{"id":478306,"date":"2023-08-09T09:30:44","date_gmt":"2023-08-09T09:30:44","guid":{"rendered":""},"modified":"2023-09-05T11:16:29","modified_gmt":"2023-09-05T11:16:29","slug":"overfitting-in-machine-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/overfitting-in-machine-learning\/","title":{"rendered":"Trang b\u1ecb qu\u00e1 m\u1ee9c trong h\u1ecdc m\u00e1y"},"content":{"rendered":"<p>Th\u00f4ng tin t\u00f3m t\u1eaft v\u1ec1 Overfitting trong machine learning: Overfitting trong machine learning \u0111\u1ec1 c\u1eadp \u0111\u1ebfn l\u1ed7i m\u00f4 h\u00ecnh h\u00f3a x\u1ea3y ra khi m\u1ed9t h\u00e0m \u0111\u01b0\u1ee3c c\u0103n ch\u1ec9nh qu\u00e1 ch\u1eb7t ch\u1ebd v\u1edbi m\u1ed9t t\u1eadp h\u1ee3p \u0111i\u1ec3m d\u1eef li\u1ec7u h\u1ea1n ch\u1ebf. N\u00f3 th\u01b0\u1eddng d\u1eabn \u0111\u1ebfn hi\u1ec7u su\u1ea5t k\u00e9m tr\u00ean d\u1eef li\u1ec7u kh\u00f4ng nh\u00ecn th\u1ea5y \u0111\u01b0\u1ee3c, v\u00ec m\u00f4 h\u00ecnh tr\u1edf n\u00ean chuy\u00ean m\u00f4n h\u00f3a cao trong vi\u1ec7c d\u1ef1 \u0111o\u00e1n d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n nh\u01b0ng kh\u00f4ng kh\u00e1i qu\u00e1t h\u00f3a \u0111\u01b0\u1ee3c c\u00e1c v\u00ed d\u1ee5 m\u1edbi.<\/p>\n<h2>L\u1ecbch s\u1eed ngu\u1ed3n g\u1ed1c c\u1ee7a vi\u1ec7c trang b\u1ecb qu\u00e1 m\u1ee9c trong h\u1ecdc m\u00e1y v\u00e0 l\u1ea7n \u0111\u1ea7u ti\u00ean \u0111\u1ec1 c\u1eadp \u0111\u1ebfn n\u00f3<\/h2>\n<p>L\u1ecbch s\u1eed c\u1ee7a vi\u1ec7c trang b\u1ecb qu\u00e1 m\u1ee9c b\u1eaft ngu\u1ed3n t\u1eeb nh\u1eefng ng\u00e0y \u0111\u1ea7u c\u1ee7a m\u00f4 h\u00ecnh th\u1ed1ng k\u00ea v\u00e0 sau \u0111\u00f3 \u0111\u01b0\u1ee3c c\u00f4ng nh\u1eadn l\u00e0 m\u1ed1i quan t\u00e2m l\u1edbn trong h\u1ecdc m\u00e1y. B\u1ea3n th\u00e2n thu\u1eadt ng\u1eef n\u00e0y b\u1eaft \u0111\u1ea7u thu h\u00fat s\u1ef1 ch\u00fa \u00fd v\u00e0o nh\u1eefng n\u0103m 1970 v\u1edbi s\u1ef1 ra \u0111\u1eddi c\u1ee7a c\u00e1c thu\u1eadt to\u00e1n ph\u1ee9c t\u1ea1p h\u01a1n. Hi\u1ec7n t\u01b0\u1ee3ng n\u00e0y \u0111\u00e3 \u0111\u01b0\u1ee3c kh\u00e1m ph\u00e1 trong c\u00e1c t\u00e1c ph\u1ea9m nh\u01b0 \u201cC\u00e1c y\u1ebfu t\u1ed1 c\u1ee7a vi\u1ec7c h\u1ecdc th\u1ed1ng k\u00ea\u201d c\u1ee7a Trevor Hastie, Robert Tibshirani v\u00e0 Jerome Friedman, v\u00e0 \u0111\u00e3 tr\u1edf th\u00e0nh m\u1ed9t kh\u00e1i ni\u1ec7m c\u01a1 b\u1ea3n trong l\u0129nh v\u1ef1c n\u00e0y.<\/p>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 Overfitting trong Machine Learning: M\u1edf r\u1ed9ng ch\u1ee7 \u0111\u1ec1<\/h2>\n<p>Qu\u00e1 kh\u1edbp x\u1ea3y ra khi m\u1ed9t m\u00f4 h\u00ecnh t\u00ecm hi\u1ec3u chi ti\u1ebft v\u00e0 \u0111\u1ed9 nhi\u1ec5u trong d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n \u0111\u1ebfn m\u1ee9c n\u00f3 t\u00e1c \u0111\u1ed9ng ti\u00eau c\u1ef1c \u0111\u1ebfn hi\u1ec7u su\u1ea5t c\u1ee7a n\u00f3 tr\u00ean d\u1eef li\u1ec7u m\u1edbi. \u0110\u00e2y l\u00e0 m\u1ed9t v\u1ea5n \u0111\u1ec1 ph\u1ed5 bi\u1ebfn trong h\u1ecdc m\u00e1y v\u00e0 x\u1ea3y ra trong nhi\u1ec1u t\u00ecnh hu\u1ed1ng kh\u00e1c nhau:<\/p>\n<ul>\n<li><strong>M\u00f4 h\u00ecnh ph\u1ee9c t\u1ea1p:<\/strong> C\u00e1c m\u00f4 h\u00ecnh c\u00f3 qu\u00e1 nhi\u1ec1u tham s\u1ed1 li\u00ean quan \u0111\u1ebfn s\u1ed1 l\u01b0\u1ee3ng quan s\u00e1t c\u00f3 th\u1ec3 d\u1ec5 d\u00e0ng \u0111i\u1ec1u ch\u1ec9nh nhi\u1ec5u trong d\u1eef li\u1ec7u.<\/li>\n<li><strong>D\u1eef li\u1ec7u h\u1ea1n ch\u1ebf:<\/strong> N\u1ebfu kh\u00f4ng c\u00f3 \u0111\u1ee7 d\u1eef li\u1ec7u, m\u1ed9t m\u00f4 h\u00ecnh c\u00f3 th\u1ec3 n\u1eafm b\u1eaft \u0111\u01b0\u1ee3c nh\u1eefng m\u1ed1i t\u01b0\u01a1ng quan gi\u1ea3 kh\u00f4ng ph\u00f9 h\u1ee3p trong b\u1ed1i c\u1ea3nh r\u1ed9ng h\u01a1n.<\/li>\n<li><strong>Thi\u1ebfu ch\u00ednh quy:<\/strong> K\u1ef9 thu\u1eadt ch\u00ednh quy h\u00f3a ki\u1ec3m so\u00e1t \u0111\u1ed9 ph\u1ee9c t\u1ea1p c\u1ee7a m\u00f4 h\u00ecnh. N\u1ebfu kh\u00f4ng c\u00f3 nh\u1eefng \u0111i\u1ec1u n\u00e0y, m\u00f4 h\u00ecnh c\u00f3 th\u1ec3 tr\u1edf n\u00ean qu\u00e1 ph\u1ee9c t\u1ea1p.<\/li>\n<\/ul>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a Overfitting trong Machine Learning: C\u00e1ch th\u1ee9c ho\u1ea1t \u0111\u1ed9ng c\u1ee7a Overfitting<\/h2>\n<p>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a overfitting c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c h\u00ecnh dung b\u1eb1ng c\u00e1ch so s\u00e1nh c\u00e1ch m\u1ed9t m\u00f4 h\u00ecnh ph\u00f9 h\u1ee3p v\u1edbi d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n v\u00e0 c\u00e1ch n\u00f3 ho\u1ea1t \u0111\u1ed9ng tr\u00ean d\u1eef li\u1ec7u kh\u00f4ng nh\u00ecn th\u1ea5y. Th\u00f4ng th\u01b0\u1eddng, khi m\u00f4 h\u00ecnh tr\u1edf n\u00ean ph\u1ee9c t\u1ea1p h\u01a1n:<\/p>\n<ul>\n<li><strong>L\u1ed7i \u0111\u00e0o t\u1ea1o gi\u1ea3m:<\/strong> M\u00f4 h\u00ecnh ph\u00f9 h\u1ee3p h\u01a1n v\u1edbi d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n.<\/li>\n<li><strong>L\u1ed7i x\u00e1c th\u1ef1c ban \u0111\u1ea7u gi\u1ea3m, sau \u0111\u00f3 t\u0103ng:<\/strong> Ban \u0111\u1ea7u, kh\u1ea3 n\u0103ng kh\u00e1i qu\u00e1t h\u00f3a c\u1ee7a m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c c\u1ea3i thi\u1ec7n, nh\u01b0ng khi v\u01b0\u1ee3t qua m\u1ed9t \u0111i\u1ec3m nh\u1ea5t \u0111\u1ecbnh, n\u00f3 b\u1eaft \u0111\u1ea7u t\u00ecm hi\u1ec3u nhi\u1ec5u trong d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n v\u00e0 l\u1ed7i x\u00e1c th\u1ef1c t\u0103ng l\u00ean.<\/li>\n<\/ul>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a Overfitting trong Machine Learning<\/h2>\n<p>C\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a trang b\u1ecb qu\u00e1 m\u1ee9c bao g\u1ed3m:<\/p>\n<ol>\n<li><strong>\u0110\u1ed9 ch\u00ednh x\u00e1c \u0111\u00e0o t\u1ea1o cao:<\/strong> M\u00f4 h\u00ecnh th\u1ef1c hi\u1ec7n \u0111\u1eb7c bi\u1ec7t t\u1ed1t tr\u00ean d\u1eef li\u1ec7u \u0111\u00e0o t\u1ea1o.<\/li>\n<li><strong>Kh\u00e1i qu\u00e1t h\u00f3a k\u00e9m:<\/strong> M\u00f4 h\u00ecnh ho\u1ea1t \u0111\u1ed9ng k\u00e9m tr\u00ean d\u1eef li\u1ec7u m\u1edbi ho\u1eb7c kh\u00f4ng nh\u00ecn th\u1ea5y \u0111\u01b0\u1ee3c.<\/li>\n<li><strong>M\u00f4 h\u00ecnh ph\u1ee9c t\u1ea1p:<\/strong> Trang b\u1ecb qu\u00e1 m\u1ee9c c\u00f3 nhi\u1ec1u kh\u1ea3 n\u0103ng x\u1ea3y ra v\u1edbi c\u00e1c m\u00f4 h\u00ecnh ph\u1ee9c t\u1ea1p kh\u00f4ng c\u1ea7n thi\u1ebft.<\/li>\n<\/ol>\n<h2>C\u00e1c lo\u1ea1i trang b\u1ecb qu\u00e1 m\u1ee9c trong Machine Learning<\/h2>\n<p>C\u00e1c bi\u1ec3u hi\u1ec7n kh\u00e1c nhau c\u1ee7a vi\u1ec7c trang b\u1ecb qu\u00e1 m\u1ee9c c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c ph\u00e2n lo\u1ea1i th\u00e0nh:<\/p>\n<ul>\n<li><strong>Trang b\u1ecb qu\u00e1 m\u1ee9c tham s\u1ed1:<\/strong> Khi m\u00f4 h\u00ecnh c\u00f3 qu\u00e1 nhi\u1ec1u tham s\u1ed1.<\/li>\n<li><strong>C\u1ea5u tr\u00fac qu\u00e1 m\u1ee9c:<\/strong> Khi c\u1ea5u tr\u00fac m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c ch\u1ecdn qu\u00e1 ph\u1ee9c t\u1ea1p.<\/li>\n<li><strong>Qu\u00e1 m\u1ee9c ti\u1ebfng \u1ed3n:<\/strong> Khi m\u00f4 h\u00ecnh h\u1ecdc t\u1eeb nhi\u1ec5u ho\u1eb7c bi\u1ebfn \u0111\u1ed9ng ng\u1eabu nhi\u00ean trong d\u1eef li\u1ec7u.<\/li>\n<\/ul>\n<table>\n<thead>\n<tr>\n<th>Ki\u1ec3u<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Trang b\u1ecb qu\u00e1 m\u1ee9c tham s\u1ed1<\/td>\n<td>C\u00e1c tham s\u1ed1 qu\u00e1 ph\u1ee9c t\u1ea1p, nhi\u1ec5u h\u1ecdc trong d\u1eef li\u1ec7u<\/td>\n<\/tr>\n<tr>\n<td>C\u1ea5u tr\u00fac qu\u00e1 m\u1ee9c<\/td>\n<td>Ki\u1ebfn tr\u00fac c\u1ee7a m\u00f4 h\u00ecnh qu\u00e1 ph\u1ee9c t\u1ea1p \u0111\u1ed1i v\u1edbi m\u1eabu c\u01a1 b\u1ea3n<\/td>\n<\/tr>\n<tr>\n<td>Ti\u1ebfng \u1ed3n qu\u00e1 m\u1ee9c<\/td>\n<td>H\u1ecdc c\u00e1c bi\u1ebfn \u0111\u1ed9ng ng\u1eabu nhi\u00ean, d\u1eabn \u0111\u1ebfn kh\u1ea3 n\u0103ng kh\u00e1i qu\u00e1t h\u00f3a k\u00e9m<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1c c\u00e1ch s\u1eed d\u1ee5ng Overfitting trong Machine Learning, c\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p<\/h2>\n<p>C\u00e1c c\u00e1ch \u0111\u1ec3 gi\u1ea3i quy\u1ebft v\u1ea5n \u0111\u1ec1 trang b\u1ecb qu\u00e1 m\u1ee9c bao g\u1ed3m:<\/p>\n<ul>\n<li><strong>S\u1eed d\u1ee5ng nhi\u1ec1u d\u1eef li\u1ec7u h\u01a1n:<\/strong> Gi\u00fap m\u00f4 h\u00ecnh kh\u00e1i qu\u00e1t h\u00f3a t\u1ed1t h\u01a1n.<\/li>\n<li><strong>\u00c1p d\u1ee5ng c\u00e1c k\u1ef9 thu\u1eadt ch\u00ednh quy h\u00f3a:<\/strong> Gi\u1ed1ng nh\u01b0 ch\u00ednh quy h\u00f3a L1 (Lasso) v\u00e0 L2 (Ridge).<\/li>\n<li><strong>X\u00e1c th\u1ef1c ch\u00e9o:<\/strong> Gi\u00fap \u0111\u00e1nh gi\u00e1 m\u1ee9c \u0111\u1ed9 kh\u00e1i qu\u00e1t c\u1ee7a m\u1ed9t m\u00f4 h\u00ecnh.<\/li>\n<li><strong>\u0110\u01a1n gi\u1ea3n h\u00f3a m\u00f4 h\u00ecnh:<\/strong> Gi\u1ea3m \u0111\u1ed9 ph\u1ee9c t\u1ea1p \u0111\u1ec3 n\u1eafm b\u1eaft t\u1ed1t h\u01a1n m\u00f4 h\u00ecnh c\u01a1 b\u1ea3n.<\/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<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Trang b\u1ecb qu\u00e1 m\u1ee9c<\/td>\n<td>\u0110\u1ed9 ch\u00ednh x\u00e1c hu\u1ea5n luy\u1ec7n cao, t\u00ednh kh\u00e1i qu\u00e1t k\u00e9m<\/td>\n<\/tr>\n<tr>\n<td>Thi\u1ebfu trang b\u1ecb<\/td>\n<td>\u0110\u1ed9 ch\u00ednh x\u00e1c hu\u1ea5n luy\u1ec7n th\u1ea5p, t\u00ednh kh\u00e1i qu\u00e1t k\u00e9m<\/td>\n<\/tr>\n<tr>\n<td>Ph\u00f9 h\u1ee3p t\u1ed1t<\/td>\n<td>C\u00e2n b\u1eb1ng \u0111\u00e0o t\u1ea1o v\u00e0 x\u00e1c nh\u1eadn \u0111\u1ed9 ch\u00ednh x\u00e1c<\/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 vi\u1ec7c trang b\u1ecb qu\u00e1 m\u1ee9c trong h\u1ecdc m\u00e1y<\/h2>\n<p>Nghi\u00ean c\u1ee9u trong t\u01b0\u01a1ng lai v\u1ec1 h\u1ecdc m\u00e1y \u0111ang t\u1eadp trung v\u00e0o c\u00e1c k\u1ef9 thu\u1eadt \u0111\u1ec3 t\u1ef1 \u0111\u1ed9ng ph\u00e1t hi\u1ec7n v\u00e0 s\u1eeda l\u1ed7i qu\u00e1 kh\u1edbp th\u00f4ng qua c\u00e1c ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc th\u00edch \u1ee9ng v\u00e0 l\u1ef1a ch\u1ecdn m\u00f4 h\u00ecnh \u0111\u1ed9ng. Vi\u1ec7c s\u1eed d\u1ee5ng c\u00e1c k\u1ef9 thu\u1eadt ch\u00ednh quy h\u00f3a n\u00e2ng cao, h\u1ecdc t\u1eadp t\u1ed5ng h\u1ee3p v\u00e0 h\u1ecdc t\u1eadp t\u1ed5ng h\u1ee3p l\u00e0 nh\u1eefng l\u0129nh v\u1ef1c \u0111\u1ea7y h\u1ee9a h\u1eb9n \u0111\u1ec3 ch\u1ed1ng l\u1ea1i vi\u1ec7c trang b\u1ecb qu\u00e1 m\u1ee9c.<\/p>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft m\u00e1y ch\u1ee7 proxy v\u1edbi vi\u1ec7c trang b\u1ecb qu\u00e1 m\u1ee9c trong Machine Learning<\/h2>\n<p>C\u00e1c m\u00e1y ch\u1ee7 proxy, gi\u1ed1ng nh\u01b0 c\u00e1c m\u00e1y ch\u1ee7 do OneProxy cung c\u1ea5p, c\u00f3 th\u1ec3 \u0111\u00f3ng vai tr\u00f2 trong vi\u1ec7c ch\u1ed1ng l\u1ea1i t\u00ecnh tr\u1ea1ng trang b\u1ecb qu\u00e1 m\u1ee9c b\u1eb1ng c\u00e1ch cho ph\u00e9p truy c\u1eadp v\u00e0o c\u00e1c b\u1ed9 d\u1eef li\u1ec7u l\u1edbn h\u01a1n, \u0111a d\u1ea1ng h\u01a1n. B\u1eb1ng c\u00e1ch thu th\u1eadp d\u1eef li\u1ec7u t\u1eeb nhi\u1ec1u ngu\u1ed3n v\u00e0 \u0111\u1ecba \u0111i\u1ec3m kh\u00e1c nhau, c\u00f3 th\u1ec3 t\u1ea1o ra m\u1ed9t m\u00f4 h\u00ecnh t\u1ed5ng qu\u00e1t v\u00e0 m\u1ea1nh m\u1ebd h\u01a1n, gi\u1ea3m nguy c\u01a1 kh\u1edbp qu\u00e1 m\u1ee9c.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<ul>\n<li><a href=\"https:\/\/web.stanford.edu\/~hastie\/ElemStatLearn\/\" target=\"_new\" rel=\"noopener nofollow\">C\u00e1c y\u1ebfu t\u1ed1 c\u1ee7a vi\u1ec7c h\u1ecdc th\u1ed1ng k\u00ea<\/a><\/li>\n<li><a href=\"https:\/\/www.overfittingguide.com\" target=\"_new\" rel=\"noopener nofollow\">T\u00ecm hi\u1ec3u v\u1ec1 Overfitting: H\u01b0\u1edbng d\u1eabn tr\u1ef1c quan<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/vn\/\" target=\"_new\" rel=\"noopener\">OneProxy: Cho ph\u00e9p thu th\u1eadp d\u1eef li\u1ec7u cho c\u00e1c m\u00f4 h\u00ecnh m\u1ea1nh m\u1ebd<\/a><\/li>\n<\/ul>","protected":false},"featured_media":469095,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478306","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Overfitting in Machine Learning<\/mark>","faq_items":[{"question":"What is Overfitting in Machine Learning?","answer":"<p>Overfitting in machine learning refers to a modeling error where a function fits too closely to a limited set of data points. It leads to high accuracy on training data but poor performance on unseen data, as the model becomes specialized in predicting the training data but fails to generalize.<\/p>"},{"question":"How Did the Concept of Overfitting Originate?","answer":"<p>The concept of overfitting has its roots in statistical modeling and gained prominence in the 1970s with the advent of more complex algorithms. It has been a central concern in various works, such as \"The Elements of Statistical Learning.\"<\/p>"},{"question":"What Causes Overfitting in Machine Learning Models?","answer":"<p>Overfitting can be caused by factors such as overly complex models with too many parameters, limited data that lead to spurious correlations, and lack of regularization, which helps in controlling the complexity of the model.<\/p>"},{"question":"What Are the Different Types of Overfitting?","answer":"<p>Overfitting can manifest as Parameter Overfitting (overly complex parameters), Structural Overfitting (overly complex model structure), or Noise Overfitting (learning random fluctuations).<\/p>"},{"question":"How Can Overfitting Be Prevented or Addressed?","answer":"<p>Preventing overfitting involves strategies like using more data, applying regularization techniques like L1 and L2, using cross-validation, and simplifying the model to reduce complexity.<\/p>"},{"question":"How is Overfitting Different from Underfitting and a Good Fit?","answer":"<p>Overfitting is characterized by high training accuracy but poor generalization. Underfitting has low training and validation accuracy, and a Good Fit represents a balance between training and validation accuracy.<\/p>"},{"question":"What are the Future Perspectives on Overfitting?","answer":"<p>Future perspectives include research in techniques to automatically detect and correct overfitting through adaptive learning, advanced regularization, ensemble learning, and meta-learning.<\/p>"},{"question":"How Can Proxy Servers like OneProxy Be Associated with Overfitting?","answer":"<p>Proxy servers like OneProxy can help in combating overfitting by allowing access to larger, more diverse datasets. Collecting data from various sources and locations can create a more generalized model, reducing the risk of overfitting.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/478306","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\/478306\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/469095"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=478306"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}