{"id":476484,"date":"2023-08-09T07:29:55","date_gmt":"2023-08-09T07:29:55","guid":{"rendered":""},"modified":"2023-09-05T11:12:51","modified_gmt":"2023-09-05T11:12:51","slug":"cross-validation","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/ar\/wiki\/cross-validation\/","title":{"rendered":"\u0639\u0628\u0631 \u0627\u0644\u0645\u0635\u0627\u062f\u0642\u0629"},"content":{"rendered":"<p>\u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0627\u0644\u0635\u062d\u0629 \u0647\u0648 \u062a\u0642\u0646\u064a\u0629 \u0625\u062d\u0635\u0627\u0626\u064a\u0629 \u0642\u0648\u064a\u0629 \u062a\u0633\u062a\u062e\u062f\u0645 \u0644\u062a\u0642\u064a\u064a\u0645 \u0623\u062f\u0627\u0621 \u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a \u0648\u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u062f\u0642\u062a\u0647\u0627. \u0641\u0647\u0648 \u064a\u0644\u0639\u0628 \u062f\u0648\u0631\u064b\u0627 \u062d\u0627\u0633\u0645\u064b\u0627 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\u0627\u0644\u0646\u0645\u0648\u0630\u062c.<\/p>\n<\/li>\n<li>\n<p><strong>\u0636\u0628\u0637 \u0627\u0644\u0645\u0639\u0644\u0645\u0629 \u0627\u0644\u0623\u0645\u062b\u0644<\/strong>: \u064a\u0633\u0627\u0639\u062f \u0641\u064a \u0627\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0627\u0644\u0645\u0639\u0644\u0645\u0627\u062a \u0627\u0644\u0641\u0627\u0626\u0642\u0629 \u0627\u0644\u0645\u062b\u0627\u0644\u064a\u0629 \u0644\u0644\u0646\u0645\u0648\u0630\u062c\u060c \u0645\u0645\u0627 \u064a\u0639\u0632\u0632 \u0642\u062f\u0631\u062a\u0647 \u0627\u0644\u062a\u0646\u0628\u0624\u064a\u0629.<\/p>\n<\/li>\n<li>\n<p><strong>\u0627\u0644\u0645\u062a\u0627\u0646\u0629<\/strong>: \u064a\u0633\u0627\u0639\u062f \u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0627\u0644\u0635\u062d\u0629 \u0641\u064a \u062a\u062d\u062f\u064a\u062f \u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062a\u064a \u062a\u062d\u0642\u0642 \u0623\u062f\u0627\u0621\u064b \u062c\u064a\u062f\u064b\u0627 \u0628\u0627\u0633\u062a\u0645\u0631\u0627\u0631 \u0641\u064a \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0641\u0631\u0639\u064a\u0629 \u0645\u062e\u062a\u0644\u0641\u0629 \u0645\u0646 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a\u060c \u0645\u0645\u0627 \u064a\u062c\u0639\u0644\u0647\u0627 \u0623\u0643\u062b\u0631 \u0642\u0648\u0629.<\/p>\n<\/li>\n<li>\n<p><strong>\u0643\u0641\u0627\u0621\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a<\/strong>: \u0625\u0646\u0647 \u064a\u0632\u064a\u062f \u0645\u0646 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0645\u062a\u0627\u062d\u0629\u060c \u062d\u064a\u062b \u064a\u062a\u0645 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0643\u0644 \u0646\u0642\u0637\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0644\u0643\u0644 \u0645\u0646 \u0627\u0644\u062a\u062f\u0631\u064a\u0628 \u0648\u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0627\u0644\u0635\u062d\u0629.<\/p>\n<\/li>\n<\/ol>\n<h2>\u0623\u0646\u0648\u0627\u0639 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644<\/h2>\n<p>\u0647\u0646\u0627\u0643 \u0639\u062f\u0629 \u0623\u0646\u0648\u0627\u0639 \u0645\u0646 \u062a\u0642\u0646\u064a\u0627\u062a \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0642\u0627\u0637\u0639\u060c \u0648\u0644\u0643\u0644 \u0645\u0646\u0647\u0627 \u0646\u0642\u0627\u0637 \u0642\u0648\u062a\u0647\u0627 \u0648\u062a\u0637\u0628\u064a\u0642\u0627\u062a\u0647\u0627. \u0641\u064a\u0645\u0627 \u064a\u0644\u064a \u0628\u0639\u0636 \u0645\u0646\u0647\u0627 \u0634\u0627\u0626\u0639\u0629 \u0627\u0644\u0627\u0633\u062a\u062e\u062f\u0627\u0645:<\/p>\n<ol>\n<li>\n<p><strong>\u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0635\u062d\u0629 K-Fold<\/strong>: \u064a\u062a\u0645 \u062a\u0642\u0633\u064a\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0641\u0631\u0639\u064a\u0629 k\u060c \u0648\u064a\u062a\u0645 \u062a\u062f\u0631\u064a\u0628 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0648\u062a\u0642\u064a\u064a\u0645\u0647 k \u0645\u0631\u0627\u062a\u060c \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0637\u064a\u0629 \u0645\u062e\u062a\u0644\u0641\u0629 \u0643\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u062e\u062a\u0628\u0627\u0631 \u0641\u064a \u0643\u0644 \u062a\u0643\u0631\u0627\u0631.<\/p>\n<\/li>\n<li>\n<p><strong>\u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0635\u062d\u0629 \u0627\u0644\u0645\u063a\u0627\u062f\u0631\u0629 \u0644\u0645\u0631\u0629 \u0648\u0627\u062d\u062f\u0629 (LOOCV)<\/strong>: \u062d\u0627\u0644\u0629 \u062e\u0627\u0635\u0629 \u0645\u0646 K-Fold CV \u062d\u064a\u062b \u062a\u0633\u0627\u0648\u064a k \u0639\u062f\u062f \u0646\u0642\u0627\u0637 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0641\u064a \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a. \u0641\u064a \u0643\u0644 \u062a\u0643\u0631\u0627\u0631\u060c \u064a\u062a\u0645 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0646\u0642\u0637\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0648\u0627\u062d\u062f\u0629 \u0641\u0642\u0637 \u0644\u0644\u0627\u062e\u062a\u0628\u0627\u0631\u060c \u0628\u064a\u0646\u0645\u0627 \u064a\u062a\u0645 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0628\u0627\u0642\u064a \u0644\u0644\u062a\u062f\u0631\u064a\u0628.<\/p>\n<\/li>\n<li>\n<p><strong>\u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0635\u062d\u0629 \u0627\u0644\u0637\u0628\u0642\u0627\u062a K-Fold<\/strong>: \u064a\u0636\u0645\u0646 \u0623\u0646 \u0643\u0644 \u0637\u064a\u0629 \u062a\u062d\u0627\u0641\u0638 \u0639\u0644\u0649 \u0646\u0641\u0633 \u062a\u0648\u0632\u064a\u0639 \u0627\u0644\u0641\u0626\u0629 \u0645\u062b\u0644 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0623\u0635\u0644\u064a\u0629\u060c \u0648\u0647\u0648 \u0623\u0645\u0631 \u0645\u0641\u064a\u062f \u0628\u0634\u0643\u0644 \u062e\u0627\u0635 \u0639\u0646\u062f \u0627\u0644\u062a\u0639\u0627\u0645\u0644 \u0645\u0639 \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u063a\u064a\u0631 \u0627\u0644\u0645\u062a\u0648\u0627\u0632\u0646\u0629.<\/p>\n<\/li>\n<li>\n<p><strong>\u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0635\u062d\u0629 \u0627\u0644\u0633\u0644\u0627\u0633\u0644 \u0627\u0644\u0632\u0645\u0646\u064a\u0629<\/strong>: \u0645\u0635\u0645\u0645 \u062e\u0635\u064a\u0635\u064b\u0627 \u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0633\u0644\u0627\u0633\u0644 \u0627\u0644\u0632\u0645\u0646\u064a\u0629\u060c \u062d\u064a\u062b \u064a\u062a\u0645 \u062a\u0642\u0633\u064a\u0645 \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0627\u0644\u062a\u062f\u0631\u064a\u0628 \u0648\u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u0627\u0644\u062a\u0631\u062a\u064a\u0628 \u0627\u0644\u0632\u0645\u0646\u064a.<\/p>\n<\/li>\n<\/ol>\n<h2>\u0637\u0631\u0642 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0642\u0627\u0637\u0639 \u0648\u0627\u0644\u0645\u0634\u0643\u0644\u0627\u062a \u0648\u062d\u0644\u0648\u0644\u0647\u0627 \u0627\u0644\u0645\u062a\u0639\u0644\u0642\u0629 \u0628\u0627\u0644\u0627\u0633\u062a\u062e\u062f\u0627\u0645.<\/h2>\n<p>\u064a\u062a\u0645 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644 \u0639\u0644\u0649 \u0646\u0637\u0627\u0642 \u0648\u0627\u0633\u0639 \u0641\u064a \u0633\u064a\u0646\u0627\u0631\u064a\u0648\u0647\u0627\u062a \u0645\u062e\u062a\u0644\u0641\u0629\u060c \u0645\u062b\u0644:<\/p>\n<ol>\n<li>\n<p><strong>\u0627\u062e\u062a\u064a\u0627\u0631 \u0627\u0644\u0646\u0645\u0648\u0630\u062c<\/strong>: \u064a\u0633\u0627\u0639\u062f \u0641\u064a \u0645\u0642\u0627\u0631\u0646\u0629 \u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u0645\u062e\u062a\u0644\u0641\u0629 \u0648\u0627\u062e\u062a\u064a\u0627\u0631 \u0627\u0644\u0623\u0641\u0636\u0644 \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u0623\u062f\u0627\u0626\u0647\u0627.<\/p>\n<\/li>\n<li>\n<p><strong>\u0636\u0628\u0637 \u0627\u0644\u0645\u0639\u0644\u0645\u0629 \u0627\u0644\u0641\u0627\u0626\u0642\u0629<\/strong>: \u064a\u0633\u0627\u0639\u062f \u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0627\u0644\u0635\u062d\u0629 \u0641\u064a \u0627\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0627\u0644\u0642\u064a\u0645 \u0627\u0644\u0645\u062b\u0644\u0649 \u0644\u0644\u0645\u0639\u0644\u0645\u0627\u062a \u0627\u0644\u0641\u0627\u0626\u0642\u0629\u060c \u0648\u0627\u0644\u062a\u064a \u062a\u0624\u062b\u0631 \u0628\u0634\u0643\u0644 \u0643\u0628\u064a\u0631 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\u0628\u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644 \u0628\u0627\u0644\u0637\u0631\u0642 \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/p>\n<ol>\n<li>\n<p><strong>\u062c\u0645\u0639 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a<\/strong>: \u064a\u0645\u0643\u0646 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062e\u0648\u0627\u062f\u0645 \u0627\u0644\u0648\u0643\u064a\u0644\u0629 \u0644\u062c\u0645\u0639 \u0645\u062c\u0645\u0648\u0639\u0627\u062a \u0628\u064a\u0627\u0646\u0627\u062a \u0645\u062a\u0646\u0648\u0639\u0629 \u0645\u0646 \u0645\u0648\u0627\u0642\u0639 \u062c\u063a\u0631\u0627\u0641\u064a\u0629 \u0645\u062e\u062a\u0644\u0641\u0629\u060c \u0648\u0647\u0648 \u0623\u0645\u0631 \u0636\u0631\u0648\u0631\u064a \u0644\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0646\u062a\u0627\u0626\u062c \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644 \u063a\u064a\u0631 \u0627\u0644\u0645\u062a\u062d\u064a\u0632\u0629.<\/p>\n<\/li>\n<li>\n<p><strong>\u0627\u0644\u0623\u0645\u0646 \u0648\u0627\u0644\u062e\u0635\u0648\u0635\u064a\u0629<\/strong>: \u0639\u0646\u062f \u0627\u0644\u062a\u0639\u0627\u0645\u0644 \u0645\u0639 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u062d\u0633\u0627\u0633\u0629\u060c \u064a\u0645\u0643\u0646 \u0623\u0646 \u062a\u0633\u0627\u0639\u062f \u0627\u0644\u062e\u0648\u0627\u062f\u0645 \u0627\u0644\u0648\u0643\u064a\u0644\u0629 \u0641\u064a \u0625\u062e\u0641\u0627\u0621 \u0647\u0648\u064a\u0629 \u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0627\u0644\u0645\u0633\u062a\u062e\u062f\u0645 \u0623\u062b\u0646\u0627\u0621 \u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0627\u0644\u0635\u062d\u0629\u060c \u0645\u0645\u0627 \u064a\u0636\u0645\u0646 \u062e\u0635\u0648\u0635\u064a\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0648\u0623\u0645\u0646\u0647\u0627.<\/p>\n<\/li>\n<li>\n<p><strong>\u062a\u0648\u0632\u064a\u0639 \u0627\u0644\u062d\u0645\u0644<\/strong>: \u0641\u064a \u0625\u0639\u062f\u0627\u062f\u0627\u062a \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644 \u0627\u0644\u0645\u0648\u0632\u0639\u0629\u060c \u064a\u0645\u0643\u0646 \u0644\u0644\u062e\u0648\u0627\u062f\u0645 \u0627\u0644\u0648\u0643\u064a\u0644\u0629 \u0627\u0644\u0645\u0633\u0627\u0639\u062f\u0629 \u0641\u064a \u0645\u0648\u0627\u0632\u0646\u0629 \u0627\u0644\u062a\u062d\u0645\u064a\u0644 \u0639\u0628\u0631 \u0627\u0644\u0639\u0642\u062f \u0627\u0644\u0645\u062e\u062a\u0644\u0641\u0629\u060c \u0645\u0645\u0627 \u064a\u0624\u062f\u064a \u0625\u0644\u0649 \u062a\u062d\u0633\u064a\u0646 \u0627\u0644\u0643\u0641\u0627\u0621\u0629 \u0627\u0644\u062d\u0633\u0627\u0628\u064a\u0629.<\/p>\n<\/li>\n<\/ol>\n<h2>\u0631\u0648\u0627\u0628\u0637 \u0630\u0627\u062a \u0639\u0644\u0627\u0642\u0629<\/h2>\n<p>\u0644\u0645\u0632\u064a\u062f \u0645\u0646 \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062a \u062d\u0648\u0644 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644\u060c \u064a\u0645\u0643\u0646\u0643 \u0627\u0644\u0631\u062c\u0648\u0639 \u0625\u0644\u0649 \u0627\u0644\u0645\u0648\u0627\u0631\u062f \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/p>\n<ol>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/cross_validation.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-Learn \u062a\u0648\u062b\u064a\u0642 \u0627\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0627\u0644\u0635\u062d\u0629<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/a-gentle-introduction-to-cross-validation-209a89d69c55\" target=\"_new\" rel=\"noopener nofollow\">\u0646\u062d\u0648 \u0639\u0644\u0645 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u2013 \u0645\u0642\u062f\u0645\u0629 \u0644\u0637\u064a\u0641\u0629 \u0644\u0644\u062a\u062d\u0642\u0642 \u0645\u0646 \u0627\u0644\u0635\u062d\u0629<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Cross-validation\" target=\"_new\" rel=\"noopener nofollow\">\u0648\u064a\u0643\u064a\u0628\u064a\u062f\u064a\u0627 \u2013 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468046,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476484","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Cross-Validation: Understanding the Power of Validation Techniques<\/mark>","faq_items":[{"question":"What is Cross-Validation, and why is it important in machine learning?","answer":"<p>Cross-Validation is a statistical technique used to assess the performance of machine learning models by partitioning the dataset into subsets for training and testing. It helps to avoid overfitting and ensures the model's ability to generalize to new data. By providing a more realistic estimation of model performance, Cross-Validation plays a vital role in selecting the best model and tuning hyperparameters.<\/p>"},{"question":"How does Cross-Validation work?","answer":"<p>Cross-Validation involves dividing the data into k subsets or folds. The model is trained on k-1 folds and evaluated on the remaining one, iterating this process k times with each fold serving as the test set once. The final performance metric is an average of the metrics obtained in each iteration.<\/p>"},{"question":"What are the different types of Cross-Validation?","answer":"<p>Some common types of Cross-Validation include K-Fold Cross-Validation, Leave-One-Out Cross-Validation (LOOCV), Stratified K-Fold Cross-Validation, and Time Series Cross-Validation. Each type has specific use cases and advantages.<\/p>"},{"question":"What are the key benefits of using Cross-Validation?","answer":"<p>Cross-Validation offers several benefits, including bias reduction, optimal parameter tuning, robustness, and maximum data efficiency. It helps in identifying models that perform consistently well and improves the model's reliability.<\/p>"},{"question":"How can Cross-Validation be used in machine learning?","answer":"<p>Cross-Validation is used for various purposes, such as model selection, hyperparameter tuning, and feature selection. It provides valuable insights into a model's performance and aids in making better decisions during the model development process.<\/p>"},{"question":"What are the potential problems related to Cross-Validation and their solutions?","answer":"<p>Some common issues with Cross-Validation include data leakage and computational cost. To address these problems, practitioners can apply proper data preprocessing techniques and leverage parallelization for efficient execution.<\/p>"},{"question":"How does Cross-Validation compare to Bootstrap?","answer":"<p>Cross-Validation is primarily used for model evaluation, while Bootstrap focuses on parameter estimation and uncertainty quantification. Cross-Validation involves multiple folds, while Bootstrap uses random sampling with replacement.<\/p>"},{"question":"What does the future hold for Cross-Validation in the machine learning landscape?","answer":"<p>The future of Cross-Validation involves integration with advanced machine learning techniques, like deep learning and AutoML. Leveraging parallel computing and distributed systems will make Cross-Validation more scalable and efficient.<\/p>"},{"question":"How do proxy servers relate to Cross-Validation?","answer":"<p>Proxy servers can be associated with Cross-Validation in data collection, security, and load balancing. They help in collecting diverse datasets, ensuring data privacy, and optimizing distributed Cross-Validation setups.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/wiki\/476484","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/wiki\/476484\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/media\/468046"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/media?parent=476484"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}