{"id":1220,"date":"2023-07-27T06:02:37","date_gmt":"2023-07-27T06:02:37","guid":{"rendered":"https:\/\/statorials.org\/tr\/rde-siniflandirma-ve-regresyon-agaclari\/"},"modified":"2023-07-27T06:02:37","modified_gmt":"2023-07-27T06:02:37","slug":"rde-siniflandirma-ve-regresyon-agaclari","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/rde-siniflandirma-ve-regresyon-agaclari\/","title":{"rendered":"R'ye s\u0131n\u0131fland\u0131rma ve regresyon a\u011fa\u00e7lar\u0131 nas\u0131l s\u0131\u011fd\u0131r\u0131l\u0131r"},"content":{"rendered":"

<\/p>\n

\n

Bir dizi yorday\u0131c\u0131 de\u011fi\u015fken ile bir yan\u0131t de\u011fi\u015fkeni<\/a> aras\u0131ndaki ili\u015fki do\u011frusal oldu\u011funda, \u00e7oklu do\u011frusal regresyon<\/a> gibi y\u00f6ntemler, do\u011fru \u00f6ng\u00f6r\u00fcc\u00fc modeller \u00fcretebilir.<\/span><\/p>\n

Bununla birlikte, bir dizi \u00f6ng\u00f6r\u00fc ile bir yan\u0131t aras\u0131ndaki ili\u015fki daha karma\u015f\u0131k oldu\u011funda do\u011frusal olmayan y\u00f6ntemler genellikle daha do\u011fru modeller \u00fcretebilir.<\/span><\/p>\n

B\u00f6yle bir y\u00f6ntem, bir yan\u0131t de\u011fi\u015fkeninin de\u011ferini tahmin eden karar a\u011fa\u00e7lar\u0131 olu\u015fturmak i\u00e7in bir dizi tahmin de\u011fi\u015fkeni kullanan s\u0131n\u0131fland\u0131rma ve regresyon a\u011fa\u00e7lar\u0131d\u0131r<\/a> (CART).<\/span><\/p>\n

Yan\u0131t de\u011fi\u015fkeni s\u00fcrekli ise regresyon a\u011fa\u00e7lar\u0131 olu\u015fturabiliriz ve yan\u0131t de\u011fi\u015fkeni kategorik ise s\u0131n\u0131fland\u0131rma a\u011fa\u00e7lar\u0131 olu\u015fturabiliriz.<\/span><\/p>\n

Bu e\u011fitimde R’de regresyon ve s\u0131n\u0131fland\u0131rma a\u011fa\u00e7lar\u0131n\u0131n nas\u0131l olu\u015fturulaca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n

\u00d6rnek 1: R’de Regresyon A\u011fac\u0131 Olu\u015fturma<\/strong><\/span><\/h3>\n

Bu \u00f6rnek i\u00e7in, 263 profesyonel beyzbol oyuncusu hakk\u0131nda \u00e7e\u015fitli bilgiler i\u00e7eren ISLR<\/strong> paketindeki Hitters<\/strong> veri setini kullanaca\u011f\u0131z.<\/span><\/p>\n

Bu veri setini, belirli bir oyuncunun maa\u015f\u0131n\u0131<\/em> tahmin etmek i\u00e7in say\u0131<\/em> ve oynanan y\u0131llar\u0131n<\/em> tahmin de\u011fi\u015fkenlerini kullanan bir regresyon a\u011fac\u0131 olu\u015fturmak i\u00e7in kullanaca\u011f\u0131z.<\/span><\/p>\n

Bu regresyon a\u011fac\u0131n\u0131 olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki ad\u0131mlar\u0131 kullan\u0131n.<\/span><\/p>\n

Ad\u0131m 1: Gerekli paketleri y\u00fckleyin.<\/strong><\/span><\/p>\n

\u00d6ncelikle bu \u00f6rnek i\u00e7in gerekli paketleri y\u00fckleyece\u011fiz:<\/span><\/p>\n

 library<\/span> (ISLR) #contains Hitters dataset<\/span>\nlibrary<\/span> (rpart) #for fitting decision trees<\/span>\nlibrary<\/span> (rpart.plot) #for plotting decision trees\n<\/span><\/strong><\/pre>\n
 Ad\u0131m 2: \u0130lk regresyon a\u011fac\u0131n\u0131 olu\u015fturun.<\/strong><\/span><\/p>\n
 \u00d6ncelikle b\u00fcy\u00fck bir ba\u015flang\u0131\u00e7 regresyon a\u011fac\u0131 olu\u015fturaca\u011f\u0131z. “Karma\u015f\u0131kl\u0131k parametresi” anlam\u0131na gelen cp<\/strong> i\u00e7in k\u00fc\u00e7\u00fck bir de\u011fer kullanarak a\u011fac\u0131n b\u00fcy\u00fck oldu\u011funu garanti edebiliriz.<\/span><\/p>\n
 Bu, modelin genel R-kare de\u011feri en az\u0131ndan cp taraf\u0131ndan belirtilen de\u011fer kadar artt\u0131\u011f\u0131 s\u00fcrece regresyon a\u011fac\u0131nda daha fazla b\u00f6lme i\u015flemi ger\u00e7ekle\u015ftirece\u011fimiz anlam\u0131na gelir.<\/span><\/p>\n
 Daha sonra model sonu\u00e7lar\u0131n\u0131 yazd\u0131rmak i\u00e7in printcp()<\/strong> fonksiyonunu kullanaca\u011f\u0131z:<\/span><\/p>\n
 #build the initial tree\n<\/span>tree <- rpart(Salary ~ Years + HmRun, data=Hitters, control=rpart. control<\/span> (cp= .0001<\/span> ))\n\n#view results<\/span>\nprintcp(tree)\n\nVariables actually used in tree construction:\n[1] HmRun Years\n\nRoot node error: 53319113\/263 = 202734\n\nn=263 (59 observations deleted due to missingness)\n\n           CP nsplit rel error xerror xstd\n1 0.24674996 0 1.00000 1.00756 0.13890\n2 0.10806932 1 0.75325 0.76438 0.12828\n3 0.01865610 2 0.64518 0.70295 0.12769\n4 0.01761100 3 0.62652 0.70339 0.12337\n5 0.01747617 4 0.60891 0.70339 0.12337\n6 0.01038188 5 0.59144 0.66629 0.11817\n7 0.01038065 6 0.58106 0.65697 0.11687\n8 0.00731045 8 0.56029 0.67177 0.11913\n9 0.00714883 9 0.55298 0.67881 0.11960\n10 0.00708618 10 0.54583 0.68034 0.11988\n11 0.00516285 12 0.53166 0.68427 0.11997\n12 0.00445345 13 0.52650 0.68994 0.11996\n13 0.00406069 14 0.52205 0.68988 0.11940\n14 0.00264728 15 0.51799 0.68874 0.11916\n15 0.00196586 16 0.51534 0.68638 0.12043\n16 0.00016686 17 0.51337 0.67577 0.11635\n17 0.00010000 18 0.51321 0.67576 0.11615\nn=263 (59 observations deleted due to missingness)\n<\/span><\/span><\/strong><\/pre>\n
 Ad\u0131m 3: A\u011fac\u0131 buday\u0131n.<\/strong><\/span><\/p>\n
 Daha sonra, en d\u00fc\u015f\u00fck test hatas\u0131na yol a\u00e7an cp (karma\u015f\u0131kl\u0131k parametresi) i\u00e7in kullan\u0131lacak en uygun de\u011feri bulmak amac\u0131yla regresyon a\u011fac\u0131n\u0131 budayaca\u011f\u0131z.<\/span><\/p>\n
 cp i\u00e7in en uygun de\u011ferin \u00f6nceki \u00e7\u0131kt\u0131daki en d\u00fc\u015f\u00fck x hatas\u0131na<\/strong> yol a\u00e7an de\u011fer oldu\u011funa dikkat edin; bu, \u00e7apraz do\u011frulama verilerinden elde edilen g\u00f6zlemlerdeki hatay\u0131 temsil eder.<\/span> <\/p>\n
 #identify best cp value to use\nbest <- tree$cptable[which. min<\/span> (tree$cptable[,\" xerror<\/span> \"]),\" CP<\/span> \"]\n\n#produce a pruned tree based on the best cp value\n<\/span>pruned_tree <- prune<\/span> (tree, cp=best)\n\n#plot the pruned tree\n<\/span>prp(pruned_tree,\n    faclen= 0<\/span> , #use full names for factor labels<\/span>\n    extra= 1<\/span> , #display number of obs. for each terminal node<\/span>\n    roundint= F<\/span> , #don't round to integers in output<\/span>\n    digits= 5<\/span> ) #display 5 decimal places in output\n<\/span><\/span><\/span><\/span><\/strong><\/pre>\n
\"R'deki<\/p>\n
 Son budanm\u0131\u015f a\u011fac\u0131n alt\u0131 terminal d\u00fc\u011f\u00fcm\u00fcne sahip oldu\u011funu g\u00f6rebiliriz. Her yaprak d\u00fc\u011f\u00fcm, o d\u00fc\u011f\u00fcmdeki oyuncular\u0131n tahmini maa\u015f\u0131n\u0131n yan\u0131 s\u0131ra, orijinal veri k\u00fcmesindeki o s\u0131n\u0131fa ait g\u00f6zlem say\u0131s\u0131n\u0131 da g\u00f6r\u00fcnt\u00fcler.<\/span><\/p>\n
 \u00d6rne\u011fin, orijinal veri setinde 4,5 y\u0131ldan az deneyime sahip 90 oyuncunun bulundu\u011funu ve ortalama maa\u015flar\u0131n\u0131n 225,83 bin dolar oldu\u011funu g\u00f6rebiliyoruz.<\/span> <\/p>\n
\"R'de<\/p>\n
 Ad\u0131m 4: Tahminlerde bulunmak i\u00e7in a\u011fac\u0131 kullan\u0131n.<\/strong><\/span><\/p>\n
 Belirli bir oyuncunun maa\u015f\u0131n\u0131, y\u0131llar\u0131n deneyimine ve ortalama say\u0131 say\u0131s\u0131na dayanarak tahmin etmek i\u00e7in son budanm\u0131\u015f a\u011fac\u0131 kullanabiliriz.<\/span><\/p>\n
 \u00d6rne\u011fin, 7 y\u0131ll\u0131k tecr\u00fcbeye sahip ve ortalama 4 home run yapan bir oyuncunun beklenen maa\u015f\u0131 502,81 bin dolar<\/strong> .<\/span> <\/p>\n
\"R'deki<\/p>\n
 Bunu onaylamak i\u00e7in R’deki tahmin()<\/strong> fonksiyonunu kullanabiliriz:<\/span><\/p>\n
 #define new player\nnew <- data.frame(Years=7, HmRun=4)\n\n<\/span>#use pruned tree to predict salary of this player\npredict(pruned_tree, newdata=new)\n\n502.8079<\/span><\/span><\/span><\/strong><\/pre>\n
 \u00d6rnek 2: R’de bir s\u0131n\u0131fland\u0131rma a\u011fac\u0131 olu\u015fturma<\/strong><\/span><\/h3>\n
 Bu \u00f6rnek i\u00e7in Titanic’teki yolcular hakk\u0131nda \u00e7e\u015fitli bilgiler i\u00e7eren rpart.plot<\/strong> paketindeki ptitanic<\/strong> veri setini kullanaca\u011f\u0131z.<\/span><\/p>\n
 Bu veri setini, belirli bir yolcunun hayatta kal\u0131p kalmad\u0131\u011f\u0131n\u0131 tahmin etmek i\u00e7in s\u0131n\u0131f<\/em> , cinsiyet<\/em> ve ya\u015f<\/em> tahmin de\u011fi\u015fkenlerini kullanan bir s\u0131n\u0131fland\u0131rma a\u011fac\u0131 olu\u015fturmak i\u00e7in kullanaca\u011f\u0131z.<\/span><\/p>\n
 Bu s\u0131n\u0131fland\u0131rma a\u011fac\u0131n\u0131 olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki ad\u0131mlar\u0131 kullan\u0131n.<\/span><\/p>\n
 Ad\u0131m 1: Gerekli paketleri y\u00fckleyin.<\/strong><\/span><\/p>\n
 \u00d6ncelikle bu \u00f6rnek i\u00e7in gerekli paketleri y\u00fckleyece\u011fiz:<\/span><\/p>\n
 library<\/span> (rpart) #for fitting decision trees<\/span>\nlibrary<\/span> (rpart.plot) #for plotting decision trees\n<\/span><\/strong><\/pre>\n
 Ad\u0131m 2: \u0130lk s\u0131n\u0131fland\u0131rma a\u011fac\u0131n\u0131 olu\u015fturun.<\/strong><\/span><\/p>\n
 \u0130lk olarak geni\u015f bir ba\u015flang\u0131\u00e7 s\u0131n\u0131fland\u0131rma a\u011fac\u0131 olu\u015fturaca\u011f\u0131z. “Karma\u015f\u0131kl\u0131k parametresi” anlam\u0131na gelen cp<\/strong> i\u00e7in k\u00fc\u00e7\u00fck bir de\u011fer kullanarak a\u011fac\u0131n b\u00fcy\u00fck oldu\u011funu garanti edebiliriz.<\/span><\/p>\n
 Bu, genel model uyumu en az\u0131ndan cp taraf\u0131ndan belirtilen de\u011fer kadar artt\u0131\u011f\u0131 s\u00fcrece s\u0131n\u0131fland\u0131rma a\u011fac\u0131nda daha fazla b\u00f6l\u00fcnme ger\u00e7ekle\u015ftirece\u011fimiz anlam\u0131na gelir.<\/span><\/p>\n
 Daha sonra model sonu\u00e7lar\u0131n\u0131 yazd\u0131rmak i\u00e7in printcp()<\/strong> fonksiyonunu kullanaca\u011f\u0131z:<\/span><\/p>\n
 #build the initial tree\n<\/span>tree <- rpart(survived~pclass+sex+age, data=ptitanic, control=rpart. control<\/span> (cp= .0001<\/span> ))\n\n#view results<\/span>\nprintcp(tree)\n\nVariables actually used in tree construction:\n[1] age pclass sex   \n\nRoot node error: 500\/1309 = 0.38197\n\nn=1309 \n\n      CP nsplit rel error xerror xstd\n1 0.4240 0 1.000 1.000 0.035158\n2 0.0140 1 0.576 0.576 0.029976\n3 0.0095 3 0.548 0.578 0.030013\n4 0.0070 7 0.510 0.552 0.029517\n5 0.0050 9 0.496 0.528 0.029035\n6 0.0025 11 0.486 0.532 0.029117\n7 0.0020 19 0.464 0.536 0.029198\n8 0.0001 22 0.458 0.528 0.029035\n<\/span><\/span><\/strong><\/pre>\n
 Ad\u0131m 3: A\u011fac\u0131 buday\u0131n.<\/strong><\/span><\/p>\n
 Daha sonra, en d\u00fc\u015f\u00fck test hatas\u0131na yol a\u00e7an cp (karma\u015f\u0131kl\u0131k parametresi) i\u00e7in kullan\u0131lacak en uygun de\u011feri bulmak amac\u0131yla regresyon a\u011fac\u0131n\u0131 budayaca\u011f\u0131z.<\/span><\/p>\n
 cp i\u00e7in en uygun de\u011ferin \u00f6nceki \u00e7\u0131kt\u0131daki en d\u00fc\u015f\u00fck x hatas\u0131na<\/strong> yol a\u00e7an de\u011fer oldu\u011funa dikkat edin; bu, \u00e7apraz do\u011frulama verilerinden elde edilen g\u00f6zlemlerdeki hatay\u0131 temsil eder.<\/span> <\/p>\n
 #identify best cp value to use\nbest <- tree$cptable[which. min<\/span> (tree$cptable[,\" xerror<\/span> \"]),\" CP<\/span> \"]\n\n#produce a pruned tree based on the best cp value\n<\/span>pruned_tree <- prune<\/span> (tree, cp=best)\n\n#plot the pruned tree\n<\/span>prp(pruned_tree,\n    faclen= 0<\/span> , #use full names for factor labels\n    extra= 1<\/span> , #display number of obs. for each terminal node\n    roundint= F<\/span> , #don't round to integers in output\n    digits= 5<\/span> ) #display 5 decimal places in output\n<\/span><\/span><\/span><\/strong><\/pre>\n
\"R'de<\/p>\n
 Son budanm\u0131\u015f a\u011fac\u0131n 10 terminal d\u00fc\u011f\u00fcm\u00fcne sahip oldu\u011funu g\u00f6rebiliriz. Her terminal d\u00fc\u011f\u00fcm\u00fc, hayatta kalanlar\u0131n yan\u0131 s\u0131ra \u00f6len yolcular\u0131n say\u0131s\u0131n\u0131 da g\u00f6sterir.<\/span><\/p>\n
 \u00d6rne\u011fin en soldaki d\u00fc\u011f\u00fcmde 664 yolcunun \u00f6ld\u00fc\u011f\u00fcn\u00fc, 136 yolcunun hayatta kald\u0131\u011f\u0131n\u0131 g\u00f6r\u00fcyoruz.<\/span> <\/p>\n
\"R'deki<\/p>\n
 Ad\u0131m 4: Tahminlerde bulunmak i\u00e7in a\u011fac\u0131 kullan\u0131n.<\/strong><\/span><\/p>\n
 Belirli bir yolcunun s\u0131n\u0131f\u0131na, ya\u015f\u0131na ve cinsiyetine g\u00f6re hayatta kalma olas\u0131l\u0131\u011f\u0131n\u0131 tahmin etmek i\u00e7in son budanm\u0131\u015f a\u011fac\u0131 kullanabiliriz.<\/span><\/p>\n
 \u00d6rne\u011fin 8 ya\u015f\u0131nda ve 1. s\u0131n\u0131ftaki bir erkek yolcunun hayatta kalma olas\u0131l\u0131\u011f\u0131 11\/29 = %37,9’dur.<\/span> <\/p>\n
\"R'de<\/p>\n
 Bu \u00f6rneklerde kullan\u0131lan R kodunun tamam\u0131n\u0131 burada<\/a> bulabilirsiniz.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
Bir dizi yorday\u0131c\u0131 de\u011fi\u015fken ile bir yan\u0131t de\u011fi\u015fkeni aras\u0131ndaki ili\u015fki do\u011frusal oldu\u011funda, \u00e7oklu do\u011frusal regresyon gibi y\u00f6ntemler, do\u011fru \u00f6ng\u00f6r\u00fcc\u00fc modeller \u00fcretebilir. Bununla birlikte, bir dizi \u00f6ng\u00f6r\u00fc ile bir yan\u0131t aras\u0131ndaki ili\u015fki daha karma\u015f\u0131k oldu\u011funda do\u011frusal olmayan y\u00f6ntemler genellikle daha do\u011fru modeller \u00fcretebilir. B\u00f6yle bir y\u00f6ntem, bir yan\u0131t de\u011fi\u015fkeninin de\u011ferini tahmin eden karar a\u011fa\u00e7lar\u0131 olu\u015fturmak i\u00e7in […]<\/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-1220","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\nR'ye s\u0131n\u0131fland\u0131rma ve regresyon a\u011fa\u00e7lar\u0131 nas\u0131l s\u0131\u011fd\u0131r\u0131l\u0131r<\/title>\n<meta name=\"description\" content=\"Bu e\u011fitimde, ad\u0131m ad\u0131m \u00f6rneklerle s\u0131n\u0131fland\u0131rma ve regresyon a\u011fa\u00e7lar\u0131n\u0131n R'ye nas\u0131l s\u0131\u011fd\u0131r\u0131laca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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