{"id":479433,"date":"2023-08-09T10:40:10","date_gmt":"2023-08-09T10:40:10","guid":{"rendered":""},"modified":"2023-09-05T11:18:48","modified_gmt":"2023-09-05T11:18:48","slug":"underfitting","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/kr\/wiki\/underfitting\/","title":{"rendered":"\uacfc\uc18c\uc801\ud569"},"content":{"rendered":"<p>\uacfc\uc18c\uc801\ud569\uc5d0 \ub300\ud55c \uac04\ub7b5\ud55c \uc815\ubcf4<\/p>\n<p>\uacfc\uc18c\uc801\ud569\uc740 \ub370\uc774\ud130\uc758 \uae30\ubcf8 \ucd94\uc138\ub97c \ud3ec\ucc29\ud560 \uc218 \uc5c6\ub294 \ud1b5\uacc4 \ubaa8\ub378 \ub610\ub294 \uae30\uacc4 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4. \uae30\uacc4 \ud559\uc2b5\uc758 \ub9e5\ub77d\uc5d0\uc11c \uc774\ub294 \ubaa8\ub378\uc774 \ub108\ubb34 \ub2e8\uc21c\ud558\uc5ec \ub370\uc774\ud130\uc758 \ubcf5\uc7a1\uc131\uc744 \ucc98\ub9ac\ud560 \uc218 \uc5c6\uc744 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4. \uacb0\uacfc\uc801\uc73c\ub85c \uacfc\uc18c\uc801\ud569\uc740 \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ubcf4\uc774\uc9c0 \uc54a\ub294 \ub370\uc774\ud130 \ubaa8\ub450\uc5d0\uc11c \uc131\ub2a5\uc774 \uc800\ud558\ub429\ub2c8\ub2e4. \uc774 \uac1c\ub150\uc740 \uc774\ub860\uc801 \uc5f0\uad6c\ubfd0\ub9cc \uc544\ub2c8\ub77c \ud504\ub85d\uc2dc \uc11c\ubc84\uc640 \uad00\ub828\ub41c \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc744 \ud3ec\ud568\ud55c \uc2e4\uc81c \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc5d0\uc11c\ub3c4 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n<h2>\uacfc\uc18c\uc801\ud569\uc758 \uae30\uc6d0\uacfc \ucd5c\ucd08 \uc5b8\uae09\uc758 \uc5ed\uc0ac<\/h2>\n<p>\uacfc\uc18c\uc801\ud569\uc758 \uc5ed\uc0ac\ub294 \ud1b5\uacc4 \ubaa8\ub378\ub9c1 \ubc0f \uae30\uacc4 \ud559\uc2b5 \ucd08\uae30\ub85c \uac70\uc2ac\ub7ec \uc62c\ub77c\uac11\ub2c8\ub2e4. \uc774 \uc6a9\uc5b4 \uc790\uccb4\ub294 20\uc138\uae30 \ud6c4\ubc18\uc5d0 \ucef4\ud4e8\ud130 \ud559\uc2b5 \uc774\ub860\uc774 \ub4f1\uc7a5\ud558\uba74\uc11c \ub450\uac01\uc744 \ub098\ud0c0\ub0c8\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud3b8\ud5a5\uacfc \ubd84\uc0b0 \uc0ac\uc774\uc758 \uade0\ud615\uc744 \uc870\uc0ac\ud558\uace0 \ub370\uc774\ud130\ub97c \uc815\ud655\ud558\uac8c \ud45c\ud604\ud558\uae30\uc5d0\ub294 \ub108\ubb34 \ub2e8\uc21c\ud55c \ubaa8\ub378\uc744 \ud0d0\uc0c9\ud588\ub358 \ud1b5\uacc4\ud559\uc790\uc640 \uc218\ud559\uc790\ub4e4\uc758 \uc791\uc5c5\uc73c\ub85c \uac70\uc2ac\ub7ec \uc62c\ub77c\uac08 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>\uacfc\uc18c\uc801\ud569\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \uc815\ubcf4: \uacfc\uc18c\uc801\ud569 \uc8fc\uc81c \ud655\uc7a5<\/h2>\n<p>\uacfc\uc18c\uc801\ud569\uc740 \ubaa8\ub378\uc774 \ub370\uc774\ud130\uc758 \ud328\ud134\uc744 \ud3ec\ucc29\ud560 \uc218 \uc788\ub294 \uc6a9\ub7c9(\ubcf5\uc7a1\uc131 \uce21\uba74\uc5d0\uc11c)\uc774 \ubd80\uc871\ud560 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4. \uc774\ub294 \uc885\uc885 \ub2e4\uc74c\uacfc \uac19\uc740 \uc774\uc720\ub85c \uc778\ud574 \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\ube44\uc120\ud615 \ub370\uc774\ud130\uc5d0 \uc120\ud615 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n<li>\uad50\uc721\uc774 \ucda9\ubd84\ud558\uc9c0 \uc54a\uac70\ub098 \uae30\ub2a5\uc774 \uac70\uc758 \uc5c6\uc2b5\ub2c8\ub2e4.<\/li>\n<li>\uc9c0\ub098\uce58\uac8c \uc5c4\uaca9\ud55c \uc815\uaddc\ud654.<\/li>\n<\/ul>\n<p>\uacb0\uacfc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\uc77c\ubc18\ud654 \ub2a5\ub825\uc774 \uc88b\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.<\/li>\n<li>\ubd80\uc815\ud655\ud55c \uc608\uce21.<\/li>\n<li>\ub370\uc774\ud130\uc758 \ud544\uc218 \ud2b9\uc131\uc744 \ud3ec\ucc29\ud558\uc9c0 \ubabb\ud588\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>\uacfc\uc18c\uc801\ud569\uc758 \ub0b4\ubd80 \uad6c\uc870: \uacfc\uc18c\uc801\ud569 \uc791\ub3d9 \ubc29\uc2dd<\/h2>\n<p>\uacfc\uc18c\uc801\ud569\uc740 \ubaa8\ub378\uc758 \ubcf5\uc7a1\uc131\uacfc \ub370\uc774\ud130\uc758 \ubcf5\uc7a1\uc131 \uc0ac\uc774\uc758 \ubd88\uc77c\uce58\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4. \uc774\ub294 \ub370\uc774\ud130\uc758 \uba85\ud655\ud55c \ube44\uc120\ud615 \ucd94\uc138\uc5d0 \uc120\ud615 \ubaa8\ub378\uc744 \ub9de\ucd94\ub294 \uac83\uc73c\ub85c \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ub2e4\uc74c \ub2e8\uacc4\uac00 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/p>\n<ol>\n<li>\uac04\ub2e8\ud55c \ubaa8\ub378\uc744 \uc120\ud0dd\ud569\ub2c8\ub2e4.<\/li>\n<li>\uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130\ub85c \ubaa8\ub378\uc744 \ud6c8\ub828\ud569\ub2c8\ub2e4.<\/li>\n<li>\ud6c8\ub828\uc5d0\uc11c \ubd80\uc9c4\ud55c \uc131\uacfc\ub97c \uad00\ucc30\ud569\ub2c8\ub2e4.<\/li>\n<li>\ubcf4\uc774\uc9c0 \uc54a\uac70\ub098 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0\uc11c\ub3c4 \ubaa8\ub378\uc774 \uc2e4\ud328\ud558\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n<h2>\uacfc\uc18c\uc801\ud569\uc758 \uc8fc\uc694 \ud2b9\uc9d5 \ubd84\uc11d<\/h2>\n<p>\uacfc\uc18c\uc801\ud569\uc758 \uc8fc\uc694 \ud2b9\uc9d5\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li><strong>\ub192\uc740 \ubc14\uc774\uc5b4\uc2a4:<\/strong> \ubaa8\ub378\uc740 \uac15\ud55c \uc120\uc785\uacac\uc744 \uac16\uace0 \uc788\uc73c\uba70 \uae30\ubcf8 \ud328\ud134\uc744 \ud559\uc2b5\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ub0ae\uc740 \ucc28\uc774:<\/strong> \ub2e4\uc591\ud55c \ud6c8\ub828 \uc138\ud2b8\uc5d0 \ub300\ud55c \uc608\uce21\uc758 \ubcc0\ud654\uac00 \ucd5c\uc18c\ud654\ub429\ub2c8\ub2e4.<\/li>\n<li><strong>\uc798\ubabb\ub41c \uc77c\ubc18\ud654:<\/strong> \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ubcf4\uc774\uc9c0 \uc54a\ub294 \ub370\uc774\ud130 \ubaa8\ub450\uc5d0\uc11c \uc131\ub2a5\uc774 \ub611\uac19\uc774 \uc57d\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc18c\uc74c\uc5d0 \ub300\ud55c \ubbfc\uac10\ub3c4:<\/strong> \ub370\uc774\ud130\uc758 \ub178\uc774\uc988\ub294 \uacfc\uc18c\uc801\ud569 \ubaa8\ub378\uc758 \uc131\ub2a5\uc5d0 \ud070 \uc601\ud5a5\uc744 \ubbf8\uce60 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>\uacfc\uc18c\uc801\ud569 \uc720\ud615<\/h2>\n<p>\ub2e4\uc591\ud55c \uc694\uc778\uc5d0 \ub530\ub77c \ub2e4\uc591\ud55c \uacfc\uc18c\uc801\ud569 \uc2dc\ub098\ub9ac\uc624\uac00 \ubc1c\uc0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uba87 \uac00\uc9c0 \uc77c\ubc18\uc801\uc778 \uc720\ud615\uc744 \ubcf4\uc5ec\uc8fc\ub294 \ud45c\uc785\ub2c8\ub2e4.<\/p>\n<table>\n<thead>\n<tr>\n<th>\uacfc\uc18c\uc801\ud569 \uc720\ud615<\/th>\n<th>\uc124\uba85<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\uad6c\uc870\uc801 \uacfc\uc18c\uc801\ud569<\/td>\n<td>\ubaa8\ub378 \uad6c\uc870\uac00 \ubcf8\uc9c8\uc801\uc73c\ub85c \ub108\ubb34 \ub2e8\uc21c\ud560 \ub54c \ubc1c\uc0dd\ud569\ub2c8\ub2e4.<\/td>\n<\/tr>\n<tr>\n<td>\ub370\uc774\ud130 \uacfc\uc18c\uc801\ud569<\/td>\n<td>\ud6c8\ub828 \uc911 \ub370\uc774\ud130\uac00 \ubd80\uc871\ud558\uac70\ub098 \uad00\ub828\uc131\uc774 \uc5c6\ub294 \uacbd\uc6b0 \ubc1c\uc0dd<\/td>\n<\/tr>\n<tr>\n<td>\uc54c\uace0\ub9ac\uc998 \uacfc\uc18c\uc801\ud569<\/td>\n<td>\ubcf8\uc9c8\uc801\uc73c\ub85c \ub2e8\uc21c\ud55c \ubaa8\ub378\uc5d0 \ud3b8\ud5a5\ub41c \uc54c\uace0\ub9ac\uc998\uc73c\ub85c \uc778\ud574<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\uacfc\uc18c\uc801\ud569\uc758 \ud65c\uc6a9\ubc29\ubc95\uacfc \ud65c\uc6a9\uc5d0 \ub530\ub978 \ubb38\uc81c\uc810 \ubc0f \ud574\uacb0\ubc29\ubc95<\/h2>\n<p>\uacfc\uc18c\uc801\ud569\uc740 \uc885\uc885 \ubb38\uc81c\ub85c \uac04\uc8fc\ub418\uc9c0\ub9cc \uc774\ub97c \uc774\ud574\ud558\uba74 \ubaa8\ub378 \uc120\ud0dd \ubc0f \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc5d0 \ub3c4\uc6c0\uc774 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 \uc194\ub8e8\uc158\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\ubaa8\ub378 \ubcf5\uc7a1\uc131\uc774 \uc99d\uac00\ud569\ub2c8\ub2e4.<\/li>\n<li>\ub354 \ub9ce\uc740 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud569\ub2c8\ub2e4.<\/li>\n<li>\uc815\uaddc\ud654\ub97c \uc904\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n<p>\ubb38\uc81c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\uacfc\uc18c\uc801\ud569\uc744 \uc2dd\ubcc4\ud558\uae30\uac00 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.<\/li>\n<li>\uacfc\ub3c4\ud558\uac8c \ubcf4\uc0c1\ub418\uba74 \uacfc\uc801\ud569\uc73c\ub85c \uc2a4\uc719\ud560 \uac00\ub2a5\uc131\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>\uc8fc\uc694 \ud2b9\uc9d5 \ubc0f \uae30\ud0c0 \uc720\uc0ac \uc6a9\uc5b4\uc640\uc758 \ube44\uad50<\/h2>\n<table>\n<thead>\n<tr>\n<th>\uc6a9\uc5b4<\/th>\n<th>\ud615\uc9c8<\/th>\n<th>\uacfc\uc18c\uc801\ud569\uacfc\uc758 \ube44\uad50<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\uacfc\uc18c\uc801\ud569<\/td>\n<td>\ub192\uc740 \ubc14\uc774\uc5b4\uc2a4, \ub0ae\uc740 \ubd84\uc0b0<\/td>\n<td>\u2013<\/td>\n<\/tr>\n<tr>\n<td>\uacfc\uc801\ud569<\/td>\n<td>\ub0ae\uc740 \ubc14\uc774\uc5b4\uc2a4, \ub192\uc740 \ubd84\uc0b0<\/td>\n<td>\uacfc\uc18c\uc801\ud569\uc758 \ubc18\ub300<\/td>\n<\/tr>\n<tr>\n<td>\uc801\ud569<\/td>\n<td>\uade0\ud615\uc7a1\ud78c \ud3b8\ud5a5\uacfc \ubd84\uc0b0<\/td>\n<td>\uacfc\uc18c\uc801\ud569\uacfc \uacfc\uc801\ud569 \uc0ac\uc774\uc758 \uc774\uc0c1\uc801\uc778 \uc0c1\ud0dc<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\uacfc\uc18c\uc801\ud569\uacfc \uad00\ub828\ub41c \ubbf8\ub798\uc758 \uad00\uc810\uacfc \uae30\uc220<\/h2>\n<p>\uacfc\uc18c\uc801\ud569\uc744 \uc774\ud574\ud558\uace0 \uc644\ud654\ud558\ub294 \uac83\uc740 \ud2b9\ud788 \ub525\ub7ec\ub2dd\uc758 \ucd9c\ud604\uacfc \ud568\uaed8 \ud65c\ubc1c\ud55c \uc5f0\uad6c \ubd84\uc57c\ub85c \ub0a8\uc544 \uc788\uc2b5\ub2c8\ub2e4. \ubbf8\ub798 \ub3d9\ud5a5\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li>\uace0\uae09 \uc9c4\ub2e8 \ub3c4\uad6c.<\/li>\n<li>\ucd5c\uc801\uc758 \ubaa8\ub378\uc744 \uc120\ud0dd\ud558\ub294 AutoML \uc194\ub8e8\uc158\uc785\ub2c8\ub2e4.<\/li>\n<li>\uacfc\uc18c\uc801\ud569 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574 \uc778\uac04 \uc804\ubb38 \uc9c0\uc2dd\uacfc AI\ub97c \ud1b5\ud569\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>\ud504\ub85d\uc2dc \uc11c\ubc84\ub97c \uc0ac\uc6a9\ud558\uac70\ub098 \uacfc\uc18c\uc801\ud569\uacfc \uc5f0\uacb0\ud558\ub294 \ubc29\ubc95<\/h2>\n<p>OneProxy\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \uac83\uacfc \uac19\uc740 \ud504\ub85d\uc2dc \uc11c\ubc84\ub294 \ud6c8\ub828 \ubaa8\ub378\uc744 \uc704\ud55c \ubcf4\ub2e4 \ub2e4\uc591\ud558\uace0 \uc2e4\uc9c8\uc801\uc778 \ub370\uc774\ud130 \uc218\uc9d1\uc744 \uc9c0\uc6d0\ud568\uc73c\ub85c\uc368 \uacfc\uc18c\uc801\ud569 \uc0c1\ud669\uc5d0\uc11c \uc5ed\ud560\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \ubd80\uc871\uc73c\ub85c \uc778\ud574 \uacfc\uc18c\uc801\ud569\uc774 \ubc1c\uc0dd\ud558\ub294 \uc0c1\ud669\uc5d0\uc11c \ud504\ub85d\uc2dc \uc11c\ubc84\ub294 \ub2e4\uc591\ud55c \uc18c\uc2a4\uc5d0\uc11c \uc815\ubcf4\ub97c \uc218\uc9d1\ud558\uc5ec \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ud48d\ubd80\ud558\uac8c \ud558\uace0 \uc7a0\uc7ac\uc801\uc73c\ub85c \uacfc\uc18c\uc801\ud569 \ubb38\uc81c\ub97c \uc904\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>\uad00\ub828\ub41c \ub9c1\ud06c\ub4e4<\/h2>\n<ul>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Statistical_learning_theory\" target=\"_new\" rel=\"noopener nofollow\">\ud1b5\uacc4\uc801 \ud559\uc2b5 \uc774\ub860<\/a><\/li>\n<li><a href=\"http:\/\/scott.fortmann-roe.com\/docs\/BiasVariance.html\" target=\"_new\" rel=\"noopener nofollow\">\ud3b8\ud5a5\uacfc \ubd84\uc0b0\uc758 \uc774\ud574<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/kr\/\" target=\"_new\" rel=\"noopener\">OneProxy \uc6f9\uc0ac\uc774\ud2b8<\/a> \ud504\ub85d\uc2dc \uc11c\ubc84\uac00 \uacfc\uc18c\uc801\ud569\uacfc \uc5b4\ub5bb\uac8c \uad00\ub828\ub420 \uc218 \uc788\ub294\uc9c0\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \ub0b4\uc6a9\uc744 \uc54c\uc544\ubcf4\uc138\uc694.<\/li>\n<\/ul>","protected":false},"featured_media":470761,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479433","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Underfitting: A Comprehensive Analysis<\/mark>","faq_items":[{"question":"What is Underfitting in the context of machine learning?","answer":"<p>Underfitting refers to a situation where a statistical model or machine learning algorithm is too simple to capture the underlying trend of the data. It leads to poor performance on both the training and unseen data because the model lacks the capacity to learn the complexity of the data.<\/p>"},{"question":"How did the concept of Underfitting originate?","answer":"<p>The concept of underfitting can be traced back to the early works of statisticians and mathematicians who were exploring the trade-offs between bias and variance. It gained prominence with the rise of computational learning theory in the late 20th century.<\/p>"},{"question":"What are the key features of Underfitting?","answer":"<p>The key features of underfitting include high bias, low variance, poor generalization ability, and sensitivity to noise. These features lead to inaccurate predictions and a failure to capture the essential characteristics of the data.<\/p>"},{"question":"What are the common types of Underfitting?","answer":"<p>The common types of underfitting include Structural Underfitting, Data Underfitting, and Algorithmic Underfitting. Each type occurs due to different factors such as the simplicity of the model, insufficient data, or algorithms biased towards simpler models.<\/p>"},{"question":"How can Underfitting be resolved?","answer":"<p>Underfitting can be resolved by increasing the complexity of the model, collecting more or relevant data, and reducing regularization. It requires a careful balance to avoid swinging to the opposite problem of overfitting.<\/p>"},{"question":"How are Proxy Servers like OneProxy associated with Underfitting?","answer":"<p>Proxy servers like OneProxy can be associated with underfitting by assisting in the collection of more diverse data for training models. They help gather information from various sources, thus enriching the dataset and potentially reducing issues related to underfitting.<\/p>"},{"question":"What are the future perspectives and technologies related to Underfitting?","answer":"<p>The future related to underfitting may include advanced diagnostic tools, AutoML solutions to choose optimal models, and the integration of human expertise with AI to address underfitting. Understanding and mitigating underfitting remains an area of active research.<\/p>"},{"question":"How does Underfitting compare with similar terms like Overfitting?","answer":"<p>Underfitting is characterized by high bias and low variance, leading to poor performance on training and unseen data. In contrast, overfitting has low bias and high variance, resulting in a model that performs well on training data but poorly on unseen data. A good fit is an ideal state with a balanced bias and variance.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki\/479433","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki\/479433\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/media\/470761"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/media?parent=479433"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}