11c \uc0ac\uc6a9\ud55c\ub2e4. \uc885\uc18d \ubcc0\uc218\uac00 \uc774\uc9c4(binary) \ub610\ub294 \ubc94\uc8fc\ud615\uc77c \ub54c \uc774\ub97c \uc124\uba85\ud558\uac70\ub098 \uc608\uce21\ud558\uae30 \uc704\ud574 \uc0ac\uc6a9\ub418\ub294 \ud1b5\uacc4 \ubd84\uc11d \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \uc885\uc18d \ubcc0\uc218\uc758 \uacb0\uacfc\uac00 \ub450 \uac00\uc9c0 \uac00\ub2a5\ud55c \uac12(\uc608: \uc131\uacf5\/\uc2e4\ud328, \ucc38\/\uac70\uc9d3) \uc911 \ud558\ub098\ub97c \ucde8\ud558\ub294 \uacbd\uc6b0\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4.
\\(\\)<\/p>\n\n\n\n

<\/span>\ub85c\uc9d3 \ubd84\uc11d\uc758 \uc8fc\uc694 \uac1c\ub150<\/span><\/h3>\n\n\n\n
    \n
  1. \uc885\uc18d \ubcc0\uc218\uc640 \ub3c5\ub9bd \ubcc0\uc218<\/strong>:<\/li>\n<\/ol>\n\n\n\n
      \n
    • \uc885\uc18d \ubcc0\uc218(Dependent Variable)<\/strong>: \uc774\uc9c4 \ub610\ub294 \ubc94\uc8fc\ud615 \ubcc0\uc218\ub97c \uac00\uc9c0\uba70, \uc608\uce21\ud558\ub824\ub294 \ub300\uc0c1\uc785\ub2c8\ub2e4. \uc608: \uc9c8\ubcd1 \uc720\ubb34(\uc788\uc74c\/\uc5c6\uc74c), \uad6c\ub9e4 \uc5ec\ubd80(\uad6c\ub9e4\/\ubbf8\uad6c\ub9e4).<\/li>\n\n\n\n
    • \ub3c5\ub9bd \ubcc0\uc218(Independent Variable)<\/strong>: \uc885\uc18d \ubcc0\uc218\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294 \ubcc0\uc218\ub4e4\uc785\ub2c8\ub2e4. \uc608: \ub098\uc774, \uc18c\ub4dd, \uad50\uc721 \uc218\uc900.<\/li>\n<\/ul>\n\n\n\n
        \n
      1. \ub85c\uc9d3 \ud568\uc218(Logit Function)<\/strong>:<\/li>\n<\/ol>\n\n\n\n
          \n
        • \ub85c\uc9d3 \ud568\uc218\ub294 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc5d0\uc11c \uc0ac\uc6a9\ub418\ub294 \ud568\uc218\ub85c, \ud2b9\uc815 \uc0ac\uac74\uc774 \ubc1c\uc0dd\ud560 \ud655\ub960\uc744 \uc608\uce21\ud569\ub2c8\ub2e4.<\/li>\n\n\n\n
        • \ub85c\uc9d3 \ud568\uc218\ub294 \ud655\ub960 ( p )\ub97c \ub85c\uadf8 \uc624\uc988(log odds)\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.
          \\[ \\text{logit}(p) = \\log\\left(\\frac{p}{1-p}\\right) \\]<\/li>\n<\/ul>\n\n\n\n
            \n
          1. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378(Logistic Regression Model)<\/strong>:<\/li>\n<\/ol>\n\n\n\n
              \n
            • \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc740 \ub85c\uc9d3 \ud568\uc218\uc640 \ub3c5\ub9bd \ubcc0\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc885\uc18d \ubcc0\uc218\uc758 \ud655\ub960\uc744 \uc608\uce21\ud569\ub2c8\ub2e4.
              \\[ \\text{logit}(p) = \\beta_0 + \\beta_1 X_1 + \\beta_2 X_2 + \\cdots + \\beta_k X_k \\]<\/li>\n\n\n\n
            • \uc5ec\uae30\uc11c \\( \\beta_0 \\)\ub294 \uc808\ud3b8(intercept), \\( \\beta_1, \\beta_2, \\ldots, \\beta_k \\)\ub294 \ud68c\uadc0 \uacc4\uc218(regression coefficients), \\( X_1, X_2, \\ldots, X_k \\)\ub294 \ub3c5\ub9bd \ubcc0\uc218\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n\n\n\n
                \n
              1. \ud655\ub960 \uc608\uce21<\/strong>:<\/li>\n<\/ol>\n\n\n\n
                  \n
                • \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud1b5\ud574 \uc608\uce21\ub41c \ub85c\uadf8 \uc624\uc988(log odds)\ub97c \ud655\ub960\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.
                  \\[ p = \\frac{1}{1 + e^{-(\\beta_0 + \\beta_1 X_1 + \\beta_2 X_2 + \\cdots + \\beta_k X_k)}} \\]<\/li>\n<\/ul>\n\n\n\n

                  <\/span>\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc758 \uc0ac\uc6a9 \uc0ac\ub840<\/span><\/h3>\n\n\n\n
                    \n
                  • \uc758\ub8cc \ubd84\uc57c<\/strong>: \ud658\uc790\uac00 \ud2b9\uc815 \uc9c8\ubcd1\uc5d0 \uac78\ub9b4 \ud655\ub960 \uc608\uce21<\/li>\n\n\n\n
                  • \ub9c8\ucf00\ud305<\/strong>: \uace0\uac1d\uc774 \uc81c\ud488\uc744 \uad6c\ub9e4\ud560 \ud655\ub960 \uc608\uce21<\/li>\n\n\n\n
                  • \uc0ac\ud68c \uacfc\ud559<\/strong>: \ud2b9\uc815 \ud589\ub3d9\uc744 \ud560 \uac00\ub2a5\uc131 \uc608\uce21(\uc608: \ud22c\ud45c \uc5ec\ubd80)<\/li>\n<\/ul>\n\n\n\n

                    <\/span>\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc758 \uc7a5\uc810<\/span><\/h3>\n\n\n\n
                      \n
                    • \ud574\uc11d \uc6a9\uc774<\/strong>: \ud68c\uadc0 \uacc4\uc218\ub294 \ub3c5\ub9bd \ubcc0\uc218\uac00 \uc885\uc18d \ubcc0\uc218\uc758 \ub85c\uadf8 \uc624\uc988\uc5d0 \ubbf8\uce58\ub294 \uc601\ud5a5\uc744 \ub098\ud0c0\ub0b4\uba70, \uc774\ub97c \ud1b5\ud574 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \ud574\uc11d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n\n\n\n
                    • \ud655\ub960 \uc608\uce21<\/strong>: \uacb0\uacfc\ub97c \ud655\ub960\ub85c \ud574\uc11d\ud560 \uc218 \uc788\uc5b4 \uc9c1\uad00\uc801\uc785\ub2c8\ub2e4.<\/li>\n\n\n\n
                    • \ub2e4\uc591\ud55c \ubcc0\uc218 \uc0ac\uc6a9 \uac00\ub2a5<\/strong>: \ubc94\uc8fc\ud615 \ubcc0\uc218\uc640 \uc5f0\uc18d\ud615 \ubcc0\uc218\ub97c \ubaa8\ub450 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n\n\n\n

                      <\/span>\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc758 \uc81c\ud55c \uc0ac\ud56d<\/span><\/h3>\n\n\n\n
                        \n
                      • \ub3c5\ub9bd \ubcc0\uc218 \uac04\uc758 \ub2e4\uc911\uacf5\uc120\uc131<\/strong>: \ub3c5\ub9bd \ubcc0\uc218\ub4e4 \uac04\uc758 \uc0c1\uad00\uad00\uacc4\uac00 \ub192\uc73c\uba74 \ud68c\uadc0 \uacc4\uc218\uc758 \ucd94\uc815\uc774 \ubd88\uc548\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n\n\n\n
                      • \uc774\uc9c4 \ub610\ub294 \ubc94\uc8fc\ud615 \uc885\uc18d \ubcc0\uc218<\/strong>: \uc885\uc18d \ubcc0\uc218\uac00 \uc5f0\uc18d\ud615\uc778 \uacbd\uc6b0\uc5d0\ub294 \uc801\ud569\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n\n\n\n
                        \"\"<\/figure>\n\n\n\n

                        <\/span>\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378 \ud3c9\uac00 \ubc29\ubc95<\/span><\/h2>\n\n\n\n

                        \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\ub294 \ubc29\ubc95\uc740 \uc5ec\ub7ec \uac00\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uc8fc\uc694 \ud3c9\uac00 \ubc29\ubc95\ub4e4\uc744 \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n

                        <\/span>1. \ud63c\ub3d9 \ud589\ub82c (Confusion Matrix)<\/span><\/h3>\n\n\n\n

                        \ud63c\ub3d9 \ud589\ub82c\uc740 \ubaa8\ub378\uc758 \ubd84\ub958 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \ub370 \uc0ac\uc6a9\ub418\ub294 \ud45c\ub85c, \uc2e4\uc81c \ud074\ub798\uc2a4\uc640 \uc608\uce21 \ud074\ub798\uc2a4\uc758 \uad50\ucc28\ud45c\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/p>\n\n\n\n

                          \n
                        • TP (True Positive)<\/strong>: \uc2e4\uc81c \ud074\ub798\uc2a4\uac00 1\uc774\uace0 \uc608\uce21 \ud074\ub798\uc2a4\ub3c4 1\uc778 \uacbd\uc6b0<\/li>\n\n\n\n
                        • TN (True Negative)<\/strong>: \uc2e4\uc81c \ud074\ub798\uc2a4\uac00 0\uc774\uace0 \uc608\uce21 \ud074\ub798\uc2a4\ub3c4 0\uc778 \uacbd\uc6b0<\/li>\n\n\n\n
                        • FP (False Positive)<\/strong>: \uc2e4\uc81c \ud074\ub798\uc2a4\uac00 0\uc774\uace0 \uc608\uce21 \ud074\ub798\uc2a4\uac00 1\uc778 \uacbd\uc6b0<\/li>\n\n\n\n
                        • FN (False Negative)<\/strong>: \uc2e4\uc81c \ud074\ub798\uc2a4\uac00 1\uc774\uace0 \uc608\uce21 \ud074\ub798\uc2a4\uac00 0\uc778 \uacbd\uc6b0<\/li>\n<\/ul>\n\n\n\n

                          \uc608\uc2dc \ucf54\ub4dc:<\/h4>\n\n\n\n
                          from sklearn.metrics import confusion_matrix\n\n# \uc608\uce21\ny_pred = model.predict(X_test)\n\n# \ud63c\ub3d9 \ud589\ub82c \uacc4\uc0b0\ncm = confusion_matrix(y_test, y_pred)\nprint(cm)<\/code><\/pre>\n\n\n\n
                          <\/span>2. \uc815\ud655\ub3c4 (Accuracy)<\/span><\/h3>\n\n\n\n
                          \uc815\ud655\ub3c4\ub294 \uc804\uccb4 \uc0d8\ud50c \uc911\uc5d0\uc11c \ub9de\uac8c \uc608\uce21\ud55c \uc0d8\ud50c\uc758 \ube44\uc728\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/p>\n\n\n\n
                          [ \\text{Accuracy} = \\frac{TP + TN}{TP + TN + FP + FN} ]<\/p>\n\n\n\n
                          \uc608\uc2dc \ucf54\ub4dc:<\/h4>\n\n\n\n
                          from sklearn.metrics import accuracy_score\n\n# \uc815\ud655\ub3c4 \uacc4\uc0b0\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"Accuracy: {accuracy}\")<\/code><\/pre>\n\n\n\n
                          <\/span>3. \uc815\ubc00\ub3c4 (Precision)\uacfc \uc7ac\ud604\uc728 (Recall)<\/span><\/h3>\n\n\n\n
                            \n
                          • \uc815\ubc00\ub3c4 (Precision)<\/strong>: \ubaa8\ub378\uc774 \uc591\uc131 \ud074\ub798\uc2a4(1)\ub85c \uc608\uce21\ud55c \uac83 \uc911 \uc2e4\uc81c \uc591\uc131 \ud074\ub798\uc2a4\uc758 \ube44\uc728
                            [ \\text{Precision} = \\frac{TP}{TP + FP} ]<\/li>\n\n\n\n
                          • \uc7ac\ud604\uc728 (Recall)<\/strong>: \uc2e4\uc81c \uc591\uc131 \ud074\ub798\uc2a4 \uc911 \ubaa8\ub378\uc774 \uc591\uc131 \ud074\ub798\uc2a4\ub85c \uc815\ud655\ud788 \uc608\uce21\ud55c \ube44\uc728
                            [ \\text{Recall} = \\frac{TP}{TP + FN} ]<\/li>\n<\/ul>\n\n\n\n
                            \uc608\uc2dc \ucf54\ub4dc:<\/h4>\n\n\n\n
                            from sklearn.metrics import precision_score, recall_score\n\n# \uc815\ubc00\ub3c4 \uacc4\uc0b0\nprecision = precision_score(y_test, y_pred)\nprint(f\"Precision: {precision}\")\n\n# \uc7ac\ud604\uc728 \uacc4\uc0b0\nrecall = recall_score(y_test, y_pred)\nprint(f\"Recall: {recall}\")<\/code><\/pre>\n\n\n\n
                            <\/span>4. F1 \uc810\uc218 (F1 Score)<\/span><\/h3>\n\n\n\n
                            F1 \uc810\uc218\ub294 \uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\uc728\uc758 \uc870\ud654 \ud3c9\uade0\uc73c\ub85c, \ub450 \uc9c0\ud45c\ub97c \ud558\ub098\uc758 \uc9c0\ud45c\ub85c \uacb0\ud569\ud569\ub2c8\ub2e4.<\/p>\n\n\n\n
                            [ \\text{F1 Score} = 2 \\cdot \\frac{\\text{Precision} \\cdot \\text{Recall}}{\\text{Precision} + \\text{Recall}} ]<\/p>\n\n\n\n
                            \uc608\uc2dc \ucf54\ub4dc:<\/h4>\n\n\n\n
                            from sklearn.metrics import f1_score\n\n# F1 \uc810\uc218 \uacc4\uc0b0\nf1 = f1_score(y_test, y_pred)\nprint(f\"F1 Score: {f1}\")<\/code><\/pre>\n\n\n\n
                            <\/span>5. ROC \uace1\uc120\uacfc AUC (Receiver Operating Characteristic Curve and Area Under Curve)<\/span><\/h3>\n\n\n\n
                              \n
                            • ROC \uace1\uc120<\/strong>: FPR(\uc704\uc591\uc131\ub960, False Positive Rate)\uc640 TPR(\uc9c4\uc591\uc131\ub960, True Positive Rate) \uac04\uc758 \uad00\uacc4\ub97c \ub098\ud0c0\ub0b4\ub294 \uace1\uc120<\/li>\n\n\n\n
                            • AUC (Area Under Curve)<\/strong>: ROC \uace1\uc120 \uc544\ub798 \uba74\uc801, \ubaa8\ub378\uc758 \ubd84\ub958 \uc131\ub2a5\uc744 \ub098\ud0c0\ub0b4\ub294 \uc9c0\ud45c<\/li>\n<\/ul>\n\n\n\n
                              \uc608\uc2dc \ucf54\ub4dc:<\/h4>\n\n\n\n
                              from sklearn.metrics import roc_curve, roc_auc_score\nimport matplotlib.pyplot as plt\n\n# \uc608\uce21 \ud655\ub960 \uacc4\uc0b0\ny_prob = model.predict_proba(X_test)[:, 1]\n\n# ROC \uace1\uc120 \uacc4\uc0b0\nfpr, tpr, _ = roc_curve(y_test, y_prob)\nauc = roc_auc_score(y_test, y_prob)\n\n# ROC \uace1\uc120 \uadf8\ub9ac\uae30\nplt.figure(figsize=(10, 6))\nplt.plot(fpr, tpr, label=f'ROC curve (AUC = {auc:.2f})')\nplt.plot([0, 1], [0, 1], linestyle='--')\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver Operating Characteristic (ROC) Curve')\nplt.legend(loc=\"lower right\")\nplt.grid(True)\nplt.show()<\/code><\/pre>\n\n\n\n
                              <\/span>6. \ub85c\uadf8 \uc190\uc2e4 (Log Loss)<\/span><\/h3>\n\n\n\n
                              \ub85c\uadf8 \uc190\uc2e4\uc740 \ubaa8\ub378\uc758 \uc608\uce21 \ud655\ub960\uacfc \uc2e4\uc81c \ud074\ub798\uc2a4 \uac04\uc758 \uc624\ucc28\ub97c \uce21\uc815\ud558\ub294 \uc9c0\ud45c\ub85c, \uac12\uc774 \uc791\uc744\uc218\ub85d \uc88b\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n
                              \uc608\uc2dc \ucf54\ub4dc:<\/h4>\n\n\n\n
                              from sklearn.metrics import log_loss\n\n# \ub85c\uadf8 \uc190\uc2e4 \uacc4\uc0b0\nlogloss = log_loss(y_test, y_prob)\nprint(f\"Log Loss: {logloss}\")<\/code><\/pre>\n\n\n\n
                              \uc774\ub7ec\ud55c \ubc29\ubc95\ub4e4\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ub2e4\uac01\ub3c4\ub85c \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac01 \ubc29\ubc95\uc740 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ub2e4\ub978 \uce21\uba74\uc5d0\uc11c \ud3c9\uac00\ud558\ubbc0\ub85c, \uc5ec\ub7ec \ubc29\ubc95\uc744 \ud568\uaed8 \uc0ac\uc6a9\ud558\uba74 \ub354 \uc885\ud569\uc801\uc778 \ud3c9\uac00\uac00 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n\n\n\n
                              \"\"<\/figure>\n\n\n\n
                              <\/span>\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \uc608\uc2dc (Python \ucf54\ub4dc)<\/span><\/h3>\n\n\n\n
                              import numpy as np\nimport pandas as pd\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report, confusion_matrix\n\n# \ub370\uc774\ud130 \uc0dd\uc131 (\uc608\uc2dc)\nnp.random.seed(0)\nX = np.random.rand(100, 2)  # \ub3c5\ub9bd \ubcc0\uc218\ny = (X[:, 0] + X[:, 1] > 1).astype(int)  # \uc885\uc18d \ubcc0\uc218 (0 \ub610\ub294 1)\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n\n# \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378 \uc0dd\uc131 \ubc0f \ud559\uc2b5\nmodel = LogisticRegression()\nmodel.fit(X_train, y_train)\n\n# \uc608\uce21\ny_pred = model.predict(X_test)\n\n# \ud3c9\uac00\nprint(confusion_matrix(y_test, y_pred))\nprint(classification_report(y_test, y_pred))<\/code><\/pre>\n\n\n\n
                              \uc774 \uc608\uc2dc\uc5d0\uc11c\ub294 \ub450 \uac1c\uc758 \ub3c5\ub9bd \ubcc0\uc218 \\( X \\)\uc640 \uc774\uc9c4 \uc885\uc18d \ubcc0\uc218 \\( y \\)\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\uace0, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uacfc \ud3c9\uac00\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n\n\n\n
                              \"\"<\/figure>\n\n\n\n
                              \"\"<\/figure>\n\n\n\n
                              \uc704 \uadf8\ub9bc\uc740 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0(Logistic Regression)\uc758 \uacb0\uacfc\ub97c \uc2dc\uac01\uc801\uc73c\ub85c \ub098\ud0c0\ub0b8 \uac83\uc785\ub2c8\ub2e4.<\/p>\n\n\n\n
                              <\/span>\uad6c\uc131 \uc694\uc18c:<\/span><\/h3>\n\n\n\n
                              \ud30c\ub780\uc0c9 \uc810 (Data)<\/strong>:<\/p>\n\n\n\n
                                \n
                              1. <\/li>\n<\/ol>\n\n\n\n
                                  \n
                                • \ub3c5\ub9bd \ubcc0\uc218\uc640 \uc885\uc18d \ubcc0\uc218\uc758 \uc2e4\uc81c \ub370\uc774\ud130\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/li>\n\n\n\n
                                • \uc885\uc18d \ubcc0\uc218\ub294 \uc774\uc9c4(0 \ub610\ub294 1)\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n\n\n\n
                                  \ube68\uac04\uc0c9 \uc120 (Logistic Regression Model)<\/strong>:<\/p>\n\n\n\n
                                    \n
                                  1. <\/li>\n<\/ol>\n\n\n\n
                                      \n
                                    • \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21\ub41c \ud655\ub960\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/li>\n\n\n\n
                                    • \ub3c5\ub9bd \ubcc0\uc218 \uac12\uc5d0 \ub530\ub978 \uc885\uc18d \ubcc0\uc218\uc758 \ud655\ub960\uc744 \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n\n\n\n
                                      <\/span>\ud574\uc11d:<\/span><\/h3>\n\n\n\n
                                        \n
                                      • \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378<\/strong>: \ub3c5\ub9bd \ubcc0\uc218 \uac12\uc774 \uc99d\uac00\ud568\uc5d0 \ub530\ub77c \uc885\uc18d \ubcc0\uc218\uac00 1\uc77c \ud655\ub960\uc774 \uc99d\uac00\ud558\ub294 \uac83\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \ub85c\uc9c0\uc2a4\ud2f1 \ud568\uc218\ub294 S-\ubaa8\uc591\uc758 \uace1\uc120\uc744 \ud615\uc131\ud558\uba70, \uc774\ub294 \ud655\ub960\uc774 0\uc5d0\uc11c 1\ub85c \uc810\uc9c4\uc801\uc73c\ub85c \ubcc0\ud654\ud568\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/li>\n\n\n\n
                                      • \ub3c5\ub9bd \ubcc0\uc218\uc758 \uc601\ud5a5<\/strong>: \uc608\uc2dc\uc5d0\uc11c\ub294 \ub3c5\ub9bd \ubcc0\uc218 \uac12\uc774 5\ub97c \uae30\uc900\uc73c\ub85c \uc885\uc18d \ubcc0\uc218\uc758 \uac12\uc774 0\uc5d0\uc11c 1\ub85c \ubcc0\ud558\ub294 \uacbd\ud5a5\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ub3c5\ub9bd \ubcc0\uc218 \uac12\uc774 5 \uc774\uc0c1\uc77c \ub54c \uc885\uc18d \ubcc0\uc218\uac00 1\uc77c \ud655\ub960\uc774 \ub192\uc544\uc9d0\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n\n\n\n
                                        \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \uc774\uc640 \uac19\uc774 \ub3c5\ub9bd \ubcc0\uc218\uc640 \uc885\uc18d \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \ud655\ub960\ub85c \ub098\ud0c0\ub0b4\uc5b4, \uc774\uc9c4 \ubd84\ub958 \ubb38\uc81c\uc5d0\uc11c \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ubd84\uc11d \ub3c4\uad6c\uc785\ub2c8\ub2e4. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \uc774\uc9c4 \ubd84\ub958 \ubb38\uc81c\uc5d0\uc11c \ub9e4\uc6b0 \uc720\uc6a9\ud55c \ubc29\ubc95\uc73c\ub85c, \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ub110\ub9ac \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n","protected":false},"excerpt":{"rendered":"
                                        \ub85c\uc9d3 \ubd84\uc11d(Logit Analysis)\uc740 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0(Logistic Regression)\ub77c\uace0\ub3c4 \ubd88\ub9ac\uba70, \ud63c\uc6a9\ud574\uc11c \uc0ac\uc6a9\ud55c\ub2e4. \uc885\uc18d \ubcc0\uc218\uac00 \uc774\uc9c4(binary) \ub610\ub294 \ubc94\uc8fc\ud615\uc77c \ub54c \uc774\ub97c \uc124\uba85\ud558\uac70\ub098 \uc608\uce21\ud558\uae30…<\/p>\n","protected":false},"author":1,"featured_media":1124,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-742","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-stat","wpcat-6-id"],"_links":{"self":[{"href":"http:\/\/www.inmining.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/742","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.inmining.co.kr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.inmining.co.kr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.inmining.co.kr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.inmining.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=742"}],"version-history":[{"count":5,"href":"http:\/\/www.inmining.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/742\/revisions"}],"predecessor-version":[{"id":1786,"href":"http:\/\/www.inmining.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/742\/revisions\/1786"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/www.inmining.co.kr\/index.php?rest_route=\/wp\/v2\/media\/1124"}],"wp:attachment":[{"href":"http:\/\/www.inmining.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=742"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.inmining.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=742"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.inmining.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=742"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}