{"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\/vn\/wiki\/underfitting\/","title":{"rendered":"Thi\u1ebfu trang b\u1ecb"},"content":{"rendered":"<p>Th\u00f4ng tin t\u00f3m t\u1eaft v\u1ec1 Underfitting<\/p>\n<p>Underfitting \u0111\u1ec1 c\u1eadp \u0111\u1ebfn m\u1ed9t m\u00f4 h\u00ecnh th\u1ed1ng k\u00ea ho\u1eb7c thu\u1eadt to\u00e1n h\u1ecdc m\u00e1y kh\u00f4ng th\u1ec3 n\u1eafm b\u1eaft \u0111\u01b0\u1ee3c xu h\u01b0\u1edbng c\u01a1 b\u1ea3n c\u1ee7a d\u1eef li\u1ec7u. Trong b\u1ed1i c\u1ea3nh h\u1ecdc m\u00e1y, n\u00f3 x\u1ea3y ra khi m\u1ed9t m\u00f4 h\u00ecnh qu\u00e1 \u0111\u01a1n gi\u1ea3n \u0111\u1ec3 x\u1eed l\u00fd \u0111\u1ed9 ph\u1ee9c t\u1ea1p c\u1ee7a d\u1eef li\u1ec7u. Do \u0111\u00f3, vi\u1ec7c trang b\u1ecb kh\u00f4ng \u0111\u1ea7y \u0111\u1ee7 s\u1ebd d\u1eabn \u0111\u1ebfn hi\u1ec7u su\u1ea5t k\u00e9m tr\u00ean c\u1ea3 d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n v\u00e0 d\u1eef li\u1ec7u ch\u01b0a \u0111\u01b0\u1ee3c nh\u00ecn th\u1ea5y. Kh\u00e1i ni\u1ec7m n\u00e0y r\u1ea5t quan tr\u1ecdng kh\u00f4ng ch\u1ec9 trong c\u00e1c nghi\u00ean c\u1ee9u l\u00fd thuy\u1ebft m\u00e0 c\u00f2n trong c\u00e1c \u1ee9ng d\u1ee5ng trong th\u1ebf gi\u1edbi th\u1ef1c, bao g\u1ed3m c\u1ea3 nh\u1eefng \u1ee9ng d\u1ee5ng li\u00ean quan \u0111\u1ebfn m\u00e1y ch\u1ee7 proxy.<\/p>\n<h2>L\u1ecbch s\u1eed ngu\u1ed3n g\u1ed1c c\u1ee7a Underfitting v\u00e0 s\u1ef1 \u0111\u1ec1 c\u1eadp \u0111\u1ea7u ti\u00ean v\u1ec1 n\u00f3<\/h2>\n<p>L\u1ecbch s\u1eed c\u1ee7a vi\u1ec7c trang b\u1ecb thi\u1ebfu b\u1eaft ngu\u1ed3n t\u1eeb nh\u1eefng ng\u00e0y \u0111\u1ea7u c\u1ee7a m\u00f4 h\u00ecnh th\u1ed1ng k\u00ea v\u00e0 h\u1ecdc m\u00e1y. B\u1ea3n th\u00e2n thu\u1eadt ng\u1eef n\u00e0y \u0111\u00e3 tr\u1edf n\u00ean n\u1ed5i b\u1eadt v\u1edbi s\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a l\u00fd thuy\u1ebft h\u1ecdc t\u00ednh to\u00e1n v\u00e0o cu\u1ed1i th\u1ebf k\u1ef7 20. N\u00f3 c\u00f3 th\u1ec3 b\u1eaft ngu\u1ed3n t\u1eeb c\u00f4ng tr\u00ecnh c\u1ee7a c\u00e1c nh\u00e0 th\u1ed1ng k\u00ea v\u00e0 to\u00e1n h\u1ecdc, nh\u1eefng ng\u01b0\u1eddi \u0111ang xem x\u00e9t s\u1ef1 \u0111\u00e1nh \u0111\u1ed5i gi\u1eefa \u0111\u1ed9 l\u1ec7ch v\u00e0 ph\u01b0\u01a1ng sai, kh\u00e1m ph\u00e1 c\u00e1c m\u00f4 h\u00ecnh qu\u00e1 \u0111\u01a1n gi\u1ea3n \u0111\u1ec3 th\u1ec3 hi\u1ec7n d\u1eef li\u1ec7u m\u1ed9t c\u00e1ch ch\u00ednh x\u00e1c.<\/p>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 Underfitting: M\u1edf r\u1ed9ng ch\u1ee7 \u0111\u1ec1 Underfitting<\/h2>\n<p>Vi\u1ec7c trang b\u1ecb kh\u00f4ng \u0111\u1ea7y \u0111\u1ee7 x\u1ea3y ra khi m\u1ed9t m\u00f4 h\u00ecnh thi\u1ebfu kh\u1ea3 n\u0103ng (v\u1ec1 \u0111\u1ed9 ph\u1ee9c t\u1ea1p) \u0111\u1ec3 n\u1eafm b\u1eaft c\u00e1c m\u1eabu trong d\u1eef li\u1ec7u. \u0110i\u1ec1u n\u00e0y th\u01b0\u1eddng l\u00e0 do:<\/p>\n<ul>\n<li>S\u1eed d\u1ee5ng m\u00f4 h\u00ecnh tuy\u1ebfn t\u00ednh cho d\u1eef li\u1ec7u phi tuy\u1ebfn.<\/li>\n<li>\u0110\u00e0o t\u1ea1o kh\u00f4ng \u0111\u1ea7y \u0111\u1ee7 ho\u1eb7c r\u1ea5t \u00edt t\u00ednh n\u0103ng.<\/li>\n<li>Ch\u00ednh quy h\u00f3a qu\u00e1 nghi\u00eam ng\u1eb7t.<\/li>\n<\/ul>\n<p>H\u1eadu qu\u1ea3 bao g\u1ed3m:<\/p>\n<ul>\n<li>Kh\u1ea3 n\u0103ng kh\u00e1i qu\u00e1t h\u00f3a k\u00e9m.<\/li>\n<li>Nh\u1eefng d\u1ef1 \u0111o\u00e1n kh\u00f4ng ch\u00ednh x\u00e1c<\/li>\n<li>Kh\u00f4ng n\u1eafm b\u1eaft \u0111\u01b0\u1ee3c c\u00e1c \u0111\u1eb7c \u0111i\u1ec3m thi\u1ebft y\u1ebfu c\u1ee7a d\u1eef li\u1ec7u.<\/li>\n<\/ul>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a Underfitting: C\u00e1ch ho\u1ea1t \u0111\u1ed9ng c\u1ee7a Underfitting<\/h2>\n<p>Vi\u1ec7c trang b\u1ecb ch\u01b0a \u0111\u1ea7y \u0111\u1ee7 li\u00ean quan \u0111\u1ebfn s\u1ef1 sai l\u1ec7ch gi\u1eefa \u0111\u1ed9 ph\u1ee9c t\u1ea1p c\u1ee7a m\u00f4 h\u00ecnh v\u00e0 \u0111\u1ed9 ph\u1ee9c t\u1ea1p c\u1ee7a d\u1eef li\u1ec7u. N\u00f3 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c h\u00ecnh dung nh\u01b0 m\u1ed9t m\u00f4 h\u00ecnh tuy\u1ebfn t\u00ednh ph\u00f9 h\u1ee3p v\u1edbi xu h\u01b0\u1edbng phi tuy\u1ebfn t\u00ednh r\u00f5 r\u00e0ng trong d\u1eef li\u1ec7u. C\u00e1c b\u01b0\u1edbc th\u01b0\u1eddng bao g\u1ed3m:<\/p>\n<ol>\n<li>L\u1ef1a ch\u1ecdn m\u1ed9t m\u00f4 h\u00ecnh \u0111\u01a1n gi\u1ea3n.<\/li>\n<li>Hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh tr\u00ean d\u1eef li\u1ec7u \u0111\u00e3 cho.<\/li>\n<li>Nh\u1eadn th\u1ea5y th\u00e0nh t\u00edch k\u00e9m trong t\u1eadp luy\u1ec7n.<\/li>\n<li>X\u00e1c minh r\u1eb1ng m\u00f4 h\u00ecnh c\u0169ng th\u1ea5t b\u1ea1i tr\u00ean d\u1eef li\u1ec7u m\u1edbi ho\u1eb7c ch\u01b0a nh\u00ecn th\u1ea5y.<\/li>\n<\/ol>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a Underfitting<\/h2>\n<p>C\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a trang b\u1ecb thi\u1ebfu bao g\u1ed3m:<\/p>\n<ul>\n<li><strong>\u0110\u1ed9 l\u1ec7ch cao:<\/strong> C\u00e1c m\u00f4 h\u00ecnh c\u00f3 nh\u1eefng \u0111\u1ecbnh ki\u1ebfn m\u1ea1nh m\u1ebd v\u00e0 kh\u00f4ng th\u1ec3 h\u1ecdc \u0111\u01b0\u1ee3c c\u00e1c m\u00f4 h\u00ecnh c\u01a1 b\u1ea3n.<\/li>\n<li><strong>Ph\u01b0\u01a1ng sai th\u1ea5p:<\/strong> Thay \u0111\u1ed5i t\u1ed1i thi\u1ec3u trong d\u1ef1 \u0111o\u00e1n cho c\u00e1c t\u1eadp hu\u1ea5n luy\u1ec7n kh\u00e1c nhau.<\/li>\n<li><strong>Kh\u00e1i qu\u00e1t h\u00f3a k\u00e9m:<\/strong> Hi\u1ec7u su\u1ea5t y\u1ebfu nh\u01b0 nhau tr\u00ean c\u1ea3 d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n v\u00e0 d\u1eef li\u1ec7u kh\u00f4ng nh\u00ecn th\u1ea5y.<\/li>\n<li><strong>\u0110\u1ed9 nh\u1ea1y v\u1edbi ti\u1ebfng \u1ed3n:<\/strong> Nhi\u1ec5u trong d\u1eef li\u1ec7u c\u00f3 th\u1ec3 \u1ea3nh h\u01b0\u1edfng l\u1edbn \u0111\u1ebfn hi\u1ec7u su\u1ea5t c\u1ee7a m\u1ed9t m\u00f4 h\u00ecnh ch\u01b0a \u0111\u01b0\u1ee3c trang b\u1ecb \u0111\u1ea7y \u0111\u1ee7.<\/li>\n<\/ul>\n<h2>C\u00e1c lo\u1ea1i trang b\u1ecb thi\u1ebfu<\/h2>\n<p>C\u00e1c t\u00ecnh hu\u1ed1ng thi\u1ebfu trang b\u1ecb kh\u00e1c nhau c\u00f3 th\u1ec3 ph\u00e1t sinh t\u00f9y thu\u1ed9c v\u00e0o nhi\u1ec1u y\u1ebfu t\u1ed1 kh\u00e1c nhau. D\u01b0\u1edbi \u0111\u00e2y l\u00e0 b\u1ea3ng minh h\u1ecda m\u1ed9t s\u1ed1 lo\u1ea1i ph\u1ed5 bi\u1ebfn:<\/p>\n<table>\n<thead>\n<tr>\n<th>Lo\u1ea1i trang b\u1ecb thi\u1ebfu<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>K\u1ebft c\u1ea5u kh\u00f4ng ph\u00f9 h\u1ee3p<\/td>\n<td>X\u1ea3y ra khi c\u1ea5u tr\u00fac m\u00f4 h\u00ecnh v\u1ed1n \u0111\u00e3 qu\u00e1 \u0111\u01a1n gi\u1ea3n<\/td>\n<\/tr>\n<tr>\n<td>D\u1eef li\u1ec7u kh\u00f4ng ph\u00f9 h\u1ee3p<\/td>\n<td>Nguy\u00ean nh\u00e2n do d\u1eef li\u1ec7u kh\u00f4ng \u0111\u1ea7y \u0111\u1ee7 ho\u1eb7c kh\u00f4ng li\u00ean quan trong qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o<\/td>\n<\/tr>\n<tr>\n<td>Thi\u1ebfu thu\u1eadt to\u00e1n<\/td>\n<td>Do c\u00e1c thu\u1eadt to\u00e1n v\u1ed1n thi\u00ean v\u1ec1 c\u00e1c m\u00f4 h\u00ecnh \u0111\u01a1n gi\u1ea3n h\u01a1n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng Underfitting, v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p li\u00ean quan \u0111\u1ebfn vi\u1ec7c s\u1eed d\u1ee5ng<\/h2>\n<p>M\u1eb7c d\u00f9 vi\u1ec7c trang b\u1ecb ch\u01b0a \u0111\u1ea7y \u0111\u1ee7 th\u01b0\u1eddng \u0111\u01b0\u1ee3c coi l\u00e0 m\u1ed9t v\u1ea5n \u0111\u1ec1, nh\u01b0ng vi\u1ec7c hi\u1ec3u n\u00f3 c\u00f3 th\u1ec3 h\u01b0\u1edbng d\u1eabn vi\u1ec7c l\u1ef1a ch\u1ecdn m\u00f4 h\u00ecnh v\u00e0 x\u1eed l\u00fd tr\u01b0\u1edbc d\u1eef li\u1ec7u. C\u00e1c gi\u1ea3i ph\u00e1p ph\u1ed5 bi\u1ebfn bao g\u1ed3m:<\/p>\n<ul>\n<li>T\u0103ng \u0111\u1ed9 ph\u1ee9c t\u1ea1p c\u1ee7a m\u00f4 h\u00ecnh<\/li>\n<li>Thu th\u1eadp th\u00eam d\u1eef li\u1ec7u.<\/li>\n<li>Gi\u1ea3m s\u1ef1 ch\u00ednh quy h\u00f3a.<\/li>\n<\/ul>\n<p>C\u00e1c v\u1ea5n \u0111\u1ec1 c\u00f3 th\u1ec3 bao g\u1ed3m:<\/p>\n<ul>\n<li>Kh\u00f3 kh\u0103n trong vi\u1ec7c x\u00e1c \u0111\u1ecbnh m\u1ee9c \u0111\u1ed9 trang b\u1ecb th\u1ea5p.<\/li>\n<li>Kh\u1ea3 n\u0103ng chuy\u1ec3n sang trang b\u1ecb qu\u00e1 m\u1ee9c n\u1ebfu \u0111\u01b0\u1ee3c b\u00f9 \u0111\u1eafp qu\u00e1 m\u1ee9c.<\/li>\n<\/ul>\n<h2>C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 nh\u1eefng so s\u00e1nh kh\u00e1c v\u1edbi c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1<\/h2>\n<table>\n<thead>\n<tr>\n<th>Thu\u1eadt ng\u1eef<\/th>\n<th>\u0110\u1eb7c tr\u01b0ng<\/th>\n<th>So s\u00e1nh v\u1edbi Underfitting<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Thi\u1ebfu trang b\u1ecb<\/td>\n<td>\u0110\u1ed9 l\u1ec7ch cao, ph\u01b0\u01a1ng sai th\u1ea5p<\/td>\n<td>\u2013<\/td>\n<\/tr>\n<tr>\n<td>Trang b\u1ecb qu\u00e1 m\u1ee9c<\/td>\n<td>\u0110\u1ed9 l\u1ec7ch th\u1ea5p, ph\u01b0\u01a1ng sai cao<\/td>\n<td>Ng\u01b0\u1ee3c l\u1ea1i v\u1edbi Underfitting<\/td>\n<\/tr>\n<tr>\n<td>Ph\u00f9 h\u1ee3p t\u1ed1t<\/td>\n<td>Xu h\u01b0\u1edbng c\u00e2n b\u1eb1ng v\u00e0 ph\u01b0\u01a1ng sai<\/td>\n<td>Tr\u1ea1ng th\u00e1i l\u00fd t\u01b0\u1edfng gi\u1eefa Underfitting v\u00e0 Overfitting<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 c\u1ee7a t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn trang b\u1ecb thi\u1ebfu trang b\u1ecb<\/h2>\n<p>Hi\u1ec3u v\u00e0 gi\u1ea3m thi\u1ec3u t\u00ecnh tr\u1ea1ng thi\u1ebfu trang b\u1ecb v\u1eabn l\u00e0 m\u1ed9t l\u0129nh v\u1ef1c \u0111ang \u0111\u01b0\u1ee3c nghi\u00ean c\u1ee9u t\u00edch c\u1ef1c, \u0111\u1eb7c bi\u1ec7t l\u00e0 v\u1edbi s\u1ef1 ra \u0111\u1eddi c\u1ee7a h\u1ecdc s\u00e2u. Xu h\u01b0\u1edbng trong t\u01b0\u01a1ng lai c\u00f3 th\u1ec3 bao g\u1ed3m:<\/p>\n<ul>\n<li>C\u00f4ng c\u1ee5 ch\u1ea9n \u0111o\u00e1n n\u00e2ng cao.<\/li>\n<li>Gi\u1ea3i ph\u00e1p AutoML \u0111\u1ec3 ch\u1ecdn m\u00f4 h\u00ecnh t\u1ed1i \u01b0u.<\/li>\n<li>T\u00edch h\u1ee3p ki\u1ebfn th\u1ee9c chuy\u00ean m\u00f4n c\u1ee7a con ng\u01b0\u1eddi v\u1edbi AI \u0111\u1ec3 gi\u1ea3i quy\u1ebft v\u1ea5n \u0111\u1ec1 thi\u1ebfu trang b\u1ecb.<\/li>\n<\/ul>\n<h2>M\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft nh\u01b0 th\u1ebf n\u00e0o v\u1edbi vi\u1ec7c trang b\u1ecb ch\u01b0a \u0111\u1ea7y \u0111\u1ee7<\/h2>\n<p>C\u00e1c m\u00e1y ch\u1ee7 proxy, ch\u1eb3ng h\u1ea1n nh\u01b0 c\u00e1c m\u00e1y ch\u1ee7 do OneProxy cung c\u1ea5p, c\u00f3 th\u1ec3 \u0111\u00f3ng m\u1ed9t vai tr\u00f2 n\u00e0o \u0111\u00f3 trong b\u1ed1i c\u1ea3nh trang b\u1ecb ch\u01b0a \u0111\u1ea7y \u0111\u1ee7 b\u1eb1ng c\u00e1ch h\u1ed7 tr\u1ee3 thu th\u1eadp d\u1eef li\u1ec7u \u0111a d\u1ea1ng v\u00e0 quan tr\u1ecdng h\u01a1n cho c\u00e1c m\u00f4 h\u00ecnh \u0111\u00e0o t\u1ea1o. Trong tr\u01b0\u1eddng h\u1ee3p khan hi\u1ebfm d\u1eef li\u1ec7u d\u1eabn \u0111\u1ebfn t\u00ecnh tr\u1ea1ng trang b\u1ecb thi\u1ebfu, m\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 gi\u00fap thu th\u1eadp th\u00f4ng tin t\u1eeb nhi\u1ec1u ngu\u1ed3n kh\u00e1c nhau, t\u1eeb \u0111\u00f3 l\u00e0m phong ph\u00fa th\u00eam t\u1eadp d\u1eef li\u1ec7u v\u00e0 c\u00f3 kh\u1ea3 n\u0103ng gi\u1ea3m thi\u1ec3u c\u00e1c v\u1ea5n \u0111\u1ec1 trang b\u1ecb thi\u1ebfu.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<ul>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Statistical_learning_theory\" target=\"_new\" rel=\"noopener nofollow\">L\u00fd thuy\u1ebft h\u1ecdc th\u1ed1ng k\u00ea<\/a><\/li>\n<li><a href=\"http:\/\/scott.fortmann-roe.com\/docs\/BiasVariance.html\" target=\"_new\" rel=\"noopener nofollow\">Hi\u1ec3u v\u1ec1 xu h\u01b0\u1edbng v\u00e0 ph\u01b0\u01a1ng sai<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/vn\/\" target=\"_new\" rel=\"noopener\">Trang web OneProxy<\/a> \u0111\u1ec3 bi\u1ebft th\u00eam th\u00f4ng tin v\u1ec1 c\u00e1ch c\u00e1c m\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 li\u00ean quan \u0111\u1ebfn vi\u1ec7c trang b\u1ecb ch\u01b0a \u0111\u1ea7y \u0111\u1ee7.<\/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\/vn\/wp-json\/wp\/v2\/wiki\/479433","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/479433\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/470761"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=479433"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}