<\/p>\n
Bir modelin tahmin do\u011frulu\u011funu \u00f6l\u00e7mek i\u00e7in en yayg\u0131n kullan\u0131lan \u00f6l\u00e7\u00fcmlerden biri, ortalama mutlak y\u00fczde hatas\u0131<\/strong> anlam\u0131na gelen MAPE’dir<\/strong> .<\/span><\/p>\n MAPE’yi hesaplamak i\u00e7in form\u00fcl a\u015fa\u011f\u0131daki gibidir:<\/span><\/p>\n MAPE<\/strong> = (1\/n) * \u03a3(|ger\u00e7ek \u2013 tahmin| \/ |ger\u00e7ek|) * 100<\/span><\/p>\n Alt\u0131n:<\/span><\/p>\n MAPE, yorumlanmas\u0131 ve a\u00e7\u0131klanmas\u0131 kolay oldu\u011fundan yayg\u0131n olarak kullan\u0131lmaktad\u0131r. \u00d6rne\u011fin %6’l\u0131k bir MAPE de\u011feri, tahmin edilen de\u011fer ile ger\u00e7ek de\u011fer aras\u0131ndaki ortalama fark\u0131n %6 oldu\u011fu anlam\u0131na gelir.<\/span><\/p>\n Bu e\u011fitimde R’de MAPE’yi hesaplamak i\u00e7in kullanabilece\u011finiz iki farkl\u0131 y\u00f6ntem sunulmaktad\u0131r.<\/span><\/p>\n Ger\u00e7ek veri de\u011ferlerini i\u00e7eren bir s\u00fctuna ve tahmin edilen veri de\u011ferlerini i\u00e7eren bir s\u00fctuna sahip bir veri setimiz oldu\u011funu varsayal\u0131m:<\/span><\/p>\n MAPE’yi hesaplamak i\u00e7in a\u015fa\u011f\u0131daki i\u015flevi kullanabiliriz:<\/span><\/p>\n Bu modelin MAPE’si %6,467<\/strong> olarak \u00e7\u0131k\u0131yor. Yani tahmin edilen de\u011fer ile ger\u00e7ekle\u015fen de\u011fer aras\u0131ndaki ortalama mutlak fark %6,467’dir.<\/span><\/p>\n Ayn\u0131 veri k\u00fcmesi i\u00e7in MAPE’yi, a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanan MLmetrics<\/strong> paketindeki MAPE()<\/strong> i\u015flevini kullanarak da hesaplayabiliriz:<\/span><\/p>\n MAPE(y_pred, y_true)<\/strong><\/span><\/p>\n Alt\u0131n:<\/span><\/p>\n \u00d6rne\u011fimizde kullanaca\u011f\u0131m\u0131z s\u00f6zdizimi \u015f\u00f6yledir:<\/span><\/p>\n Bu, \u00f6nceki y\u00f6ntemi kullanarak hesaplad\u0131\u011f\u0131m\u0131z %6,467’lik<\/strong> MAPE de\u011ferinin ayn\u0131s\u0131n\u0131 \u00fcretir.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" Bir modelin tahmin do\u011frulu\u011funu \u00f6l\u00e7mek i\u00e7in en yayg\u0131n kullan\u0131lan \u00f6l\u00e7\u00fcmlerden biri, ortalama mutlak y\u00fczde hatas\u0131 anlam\u0131na gelen MAPE’dir . MAPE’yi hesaplamak i\u00e7in form\u00fcl a\u015fa\u011f\u0131daki gibidir: MAPE = (1\/n) * \u03a3(|ger\u00e7ek \u2013 tahmin| \/ |ger\u00e7ek|) * 100 Alt\u0131n: \u03a3 \u2013 \u201ctoplam\u201d anlam\u0131na gelen s\u00fcsl\u00fc bir sembol n \u2013 \u00f6rneklem b\u00fcy\u00fckl\u00fc\u011f\u00fc ger\u00e7ek \u2013 verilerin ger\u00e7ek de\u011feri tahmin […]<\/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-677","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n\n
Y\u00f6ntem 1: Kendi i\u015flevinizi yaz\u0131n<\/strong><\/span><\/h3>\n
#create dataset<\/span>\ndata <- data. frame<\/span> (actual=c(34, 37, 44, 47, 48, 48, 46, 43, 32, 27, 26, 24),\n forecast=c(37, 40, 46, 44, 46, 50, 45, 44, 34, 30, 22, 23))\n\n#view dataset<\/span>\ndata\n\n current forecast\n1 34 37\n2 37 40\n3 44 46\n4 47 44\n5 48 46\n6 48 50\n7 46 45\n8 43 44\n9 32 34\n10 27 30\n11 26 22\n12 24 23\n<\/strong><\/pre>\n #calculate MAPE<\/span>\nmean(abs((data$actual-data$forecast)\/data$actual)) * 100\n\n[1] 6.467108\n<\/strong><\/pre>\n Y\u00f6ntem 2: Paket kullanma<\/strong><\/span><\/h3>\n
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#load MLmetrics package<\/span>\nlibrary<\/span> (MLmetrics)\n\n#calculate MAPE<\/span>\nMAPE(data$forecast, data$actual)\n\n[1] 0.06467108<\/strong><\/pre>\n