{"id":9932,"date":"2020-07-08T09:27:24","date_gmt":"2020-07-08T00:27:24","guid":{"rendered":"http:\/\/www.gisdeveloper.co.kr\/?p=9932"},"modified":"2020-07-14T08:55:23","modified_gmt":"2020-07-13T23:55:23","slug":"%ed%98%bc%eb%8f%88%ed%96%89%eb%a0%acconfusion-matrix%ec%99%80-%ec%a0%95%eb%b0%80%eb%8f%84-%ec%9e%ac%ed%98%84%eb%a5%a0-f1%ec%a0%90%ec%88%98","status":"publish","type":"post","link":"http:\/\/www.gisdeveloper.co.kr\/?p=9932","title":{"rendered":"\ud63c\ub3c8\ud589\ub82c(Confusion Matrix)\uc640 \uc815\ubc00\ub3c4, \uc7ac\ud604\ub960, F1\uc810\uc218"},"content":{"rendered":"

\uc774 \uae00\uc740 \ud55c\ube5b\ubbf8\ub514\uc5b4\uc758 \ud578\uc988\uc628 \uba38\uc2e0\ub7ec\ub2dd<\/a>\uc744 \uc218\uc5c5\uc790\ub8cc\ub85c\uc368 \ud30c\uc545\ud558\uba74\uc11c \uc774\ud574\ud55c \ubc14\ub97c \uc9e7\uac8c \uc694\uc57d\ud55c \uae00\uc785\ub2c8\ub2e4. \uc694\uc998 \uc774 \ucc45\uc744 \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd\uc744 \ub2e4\uc2dc \uc811\ud558\uace0 \uc788\ub294\ub370, \uccb4\uacc4\uc801\uc774\uace0 \uc88b\uc740 \ub0b4\uc6a9\uc744 \uc81c\uacf5\ud558\uace0 \uc788\uace0, \ub098 \uc790\uc2e0\uc744 \uc704\ud55c \ubcf4\ub2e4 \uba85\ud655\ud55c \uc774\ud574\ub97c \ub3d5\uace0\uc790 \uc774 \uae00\uc744 \uc791\uc131 \uc791\uc131\ud569\ub2c8\ub2e4. \uc694\uc998 \uc81c\uac00 \ube14\ub85c\uadf8\uc5d0 \uc62c\ub9ac\ub294 \uba38\uc2e0\ub7ec\ub2dd \uad00\ub828 \uae00\uc740 \ub300\ubd80\ubd84 \uc774 \ucc45\uc758 \ub0b4\uc6a9\uc5d0 \ub300\ud55c \ub098\ub984\ub300\ub85c\uc758 \ud574\uc11d\uc744 \ud1a0\ub300\ub85c \ud569\ub2c8\ub2e4. \ubcf4\ub2e4 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 \ud574\ub2f9 \ub3c4\uc11c\ub97c \ucc38\uace0\ud558\uae30 \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n

\uc774\uae00\uc740 \ud6c8\ub828\ub41c \uc608\uce21 \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud55c \uc9c0\ud45c\uc778 \uc815\ubc00\ub3c4, \uc7ac\ud604\ub960, F1\uc5d0 \ub300\ud55c \ub0b4\uc6a9\uc785\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ud3c9\uac00 \uc9c0\ud45c\ub294 \ud63c\ub3c8\ud589\ub82c\uc774\ub77c\ub294 \ub370\uc774\ud130\ub97c \ud1a0\ub300\ub85c \uacc4\uc0b0\ub418\ub294\ub370\uc694, \uba3c\uc800 \ud63c\ub3c8\ud589\ub82c\uc744 \uad6c\ud558\uae30 \uc704\ud574 \ud559\uc2b5 \ub370\uc774\ud130\uc14b\uc774 \ud544\uc694\ud558\uba70, 0~9\uae4c\uc9c0\uc758 \uc22b\uc790\ub97c \uc190\uc73c\ub85c \uc791\uc131\ud55c MINIST\ub97c \uc0ac\uc6a9\ud558\uace0, \uc774 \uc190\uae00\uc528\uac00 7\uc778\uc9c0\uc5d0 \ub300\ud55c \uc608\uce21 \ubaa8\ub378\uc744 \uc608\ub85c \ud569\ub2c8\ub2e4. MNIST \ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc6b4\ub85c\ub4dc \ubc1b\uace0, \ub808\uc774\ube14 \ub370\uc774\ud130\ub97c \uc7ac\uac00\uacf5\ud569\ub2c8\ub2e4.<\/p>\n

\r\nfrom sklearn.datasets import fetch_openml\r\nimport numpy as np\r\n\r\nmnist = fetch_openml('mnist_784', version=1, data_home='D:\/__Temp__\/_')\r\n\r\nX, y = mnist[\"data\"], mnist[\"target\"]\r\ny = y.astype(np.uint8)\r\n\r\nX_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]\r\ny_train_7 = (y_train == 7)\r\ny_test_7 = (y_test == 7)\r\n<\/pre>\n
\uc608\uce21 \ubaa8\ub378\uc740 SGDClassifier\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n
\r\nfrom sklearn.linear_model import SGDClassifier\r\n\r\nmodel = SGDClassifier(random_state=3224)\r\n<\/pre>\n
\ud63c\ub3c8 \ud589\ub82c\uc744 \uc5bb\uae30 \uc704\ud574 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc2e4\ud589\ud569\ub2c8\ub2e4.<\/p>\n
\r\nfrom sklearn.model_selection import cross_val_predict\r\nfrom sklearn.metrics import confusion_matrix\r\n\r\ny_train_pred = cross_val_predict(model, X_train, y_train_7, cv=3)\r\ncf = confusion_matrix(y_train_7, y_train_pred)\r\nprint(cf)\r\n<\/pre>\n
\r\n[[53223   512]\r\n [  726  5539]]\r\n<\/pre>\n
cross_val_predict \ud568\uc218\ub294 \uc544\uc9c1 \uc804\ud600 \ud559\uc2b5\uc774 \ub418\uc9c0 \uc54a\uc740 \ubaa8\ub378\uc744 \uc9c0\uc815\ub41c \uad50\ucc28\uac80\uc99d \uc218\ub9cc\ud07c \ud559\uc2b5\uc2dc\ud0a8 \ub4a4 \uc608\uce21\uac12\uc744 \ubc18\ud658\ud569\ub2c8\ub2e4. \uc774\ub807\uac8c \uc5bb\uc740 \uc608\uce21\uac12\uacfc \uc2e4\uc81c \uac12\uc744 \ube44\uad50\ud574\uc11c \uc5bb\uc740 \ud63c\ub3c8\ud589\ub82c\uc758 \uacb0\uacfc\uc5d0 \ub300\ud55c \uc0c1\uc138\ud55c \uc774\ubbf8\uc9c0\ub294 \uc544\ub798\uc640 \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
\"\"<\/p>\n
\uc704\uc758 \uadf8\ub9bc\uc5d0\uc11c \ud45c\uc5d0 \ub2f4\uae34 4\uac1c\uc758 \uac12\uc740 \ubc1c\uc0dd\ud69f\uc218\uc785\ub2c8\ub2e4. TN\uacfc TP\uc758 \uac12\uc740 \uc633\ubc14\ub974\uac8c \uc608\uce21\ud55c \ud69f\uc218\uc774\uace0 FN\uacfc FP\ub294 \uc798\ubabb \uc608\uce21\ud55c \ud69f\uc218\uc785\ub2c8\ub2e4. \uc989, FN\uacfc FP\uac00 0\uc77c\ub54c \ubaa8\ub378\uc740 \uc644\ubcbd\ud558\ub2e4\ub294 \uc758\ubbf8\uc785\ub2c8\ub2e4.<\/p>\n
\uc774\uc81c \uc704\uc758 \ud63c\ub3c8\ud589\ub82c\uc5d0\uc11c \uc815\ubc00\ub3c4(Precision)\uc640 \uc7ac\ud604\ub960(Recall), F1\uc810\uc218\uc5d0 \ub300\ud55c \uc218\uc2dd\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n
<\/p>\n
   <\/span>   <\/span>\"$$Precision=\frac{TP}{TP+FP}, Recall=\frac{TP}{TP+FN},F1=2\times\frac{Precision \times Recall}{Precision+Recall}$$\"<\/p>\n
<\/center><\/p>\n
\uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\ub960\uc774 \uc11c\ub85c \uc0c1\ubc18\uad00\uacc4\uc5d0 \uc788\uc2b5\ub2c8\ub2e4. \uc989, \uc815\ubc00\ub3c4\uac00 \ub192\uc73c\uba74 \uc7ac\ud604\ub960\uc774 \ub5a8\uc5b4\uc9c0\uba70 \uc7ac\ud604\ub960\uc774 \ub192\uc544\uc9c0\uba74 \uc815\ubc00\ub3c4\uac00 \ub5a8\uc5b4\uc9c0\ub294 \uacbd\ud5a5\uc774 \uc788\uc2b5\ub2c8\ub2e4. F1\uc740 \uc774\ub7f0 \uc0c1\ubc18\uad00\uacc4\uc5d0 \uc788\ub294 \uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\ub960\uc744 \ubb36\uc5b4 \ud3c9\uac00\ud558\uace0\uc790 \ud558\ub294 \uc9c0\ud45c\uc785\ub2c8\ub2e4.<\/p>\n
\ube44\ub85d \uc815\ubc00\ub3c4\uc640 \uc7ac\ud604\ub960, F1\uc810\uc218\ub294 \ub9e4\uc6b0 \ub2e8\uc21c\ud574 \uacc4\uc0b0\ud558\uae30 \uc26c\uc6b0\ub098 \ub2e4\uc74c\uc758 \ucf54\ub4dc\ub97c \ud1b5\ud574\uc11c\ub3c4 \uc27d\uac8c \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\r\nfrom sklearn.metrics import precision_score, recall_score, f1_score\r\np = precision_score(y_train_7, y_train_pred)\r\nprint(p)\r\nr = recall_score(y_train_7, y_train_pred)\r\nprint(r)\r\nf1 = f1_score(y_train_7, y_train_pred)\r\nprint(f1)\r\n<\/pre>\n
\r\n0.9153858866303091\r\n0.8841181165203511\r\n0.8994803507632347\r\n<\/pre>\n","protected":false},"excerpt":{"rendered":"
\uc774 \uae00\uc740 \ud55c\ube5b\ubbf8\ub514\uc5b4\uc758 \ud578\uc988\uc628 \uba38\uc2e0\ub7ec\ub2dd\uc744 \uc218\uc5c5\uc790\ub8cc\ub85c\uc368 \ud30c\uc545\ud558\uba74\uc11c \uc774\ud574\ud55c \ubc14\ub97c \uc9e7\uac8c \uc694\uc57d\ud55c \uae00\uc785\ub2c8\ub2e4. \uc694\uc998 \uc774 \ucc45\uc744 \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd\uc744 \ub2e4\uc2dc \uc811\ud558\uace0 \uc788\ub294\ub370, \uccb4\uacc4\uc801\uc774\uace0 \uc88b\uc740 \ub0b4\uc6a9\uc744 \uc81c\uacf5\ud558\uace0 \uc788\uace0, \ub098 \uc790\uc2e0\uc744 \uc704\ud55c \ubcf4\ub2e4 \uba85\ud655\ud55c \uc774\ud574\ub97c \ub3d5\uace0\uc790 \uc774 \uae00\uc744 \uc791\uc131 \uc791\uc131\ud569\ub2c8\ub2e4. \uc694\uc998 \uc81c\uac00 \ube14\ub85c\uadf8\uc5d0 \uc62c\ub9ac\ub294 \uba38\uc2e0\ub7ec\ub2dd \uad00\ub828 \uae00\uc740 \ub300\ubd80\ubd84 \uc774 \ucc45\uc758 \ub0b4\uc6a9\uc5d0 \ub300\ud55c \ub098\ub984\ub300\ub85c\uc758 \ud574\uc11d\uc744 \ud1a0\ub300\ub85c \ud569\ub2c8\ub2e4. \ubcf4\ub2e4 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 … <\/p>\n
\ub354 \ubcf4\uae30 “\ud63c\ub3c8\ud589\ub82c(Confusion Matrix)\uc640 \uc815\ubc00\ub3c4, \uc7ac\ud604\ub960, F1\uc810\uc218”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[131,132],"tags":[],"class_list":["post-9932","post","type-post","status-publish","format-standard","hentry","category-python","category-deep-machine-learning"],"_links":{"self":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/9932","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=9932"}],"version-history":[{"count":13,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/9932\/revisions"}],"predecessor-version":[{"id":9947,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=\/wp\/v2\/posts\/9932\/revisions\/9947"}],"wp:attachment":[{"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9932"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9932"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.gisdeveloper.co.kr\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9932"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}