{"id":479372,"date":"2023-08-09T10:35:43","date_gmt":"2023-08-09T10:35:43","guid":{"rendered":""},"modified":"2023-09-05T11:18:40","modified_gmt":"2023-09-05T11:18:40","slug":"training-and-test-sets-in-machine-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/training-and-test-sets-in-machine-learning\/","title":{"rendered":"B\u1ed9 hu\u1ea5n luy\u1ec7n v\u00e0 ki\u1ec3m tra trong h\u1ecdc m\u00e1y"},"content":{"rendered":"<p>Th\u00f4ng tin t\u00f3m t\u1eaft v\u1ec1 Training v\u00e0 test sets trong machine learning<\/p>\n<p>Trong h\u1ecdc m\u00e1y, t\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 ki\u1ec3m tra l\u00e0 nh\u1eefng th\u00e0nh ph\u1ea7n quan tr\u1ecdng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 x\u00e2y d\u1ef1ng, x\u00e1c th\u1ef1c v\u00e0 \u0111\u00e1nh gi\u00e1 c\u00e1c m\u00f4 h\u00ecnh. T\u1eadp hu\u1ea5n luy\u1ec7n \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 d\u1ea1y m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y, trong khi t\u1eadp ki\u1ec3m tra \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 \u0111\u00e1nh gi\u00e1 hi\u1ec7u su\u1ea5t c\u1ee7a m\u00f4 h\u00ecnh. C\u00f9ng v\u1edbi nhau, hai b\u1ed9 d\u1eef li\u1ec7u n\u00e0y \u0111\u00f3ng m\u1ed9t vai tr\u00f2 quan tr\u1ecdng trong vi\u1ec7c \u0111\u1ea3m b\u1ea3o hi\u1ec7u su\u1ea5t v\u00e0 hi\u1ec7u su\u1ea5t c\u1ee7a c\u00e1c thu\u1eadt to\u00e1n h\u1ecdc m\u00e1y.<\/p>\n<h2>L\u1ecbch s\u1eed ngu\u1ed3n g\u1ed1c c\u1ee7a T\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 t\u1eadp ki\u1ec3m tra trong h\u1ecdc m\u00e1y v\u00e0 l\u1ea7n \u0111\u1ea7u ti\u00ean \u0111\u1ec1 c\u1eadp \u0111\u1ebfn n\u00f3<\/h2>\n<p>Kh\u00e1i ni\u1ec7m t\u00e1ch d\u1eef li\u1ec7u th\u00e0nh t\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 t\u1eadp ki\u1ec3m tra c\u00f3 ngu\u1ed3n g\u1ed1c t\u1eeb c\u00e1c k\u1ef9 thu\u1eadt x\u00e1c th\u1ef1c v\u00e0 m\u00f4 h\u00ecnh h\u00f3a th\u1ed1ng k\u00ea. N\u00f3 \u0111\u01b0\u1ee3c gi\u1edbi thi\u1ec7u trong h\u1ecdc m\u00e1y v\u00e0o \u0111\u1ea7u nh\u1eefng n\u0103m 1970 khi c\u00e1c nh\u00e0 nghi\u00ean c\u1ee9u nh\u1eadn ra t\u1ea7m quan tr\u1ecdng c\u1ee7a vi\u1ec7c \u0111\u00e1nh gi\u00e1 c\u00e1c m\u00f4 h\u00ecnh tr\u00ean d\u1eef li\u1ec7u ch\u01b0a \u0111\u01b0\u1ee3c nh\u00ecn th\u1ea5y. C\u00e1ch th\u1ef1c h\u00e0nh n\u00e0y gi\u00fap \u0111\u1ea3m b\u1ea3o r\u1eb1ng m\u1ed9t m\u00f4 h\u00ecnh c\u00f3 kh\u1ea3 n\u0103ng kh\u00e1i qu\u00e1t h\u00f3a t\u1ed1t v\u00e0 kh\u00f4ng ch\u1ec9 \u0111\u01a1n thu\u1ea7n l\u00e0 ghi nh\u1edb d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n, m\u1ed9t hi\u1ec7n t\u01b0\u1ee3ng \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 trang b\u1ecb qu\u00e1 m\u1ee9c.<\/p>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 T\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 b\u00e0i ki\u1ec3m tra trong h\u1ecdc m\u00e1y. M\u1edf r\u1ed9ng ch\u1ee7 \u0111\u1ec1 T\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 ki\u1ec3m tra trong h\u1ecdc m\u00e1y<\/h2>\n<p>B\u1ed9 \u0111\u00e0o t\u1ea1o v\u00e0 ki\u1ec3m tra l\u00e0 nh\u1eefng ph\u1ea7n kh\u00f4ng th\u1ec3 thi\u1ebfu trong quy tr\u00ecnh h\u1ecdc m\u00e1y:<\/p>\n<ul>\n<li><strong>T\u1eadp hu\u1ea5n luy\u1ec7n<\/strong>: D\u00f9ng \u0111\u1ec3 hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh. N\u00f3 bao g\u1ed3m c\u1ea3 d\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o v\u00e0 \u0111\u1ea7u ra d\u1ef1 ki\u1ebfn t\u01b0\u01a1ng \u1ee9ng.<\/li>\n<li><strong>T\u1eadp ki\u1ec3m tra<\/strong>: \u0110\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 \u0111\u00e1nh gi\u00e1 hi\u1ec7u su\u1ea5t c\u1ee7a m\u00f4 h\u00ecnh tr\u00ean d\u1eef li\u1ec7u ch\u01b0a nh\u00ecn th\u1ea5y. N\u00f3 c\u0169ng ch\u1ee9a d\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o c\u00f9ng v\u1edbi \u0111\u1ea7u ra d\u1ef1 ki\u1ebfn, tuy nhi\u00ean d\u1eef li\u1ec7u n\u00e0y kh\u00f4ng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong qu\u00e1 tr\u00ecnh hu\u1ea5n luy\u1ec7n.<\/li>\n<\/ul>\n<h3>B\u1ed9 x\u00e1c th\u1ef1c<\/h3>\n<p>M\u1ed9t s\u1ed1 tri\u1ec3n khai c\u0169ng bao g\u1ed3m m\u1ed9t t\u1eadp h\u1ee3p x\u00e1c th\u1ef1c, \u0111\u01b0\u1ee3c chia nh\u1ecf h\u01a1n t\u1eeb t\u1eadp hu\u1ea5n luy\u1ec7n, \u0111\u1ec3 tinh ch\u1ec9nh c\u00e1c tham s\u1ed1 m\u00f4 h\u00ecnh.<\/p>\n<h3>Trang b\u1ecb qu\u00e1 m\u1ee9c v\u00e0 trang b\u1ecb thi\u1ebfu<\/h3>\n<p>Vi\u1ec7c ph\u00e2n chia d\u1eef li\u1ec7u h\u1ee3p l\u00fd gi\u00fap tr\u00e1nh vi\u1ec7c trang b\u1ecb qu\u00e1 m\u1ee9c (trong \u0111\u00f3 m\u00f4 h\u00ecnh ho\u1ea1t \u0111\u1ed9ng t\u1ed1t tr\u00ean d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n nh\u01b0ng k\u00e9m tr\u00ean d\u1eef li\u1ec7u kh\u00f4ng nh\u00ecn th\u1ea5y) v\u00e0 trang b\u1ecb thi\u1ebfu (trong \u0111\u00f3 m\u00f4 h\u00ecnh ho\u1ea1t \u0111\u1ed9ng k\u00e9m tr\u00ean c\u1ea3 d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n v\u00e0 d\u1eef li\u1ec7u kh\u00f4ng nh\u00ecn th\u1ea5y).<\/p>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a T\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 t\u1eadp ki\u1ec3m tra trong h\u1ecdc m\u00e1y. C\u00e1ch th\u1ee9c ho\u1ea1t \u0111\u1ed9ng c\u1ee7a T\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 b\u00e0i ki\u1ec3m tra trong h\u1ecdc m\u00e1y<\/h2>\n<p>C\u00e1c t\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 ki\u1ec3m tra th\u01b0\u1eddng \u0111\u01b0\u1ee3c chia t\u1eeb m\u1ed9t t\u1eadp d\u1eef li\u1ec7u duy nh\u1ea5t:<\/p>\n<ul>\n<li>T\u1eadp hu\u1ea5n luy\u1ec7n: Th\u01b0\u1eddng ch\u1ee9a 60-80% d\u1eef li\u1ec7u.<\/li>\n<li>B\u1ed9 ki\u1ec3m tra: Bao g\u1ed3m 20-40% d\u1eef li\u1ec7u c\u00f2n l\u1ea1i.<\/li>\n<\/ul>\n<p>M\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n tr\u00ean t\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 \u0111\u01b0\u1ee3c \u0111\u00e1nh gi\u00e1 tr\u00ean t\u1eadp ki\u1ec3m tra, \u0111\u1ea3m b\u1ea3o \u0111\u00e1nh gi\u00e1 kh\u00e1ch quan.<\/p>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a t\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 ki\u1ec3m tra trong h\u1ecdc m\u00e1y<\/h2>\n<p>C\u00e1c t\u00ednh n\u0103ng ch\u00ednh bao g\u1ed3m:<\/p>\n<ul>\n<li><strong>S\u1ef1 \u0111\u00e1nh \u0111\u1ed5i \u0111\u1ed9 l\u1ec7ch-ph\u01b0\u01a1ng sai<\/strong>: C\u00e2n b\u1eb1ng \u0111\u1ed9 ph\u1ee9c t\u1ea1p \u0111\u1ec3 tr\u00e1nh trang b\u1ecb qu\u00e1 m\u1ee9c ho\u1eb7c thi\u1ebfu trang b\u1ecb.<\/li>\n<li><strong>X\u00e1c th\u1ef1c ch\u00e9o<\/strong>: M\u1ed9t k\u1ef9 thu\u1eadt \u0111\u1ec3 \u0111\u00e1nh gi\u00e1 c\u00e1c m\u00f4 h\u00ecnh s\u1eed d\u1ee5ng c\u00e1c t\u1eadp h\u1ee3p con d\u1eef li\u1ec7u kh\u00e1c nhau.<\/li>\n<li><strong>S\u1ef1 kh\u00e1i qu\u00e1t<\/strong>: \u0110\u1ea3m b\u1ea3o m\u00f4 h\u00ecnh ho\u1ea1t \u0111\u1ed9ng t\u1ed1t tr\u00ean d\u1eef li\u1ec7u kh\u00f4ng nh\u00ecn th\u1ea5y.<\/li>\n<\/ul>\n<h2>Vi\u1ebft nh\u1eefng lo\u1ea1i t\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 ki\u1ec3m tra n\u00e0o t\u1ed3n t\u1ea1i trong h\u1ecdc m\u00e1y. S\u1eed d\u1ee5ng b\u1ea3ng v\u00e0 danh s\u00e1ch \u0111\u1ec3 vi\u1ebft<\/h2>\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>Chia ng\u1eabu nhi\u00ean<\/td>\n<td>Ph\u00e2n chia ng\u1eabu nhi\u00ean d\u1eef li\u1ec7u th\u00e0nh t\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 t\u1eadp ki\u1ec3m tra<\/td>\n<\/tr>\n<tr>\n<td>Ph\u00e2n chia ph\u00e2n t\u1ea7ng<\/td>\n<td>\u0110\u1ea3m b\u1ea3o s\u1ef1 \u0111\u1ea1i di\u1ec7n t\u01b0\u01a1ng x\u1ee9ng c\u1ee7a c\u00e1c l\u1edbp trong c\u1ea3 hai b\u1ed9<\/td>\n<\/tr>\n<tr>\n<td>Ph\u00e2n chia chu\u1ed7i th\u1eddi gian<\/td>\n<td>Ph\u00e2n chia d\u1eef li\u1ec7u theo th\u1ee9 t\u1ef1 th\u1eddi gian cho d\u1eef li\u1ec7u ph\u1ee5 thu\u1ed9c th\u1eddi gian<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng B\u1ed9 \u0111\u00e0o t\u1ea1o v\u00e0 ki\u1ec3m tra trong h\u1ecdc m\u00e1y, c\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p li\u00ean quan \u0111\u1ebfn vi\u1ec7c s\u1eed d\u1ee5ng<\/h2>\n<p>Vi\u1ec7c s\u1eed d\u1ee5ng t\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 t\u1eadp ki\u1ec3m tra trong h\u1ecdc m\u00e1y c\u00f3 nhi\u1ec1u th\u00e1ch th\u1ee9c kh\u00e1c nhau:<\/p>\n<ul>\n<li><strong>R\u00f2 r\u1ec9 d\u1eef li\u1ec7u<\/strong>: \u0110\u1ea3m b\u1ea3o kh\u00f4ng c\u00f3 th\u00f4ng tin n\u00e0o t\u1eeb b\u1ed9 ki\u1ec3m tra b\u1ecb r\u00f2 r\u1ec9 v\u00e0o qu\u00e1 tr\u00ecnh hu\u1ea5n luy\u1ec7n.<\/li>\n<li><strong>D\u1eef li\u1ec7u m\u1ea5t c\u00e2n b\u1eb1ng<\/strong>: X\u1eed l\u00fd c\u00e1c t\u1eadp d\u1eef li\u1ec7u c\u00f3 bi\u1ec3u di\u1ec5n l\u1edbp kh\u00f4ng c\u00e2n x\u1ee9ng.<\/li>\n<li><strong>chi\u1ec1u cao<\/strong>: X\u1eed l\u00fd d\u1eef li\u1ec7u c\u00f3 s\u1ed1 l\u01b0\u1ee3ng l\u1edbn c\u00e1c t\u00ednh n\u0103ng.<\/li>\n<\/ul>\n<p>C\u00e1c gi\u1ea3i ph\u00e1p bao g\u1ed3m ti\u1ec1n x\u1eed l\u00fd c\u1ea9n th\u1eadn, s\u1eed d\u1ee5ng chi\u1ebfn l\u01b0\u1ee3c ph\u00e2n t\u00e1ch th\u00edch h\u1ee3p v\u00e0 s\u1eed d\u1ee5ng c\u00e1c k\u1ef9 thu\u1eadt nh\u01b0 l\u1ea5y m\u1eabu l\u1ea1i cho d\u1eef li\u1ec7u kh\u00f4ng c\u00e2n b\u1eb1ng.<\/p>\n<h2>C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 so s\u00e1nh kh\u00e1c v\u1edbi c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1 d\u01b0\u1edbi d\u1ea1ng b\u1ea3ng v\u00e0 danh s\u00e1ch<\/h2>\n<table>\n<thead>\n<tr>\n<th>Thu\u1eadt ng\u1eef<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>T\u1eadp hu\u1ea5n luy\u1ec7n<\/td>\n<td>D\u00f9ng \u0111\u1ec3 hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh<\/td>\n<\/tr>\n<tr>\n<td>T\u1eadp ki\u1ec3m tra<\/td>\n<td>D\u00f9ng \u0111\u1ec3 \u0111\u00e1nh gi\u00e1 m\u00f4 h\u00ecnh<\/td>\n<\/tr>\n<tr>\n<td>B\u1ed9 x\u00e1c th\u1ef1c<\/td>\n<td>\u0110\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 \u0111i\u1ec1u ch\u1ec9nh c\u00e1c tham s\u1ed1 m\u00f4 h\u00ecnh<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1c quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 c\u1ee7a t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn b\u1ed9 \u0111\u00e0o t\u1ea1o v\u00e0 b\u00e0i ki\u1ec3m tra trong h\u1ecdc m\u00e1y<\/h2>\n<p>Nh\u1eefng ti\u1ebfn b\u1ed9 trong t\u01b0\u01a1ng lai trong l\u0129nh v\u1ef1c n\u00e0y c\u00f3 th\u1ec3 bao g\u1ed3m:<\/p>\n<ul>\n<li><strong>T\u00e1ch d\u1eef li\u1ec7u t\u1ef1 \u0111\u1ed9ng<\/strong>: S\u1eed d\u1ee5ng AI \u0111\u1ec3 ph\u00e2n chia d\u1eef li\u1ec7u t\u1ed1i \u01b0u.<\/li>\n<li><strong>Th\u1eed nghi\u1ec7m th\u00edch \u1ee9ng<\/strong>: T\u1ea1o c\u00e1c b\u1ed9 th\u1eed nghi\u1ec7m ph\u00e1t tri\u1ec3n c\u00f9ng v\u1edbi m\u00f4 h\u00ecnh.<\/li>\n<li><strong>Quy\u1ec1n ri\u00eang t\u01b0 d\u1eef li\u1ec7u<\/strong>: \u0110\u1ea3m b\u1ea3o r\u1eb1ng qu\u00e1 tr\u00ecnh ph\u00e2n t\u00e1ch t\u00f4n tr\u1ecdng c\u00e1c r\u00e0ng bu\u1ed9c v\u1ec1 quy\u1ec1n ri\u00eang t\u01b0.<\/li>\n<\/ul>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft m\u00e1y ch\u1ee7 proxy v\u1edbi T\u1eadp \u0111\u00e0o t\u1ea1o v\u00e0 ki\u1ec3m tra trong h\u1ecdc m\u00e1y<\/h2>\n<p>C\u00e1c m\u00e1y ch\u1ee7 proxy nh\u01b0 OneProxy c\u00f3 th\u1ec3 t\u1ea1o \u0111i\u1ec1u ki\u1ec7n truy c\u1eadp v\u00e0o d\u1eef li\u1ec7u \u0111a d\u1ea1ng v\u00e0 \u0111\u01b0\u1ee3c ph\u00e2n b\u1ed5 theo \u0111\u1ecba l\u00fd, \u0111\u1ea3m b\u1ea3o r\u1eb1ng c\u00e1c t\u1eadp hu\u1ea5n luy\u1ec7n v\u00e0 ki\u1ec3m tra \u0111\u1ea1i di\u1ec7n cho nhi\u1ec1u t\u00ecnh hu\u1ed1ng th\u1ef1c t\u1ebf kh\u00e1c nhau. \u0110i\u1ec1u n\u00e0y c\u00f3 th\u1ec3 h\u1ed7 tr\u1ee3 trong vi\u1ec7c t\u1ea1o ra c\u00e1c m\u00f4 h\u00ecnh m\u1ea1nh m\u1ebd h\u01a1n v\u00e0 c\u00f3 t\u00ednh kh\u00e1i qu\u00e1t t\u1ed1t h\u01a1n.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<ul>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/cross_validation.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-learn: Ph\u00e2n chia \u0111\u00e0o t\u1ea1o\/ki\u1ec3m tra<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/vn\/\" target=\"_new\" rel=\"noopener\">OneProxy: T\u0103ng c\u01b0\u1eddng thu th\u1eadp d\u1eef li\u1ec7u<\/a><\/li>\n<li><a href=\"https:\/\/machinelearningmastery.com\" target=\"_new\" rel=\"noopener nofollow\">L\u00e0m ch\u1ee7 h\u1ecdc m\u00e1y: T\u00ecm hi\u1ec3u v\u1ec1 \u0111\u00e0o t\u1ea1o, x\u00e1c th\u1ef1c, ph\u00e2n t\u00e1ch th\u1eed nghi\u1ec7m<\/a><\/li>\n<\/ul>","protected":false},"featured_media":470722,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479372","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Training and Test Sets in Machine Learning<\/mark>","faq_items":[{"question":"What are Training and Test Sets in Machine Learning?","answer":"<p>Training and test sets are two separate data groups used in machine learning. The training set is used to train the model, teaching it to recognize patterns and make predictions, while the test set is used to evaluate how well the model has learned and how it performs on unseen data.<\/p>"},{"question":"How Did the Concept of Training and Test Sets Originate in Machine Learning?","answer":"<p>The concept of dividing data into training and test sets emerged in the early 1970s in the field of statistical modeling. It was introduced to machine learning to avoid overfitting, ensuring that the model generalizes well on unseen data.<\/p>"},{"question":"What is the Importance of Properly Dividing Training and Test Sets?","answer":"<p>Proper division of training and test sets ensures that the model is unbiased, helping to avoid overfitting (where the model performs well on the training data but poorly on new data) and underfitting (where the model performs poorly in general).<\/p>"},{"question":"How are Training and Test Sets Structured?","answer":"<p>Typically, the training set contains 60-80% of the data, and the test set comprises the remaining 20-40%. This division allows the model to be trained on a substantial portion of the data while still being tested on unseen data to evaluate its performance.<\/p>"},{"question":"What Are Some Common Types of Training and Test Set Splits?","answer":"<p>Some common types include Random Split, where data is randomly divided; Stratified Split, ensuring proportionate class representation in both sets; and Time Series Split, where data is divided chronologically.<\/p>"},{"question":"What are the Future Perspectives Related to Training and Test Sets in Machine Learning?","answer":"<p>Future advancements may include automated data splitting using AI, adaptive testing with evolving test sets, and incorporating data privacy considerations in the splitting process.<\/p>"},{"question":"How Can Proxy Servers like OneProxy be Associated with Training and Test Sets in Machine Learning?","answer":"<p>Proxy servers such as OneProxy can provide access to diverse and geographically distributed data, ensuring that training and test sets are representative of various real-world scenarios. This aids in creating more robust and well-generalized models.<\/p>"},{"question":"What are Some Challenges and Solutions Related to the Use of Training and Test Sets in Machine Learning?","answer":"<p>Challenges include data leakage, imbalanced data, and high dimensionality. Solutions can involve careful preprocessing, proper splitting strategies, and employing techniques like resampling for imbalanced data.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/479372","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\/479372\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/470722"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=479372"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}