{"id":476322,"date":"2023-08-09T07:28:31","date_gmt":"2023-08-09T07:28:31","guid":{"rendered":""},"modified":"2023-09-05T11:12:27","modified_gmt":"2023-09-05T11:12:27","slug":"collinearity-in-regression-analysis","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/ir\/wiki\/collinearity-in-regression-analysis\/","title":{"rendered":"\u0647\u0645 \u062e\u0637\u06cc \u062f\u0631 \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646"},"content":{"rendered":"<p>\u0647\u0645 \u062e\u0637\u06cc \u062f\u0631 \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u0647 \u067e\u062f\u06cc\u062f\u0647 \u0622\u0645\u0627\u0631\u06cc \u0627\u0637\u0644\u0627\u0642 \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u062f\u0648 \u06cc\u0627 \u0686\u0646\u062f \u0645\u062a\u063a\u06cc\u0631 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u062f\u0631 \u06cc\u06a9 \u0645\u062f\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 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\u0628\u0631\u0627\u0646\u06af\u06cc\u0632 \u0645\u06cc \u0634\u0648\u062f \u0632\u06cc\u0631\u0627 \u062c\u062f\u0627 \u06a9\u0631\u062f\u0646 \u062a\u0623\u062b\u06cc\u0631 \u0647\u0631 \u0645\u062a\u063a\u06cc\u0631 \u062f\u0634\u0648\u0627\u0631 \u0627\u0633\u062a.<\/li>\n<li><strong>\u06a9\u0627\u0647\u0634 \u0642\u062f\u0631\u062a \u0622\u0645\u0627\u0631\u06cc:<\/strong> \u0642\u062f\u0631\u062a \u0622\u0645\u0627\u0631\u06cc \u0645\u062f\u0644 \u0631\u0627 \u06a9\u0627\u0647\u0634 \u0645\u06cc \u062f\u0647\u062f\u060c \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u06a9\u0647 \u0627\u062d\u062a\u0645\u0627\u0644 \u0627\u06cc\u0646\u06a9\u0647 \u0636\u0631\u0627\u06cc\u0628 \u0627\u0632 \u0646\u0638\u0631 \u0622\u0645\u0627\u0631\u06cc \u0645\u0639\u0646\u06cc \u062f\u0627\u0631 \u0634\u0648\u0646\u062f \u06a9\u0645\u062a\u0631 \u0645\u06cc \u0634\u0648\u062f.<\/li>\n<\/ul>\n<h2>\u0627\u0646\u0648\u0627\u0639 \u062e\u0637 \u062e\u0637\u06cc<\/h2>\n<p>\u062f\u0631 \u062f\u0631\u062c\u0647 \u0627\u0648\u0644 \u062f\u0648 \u0646\u0648\u0639 \u0647\u0645 \u062e\u0637\u06cc \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f:<\/p>\n<ol>\n<li><strong>\u0686\u0646\u062f \u062e\u0637\u06cc \u0628\u0648\u062f\u0646:<\/strong> \u0632\u0645\u0627\u0646\u06cc \u06a9\u0647 \u0633\u0647 \u06cc\u0627 \u0686\u0646\u062f \u0645\u062a\u063a\u06cc\u0631\u060c \u06a9\u0647 \u0628\u0627\u0644\u0627 \u0647\u0633\u062a\u0646\u062f \u0627\u0645\u0627 \u0627\u0632 \u0646\u0638\u0631 \u062e\u0637\u06cc \u0647\u0645\u0628\u0633\u062a\u06af\u06cc \u06a9\u0627\u0645\u0644\u06cc \u0646\u062f\u0627\u0631\u0646\u062f\u060c \u062f\u0631 \u06cc\u06a9 \u0645\u062f\u0644 \u06af\u0646\u062c\u0627\u0646\u062f\u0647 \u0634\u0648\u0646\u062f.<\/li>\n<li><strong>\u0647\u0645 \u062e\u0637\u06cc \u06a9\u0627\u0645\u0644:<\/strong> \u0648\u0642\u062a\u06cc \u06cc\u06a9 \u0645\u062a\u063a\u06cc\u0631 \u0645\u0633\u062a\u0642\u0644 \u062a\u0631\u06a9\u06cc\u0628 \u062e\u0637\u06cc \u06a9\u0627\u0645\u0644\u06cc \u0627\u0632 \u06cc\u06a9 \u06cc\u0627 \u0686\u0646\u062f \u0645\u062a\u063a\u06cc\u0631 \u0645\u0633\u062a\u0642\u0644 \u062f\u06cc\u06af\u0631 \u0628\u0627\u0634\u062f.<\/li>\n<\/ol>\n<h2>\u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0647\u0645 \u062e\u0637\u06cc \u062f\u0631 \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646: \u0645\u0633\u0627\u0626\u0644 \u0648 \u0631\u0627\u0647 \u062d\u0644 \u0647\u0627<\/h2>\n<p>\u0645\u062f\u06cc\u0631\u06cc\u062a \u0647\u0645 \u062e\u0637\u06cc \u062f\u0631 \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u0631\u0627\u06cc \u0628\u0647\u0628\u0648\u062f \u0642\u0627\u0628\u0644\u06cc\u062a \u0627\u0637\u0645\u06cc\u0646\u0627\u0646 \u0648 \u062a\u0641\u0633\u06cc\u0631\u067e\u0630\u06cc\u0631\u06cc \u0645\u062f\u0644 \u0628\u0633\u06cc\u0627\u0631 \u0645\u0647\u0645 \u0627\u0633\u062a. \u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u0631\u0627\u0647 \u062d\u0644 \u0647\u0627\u06cc \u0631\u0627\u06cc\u062c \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f:<\/p>\n<ul>\n<li><strong>\u0636\u0631\u06cc\u0628 \u062a\u0648\u0631\u0645 \u0648\u0627\u0631\u06cc\u0627\u0646\u0633 (VIF):<\/strong> \u0627\u0646\u062f\u0627\u0632\u0647 \u06af\u06cc\u0631\u06cc \u06a9\u0647 \u062a\u062e\u0645\u06cc\u0646 \u0645\u06cc \u0632\u0646\u062f \u06a9\u0647 \u0686\u0642\u062f\u0631 \u0648\u0627\u0631\u06cc\u0627\u0646\u0633 \u06cc\u06a9 \u0636\u0631\u06cc\u0628 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0628\u0631\u0622\u0648\u0631\u062f \u0634\u062f\u0647 \u0628\u0647 \u062f\u0644\u06cc\u0644 \u0686\u0646\u062f \u062e\u0637\u06cc \u0628\u0648\u062f\u0646 \u0627\u0641\u0632\u0627\u06cc\u0634 \u06cc\u0627\u0641\u062a\u0647 \u0627\u0633\u062a.<\/li>\n<li><strong>\u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0631\u06cc\u062c:<\/strong> \u062a\u06a9\u0646\u06cc\u06a9\u06cc \u06a9\u0647 \u0628\u0627 \u0686\u0646\u062f \u062e\u0637\u06cc \u0628\u0648\u062f\u0646 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u0627\u0646\u0642\u0628\u0627\u0636 \u0633\u0631\u0648\u06a9\u0627\u0631 \u062f\u0627\u0631\u062f.<\/li>\n<\/ul>\n<h2>\u062e\u0637 \u062e\u0637\u06cc \u0648 \u0633\u0627\u06cc\u0631 \u0627\u0635\u0637\u0644\u0627\u062d\u0627\u062a \u0645\u0634\u0627\u0628\u0647<\/h2>\n<p>\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u0628\u0631\u062e\u06cc \u0627\u0632 \u0627\u0635\u0637\u0644\u0627\u062d\u0627\u062a \u0645\u0634\u0627\u0628\u0647 \u0628\u0627 \u0647\u0645 \u062e\u0637\u06cc \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f:<\/p>\n<ul>\n<li><strong>\u06a9\u0648\u0648\u0627\u0631\u06cc\u0627\u0646\u0633:<\/strong> \u0645\u06cc\u0632\u0627\u0646 \u062a\u0641\u0627\u0648\u062a \u062f\u0648 \u0645\u062a\u063a\u06cc\u0631 \u062a\u0635\u0627\u062f\u0641\u06cc \u0631\u0627 \u0628\u0627 \u0647\u0645 \u0627\u0646\u062f\u0627\u0632\u0647 \u0645\u06cc \u06af\u06cc\u0631\u062f.<\/li>\n<li><strong>\u0647\u0645\u0628\u0633\u062a\u06af\u06cc:<\/strong> \u0642\u062f\u0631\u062a \u0648 \u062c\u0647\u062a \u06cc\u06a9 \u0631\u0627\u0628\u0637\u0647 \u062e\u0637\u06cc \u0628\u06cc\u0646 \u062f\u0648 \u0645\u062a\u063a\u06cc\u0631 \u0631\u0627 \u0627\u0646\u062f\u0627\u0632\u0647 \u06af\u06cc\u0631\u06cc \u0645\u06cc \u06a9\u0646\u062f.<\/li>\n<\/ul>\n<p>\u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u06a9\u0648\u0648\u0627\u0631\u06cc\u0627\u0646\u0633 \u0645\u0639\u06cc\u0627\u0631\u06cc \u0628\u0631\u0627\u06cc \u0647\u0645\u0628\u0633\u062a\u06af\u06cc \u0627\u0633\u062a\u060c \u0647\u0645 \u062e\u0637\u06cc \u0628\u0647 \u0645\u0648\u0642\u0639\u06cc\u062a\u06cc \u0627\u0634\u0627\u0631\u0647 \u062f\u0627\u0631\u062f \u06a9\u0647 \u062f\u0648 \u0645\u062a\u063a\u06cc\u0631 \u0628\u0647 \u0634\u062f\u062a \u0647\u0645\u0628\u0633\u062a\u06af\u06cc \u062f\u0627\u0631\u0646\u062f.<\/p>\n<h2>\u0686\u0634\u0645 \u0627\u0646\u062f\u0627\u0632\u0647\u0627\u06cc \u0622\u06cc\u0646\u062f\u0647 \u062f\u0631 \u062e\u0637 \u062e\u0637\u06cc<\/h2>\n<p>\u0628\u0627 \u067e\u06cc\u0634\u0631\u0641\u062a \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645\u200c\u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646 \u0627\u062b\u0631\u0627\u062a \u0647\u0645\u200c\u062e\u0637\u06cc \u0631\u0627 \u06a9\u0627\u0647\u0634 \u062f\u0627\u062f. \u062a\u06a9\u0646\u06cc\u06a9\u200c\u0647\u0627\u06cc\u06cc \u0645\u0627\u0646\u0646\u062f \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0645\u0624\u0644\u0641\u0647 \u0627\u0635\u0644\u06cc (PCA) \u06cc\u0627 \u0631\u0648\u0634\u200c\u0647\u0627\u06cc \u0645\u0646\u0638\u0645\u200c\u0633\u0627\u0632\u06cc (Lasso\u060c Ridge \u0648 Elastic Net) \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u0646\u062f \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0628\u0627 \u0627\u0628\u0639\u0627\u062f \u0628\u0627\u0644\u0627 \u0631\u0627 \u062f\u0631 \u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0647\u0645\u062e\u0637\u06cc \u0628\u0648\u062f\u0646 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0645\u0634\u06a9\u0644\u200c\u0633\u0627\u0632 \u0628\u0627\u0634\u062f\u060c \u0645\u062f\u06cc\u0631\u06cc\u062a \u06a9\u0646\u0646\u062f. \u0627\u0646\u062a\u0638\u0627\u0631 \u0645\u06cc \u0631\u0648\u062f \u0627\u06cc\u0646 \u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627 \u0628\u0627 \u067e\u06cc\u0634\u0631\u0641\u062a \u0647\u0627\u06cc \u0628\u06cc\u0634\u062a\u0631 \u062f\u0631 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646\u06cc \u067e\u06cc\u0686\u06cc\u062f\u0647 \u062a\u0631 \u0634\u0648\u0646\u062f.<\/p>\n<h2>\u0633\u0631\u0648\u0631\u0647\u0627\u06cc \u067e\u0631\u0648\u06a9\u0633\u06cc \u0648 \u0647\u0645 \u062e\u0637\u06cc \u062f\u0631 \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646<\/h2>\n<p>\u0633\u0631\u0648\u0631\u0647\u0627\u06cc \u067e\u0631\u0648\u06a9\u0633\u06cc \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0627\u0633\u0637\u0647 \u0628\u06cc\u0646 \u0645\u0634\u062a\u0631\u06cc \u0648 \u0633\u0631\u0648\u0631 \u0639\u0645\u0644 \u0645\u06cc \u06a9\u0646\u0646\u062f \u0648 \u0645\u0632\u0627\u06cc\u0627\u06cc \u0645\u062e\u062a\u0644\u0641\u06cc \u0645\u0627\u0646\u0646\u062f \u0646\u0627\u0634\u0646\u0627\u0633 \u0628\u0648\u062f\u0646 \u0648 \u0627\u0645\u0646\u06cc\u062a \u0631\u0627 \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc \u062f\u0647\u0646\u062f. \u062f\u0631 \u0632\u0645\u06cc\u0646\u0647 \u0647\u0645\u062e\u0637\u06cc \u062f\u0631 \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646\u060c \u0627\u0632 \u0633\u0631\u0648\u0631\u0647\u0627\u06cc \u067e\u0631\u0648\u06a9\u0633\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0631\u0627\u06cc \u062c\u0645\u0639 \u0622\u0648\u0631\u06cc \u0648 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0642\u0628\u0644 \u0627\u0632 \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f. \u0627\u06cc\u0646 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0634\u0627\u0645\u0644 \u0634\u0646\u0627\u0633\u0627\u06cc\u06cc \u0648 \u06a9\u0627\u0647\u0634 \u0647\u0645\u062e\u0637\u06cc \u0628\u0648\u062f\u0646 \u0628\u0627\u0634\u062f\u060c \u0628\u0647\u200c\u0648\u06cc\u0698\u0647 \u0647\u0646\u06af\u0627\u0645 \u0645\u062f\u06cc\u0631\u06cc\u062a \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0628\u0632\u0631\u06af \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0645\u0633\u0627\u0626\u0644 \u0645\u0631\u062a\u0628\u0637 \u0628\u0627 \u0647\u0645\u062e\u0637\u06cc \u0631\u0627 \u062a\u0642\u0648\u06cc\u062a \u06a9\u0646\u062f.<\/p>\n<h2>\u0644\u06cc\u0646\u06a9 \u0647\u0627\u06cc \u0645\u0631\u0628\u0648\u0637\u0647<\/h2>\n<p>\u0628\u0631\u0627\u06cc \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0628\u06cc\u0634\u062a\u0631 \u062f\u0631 \u0645\u0648\u0631\u062f \u0647\u0645\u062e\u0637\u06cc \u062f\u0631 \u062a\u062d\u0644\u06cc\u0644 \u0631\u06af\u0631\u0633\u06cc\u0648\u0646\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u0645\u0646\u0627\u0628\u0639 \u0632\u06cc\u0631 \u062f\u06cc\u062f\u0646 \u06a9\u0646\u06cc\u062f:<\/p>\n<ul>\n<li><a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4468013\/\" target=\"_new\" rel=\"noopener nofollow\">\u0686\u0646\u062f \u062e\u0637\u06cc \u062f\u0631 \u062a\u062d\u0644\u06cc\u0644 \u0647\u0627\u06cc \u0631\u06af\u0631\u0633\u06cc\u0648\u0646\u06cc \u0627\u0646\u062c\u0627\u0645 \u0634\u062f\u0647 \u062f\u0631 \u0645\u0637\u0627\u0644\u0639\u0627\u062a \u0627\u067e\u06cc\u062f\u0645\u06cc\u0648\u0644\u0648\u0698\u06cc\u06a9<\/a><\/li>\n<li><a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2020\/03\/what-is-multicollinearity\/\" target=\"_new\" rel=\"noopener nofollow\">\u0686\u0646\u062f \u062e\u0637\u06cc \u0686\u06cc\u0633\u062a\u061f \u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u0647\u0645\u0647 \u0686\u06cc\u0632\u0647\u0627\u06cc\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0628\u0627\u06cc\u062f \u0628\u062f\u0627\u0646\u06cc\u062f<\/a><\/li>\n<li><a href=\"https:\/\/www.statisticssolutions.com\/multicollinearity\/\" target=\"_new\" rel=\"noopener nofollow\">\u0645\u0642\u0627\u0628\u0644\u0647 \u0628\u0627 \u0686\u0646\u062f \u062e\u0637\u06cc \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 VIF<\/a><\/li>\n<li><a href=\"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3881361\/\" target=\"_new\" rel=\"noopener nofollow\">\u0647\u0645 \u062e\u0637\u06cc: \u0645\u0631\u0648\u0631\u06cc \u0628\u0631 \u0631\u0648\u0634 \u0647\u0627\u06cc \u0645\u0642\u0627\u0628\u0644\u0647 \u0628\u0627 \u0622\u0646 \u0648 \u06cc\u06a9 \u0645\u0637\u0627\u0644\u0639\u0647 \u0634\u0628\u06cc\u0647 \u0633\u0627\u0632\u06cc \u06a9\u0647 \u0639\u0645\u0644\u06a9\u0631\u062f \u0622\u0646\u0647\u0627 \u0631\u0627 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u06cc \u06a9\u0646\u062f<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Proxy_server\" target=\"_new\" rel=\"noopener nofollow\">\u0633\u0631\u0648\u0631 \u067e\u0631\u0648\u06a9\u0633\u06cc<\/a><\/li>\n<\/ul>","protected":false},"featured_media":467906,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476322","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Collinearity in Regression Analysis: An Indispensable Concept in Data Analytics<\/mark>","faq_items":[{"question":"What is Collinearity in Regression Analysis?","answer":"<p>Collinearity in regression analysis is a statistical phenomenon where two or more predictor variables in a multiple regression model are highly correlated. This strong correlation can undermine the statistical significance of an independent variable by creating difficulties in estimating the relationship between each predictor and the response variable.<\/p>"},{"question":"Who was the first to mention Collinearity?","answer":"<p>The concept of collinearity can be traced back to the early 20th century and was initially identified by the renowned economist, Ragnar Frisch.<\/p>"},{"question":"Why is Collinearity a problem in Regression Analysis?","answer":"<p>Collinearity is a problem in regression analysis because it makes it difficult to isolate the impact of each independent variable on the dependent variable. It inflates the variance of the predicted coefficients, leading to unreliable and unstable estimates of regression coefficients.<\/p>"},{"question":"What are the key features of Collinearity?","answer":"<p>The key features of Collinearity include the inflation of the variance of regression coefficients, impaired model interpretability, and a reduction in the statistical power of the model.<\/p>"},{"question":"What types of Collinearity exist?","answer":"<p>There are primarily two types of collinearity: multicollinearity, which involves three or more variables that are high but not perfect linearly correlated, and perfect collinearity, which occurs when one independent variable is a perfect linear combination of one or more other independent variables.<\/p>"},{"question":"How can one address the problems of Collinearity in Regression Analysis?","answer":"<p>Problems related to Collinearity in regression analysis can be solved by using the Variance Inflation Factor (VIF) to measure the variance of an estimated regression coefficient, and Ridge Regression, a technique that deals with multicollinearity through a shrinkage parameter.<\/p>"},{"question":"How can Proxy Servers be used with Collinearity in Regression Analysis?","answer":"<p>In the context of collinearity in regression analysis, proxy servers can be used to collect and preprocess data before regression analysis. This includes identifying and mitigating collinearity, especially when handling large datasets that could amplify the issues associated with collinearity.<\/p>"},{"question":"What are the future perspectives on Collinearity?","answer":"<p>With the advancement of machine learning algorithms, techniques such as Principal Component Analysis (PCA) or regularization methods (Lasso, Ridge, and Elastic Net) can handle high-dimensional data where collinearity might be a problem. These techniques are expected to become more sophisticated with further advances in artificial intelligence and machine learning.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/wiki\/476322","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/wiki\/476322\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/media\/467906"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/media?parent=476322"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}