<\/p>\n
\u0130statistiklerde ortalama mutlak hata<\/strong> (MAE), belirli bir modelin do\u011frulu\u011funu \u00f6l\u00e7menin bir yoludur. A\u015fa\u011f\u0131daki \u015fekilde hesaplan\u0131r:<\/span><\/p>\n MAE = (1\/n) * \u03a3|y i<\/sub> \u2013 x i<\/sub> |<\/span><\/p>\n Alt\u0131n:<\/span><\/p>\n Python’da ortalama mutlak hatay\u0131 Scikit-learn’in Mean_absolute_error()<\/a> fonksiyonunu kullanarak kolayca hesaplayabiliriz.<\/span><\/p>\n Bu e\u011fitimde bu \u00f6zelli\u011fin kullan\u0131m\u0131na ili\u015fkin pratik bir \u00f6rnek sunulmaktad\u0131r.<\/span><\/p>\n Python’da a\u015fa\u011f\u0131daki ger\u00e7ek de\u011fer ve tahmin edilen de\u011fer dizilerine sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, bu model i\u00e7in ortalama mutlak hatan\u0131n nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Ortalama mutlak hatan\u0131n (MAE) 2,42857<\/strong> oldu\u011fu ortaya \u00e7\u0131k\u0131yor.<\/span><\/p>\n Bu bize ger\u00e7ek veri de\u011feri ile modelin \u00f6ng\u00f6rd\u00fc\u011f\u00fc de\u011fer aras\u0131ndaki ortalama fark\u0131n 2,42857 oldu\u011funu s\u00f6yl\u00fcyor.<\/span><\/p>\n Hangi modellerin en iyi performans\u0131 g\u00f6sterdi\u011fini g\u00f6rmek i\u00e7in bu MAE’yi di\u011fer tahmin modelleri taraf\u0131ndan elde edilen MAE ile kar\u015f\u0131la\u015ft\u0131rabiliriz.<\/span><\/p>\n Belirli bir model i\u00e7in MAE ne kadar d\u00fc\u015f\u00fckse, model ger\u00e7ek de\u011ferleri o kadar iyi tahmin edebilir.<\/span><\/p>\n Not:<\/strong> Bu i\u015flevin do\u011fru \u00e7al\u0131\u015fmas\u0131 i\u00e7in hem ger\u00e7ek de\u011fer dizisinin hem de tahmin edilen de\u011fer dizisinin ayn\u0131 uzunlukta olmas\u0131 gerekir.<\/span><\/p>\n Python’da MAPE nas\u0131l hesaplan\u0131r<\/a> \u0130statistiklerde ortalama mutlak hata (MAE), belirli bir modelin do\u011frulu\u011funu \u00f6l\u00e7menin bir yoludur. A\u015fa\u011f\u0131daki \u015fekilde hesaplan\u0131r: MAE = (1\/n) * \u03a3|y i \u2013 x i | Alt\u0131n: \u03a3: \u201ctoplam\u201d anlam\u0131na gelen Yunanca bir sembol y i : i’inci g\u00f6zlem i\u00e7in g\u00f6zlemlenen de\u011fer x i : i’inci g\u00f6zlem i\u00e7in tahmin edilen de\u011fer n: Toplam g\u00f6zlem say\u0131s\u0131 Python’da […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1356","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n\n
\u00d6rnek: Python’da Ortalama Mutlak Hatan\u0131n Hesaplanmas\u0131<\/strong><\/span><\/h3>\n
actual = [12, 13, 14, 15, 15, 22, 27]\npred = [11, 13, 14, 14, 15, 16, 18]\n<\/strong><\/pre>\n
from<\/span> sklearn. metrics<\/span> import<\/span> mean_absolute_error as<\/span> mae\n\n#calculate MAE<\/span>\nmae(actual, pred)\n\n2.4285714285714284\n<\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/h3>\n
Python’da SMAPE nas\u0131l hesaplan\u0131r<\/a>
Python’da MSE Nas\u0131l Hesaplan\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"