A\u015fa\u011f\u0131daki ad\u0131m ad\u0131m \u00f6rnek, R’de LOESS regresyonunun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini g\u00f6sterir.<\/span><\/p>\n \u00d6ncelikle R’de a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesini olu\u015ftural\u0131m:<\/span><\/p>\n Span<\/strong> parametresi i\u00e7in farkl\u0131 de\u011ferler kullanarak birden fazla LOESS regresyon modelini bu veri k\u00fcmesine s\u0131\u011fd\u0131rmak i\u00e7in loess()<\/strong> i\u015flevini kullanabiliriz:<\/span> <\/p>\n Span<\/strong> i\u00e7in kulland\u0131\u011f\u0131m\u0131z de\u011fer ne kadar d\u00fc\u015f\u00fck olursa, regresyon modelinin o kadar az “p\u00fcr\u00fczs\u00fcz” olaca\u011f\u0131n\u0131 ve modelin veri noktalar\u0131na uymaya o kadar fazla \u00e7al\u0131\u015faca\u011f\u0131n\u0131 unutmay\u0131n.<\/span><\/p>\n Kullan\u0131lacak en uygun kapsam<\/strong> de\u011ferini bulmak i\u00e7in, d\u00fczeltme<\/strong> paketindeki i\u015flevleri kullanarak k-katl\u0131 \u00e7apraz do\u011frulama<\/a> ger\u00e7ekle\u015ftirebiliriz:<\/span><\/p>\n Ortalama karek\u00f6k hata<\/a> (RMSE) i\u00e7in en d\u00fc\u015f\u00fck de\u011feri \u00fcreten span<\/strong> de\u011ferinin 0,7<\/strong> oldu\u011funu g\u00f6r\u00fcyoruz.<\/span><\/p>\n Dolay\u0131s\u0131yla, son LOESS regresyon modelimiz i\u00e7in, loess()<\/strong> fonksiyonundaki span<\/strong> arg\u00fcman\u0131 i\u00e7in 0,7<\/strong> de\u011ferini kullanmay\u0131 se\u00e7ece\u011fiz.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimler R’deki regresyon modelleri hakk\u0131nda ek bilgi sa\u011flar:<\/span><\/p>\n R’de basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a> Bazen yerel regresyon olarak da adland\u0131r\u0131lan LOESS regresyonu , bir regresyon modelini bir veri k\u00fcmesine s\u0131\u011fd\u0131rmak i\u00e7in yerel ayarlamay\u0131 kullanan bir y\u00f6ntemdir. A\u015fa\u011f\u0131daki ad\u0131m ad\u0131m \u00f6rnek, R’de LOESS regresyonunun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini g\u00f6sterir. 1. Ad\u0131m: Verileri olu\u015fturun \u00d6ncelikle R’de a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesini olu\u015ftural\u0131m: #view DataFrame df <- data. frame (x=c(1, 2, 3, 4, 5, 6, 7, […]<\/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-3151","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n 1. Ad\u0131m: Verileri olu\u015fturun<\/strong><\/span><\/h3>\n
#view DataFrame\n<\/span>df <- data. frame<\/span> (x=c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14),\n y=c(1, 4, 7, 13, 19, 24, 20, 15, 13, 11, 15, 18, 22, 27))\n\n#view first six rows of data frame<\/span>\nhead(df)\n\n xy\n1 1 1\n2 2 4\n3 3 7\n4 4 13\n5 5 19\n6 6 24\n<\/strong><\/pre>\n Ad\u0131m 2: Birden fazla LOESS regresyon modelini yerle\u015ftirin<\/strong><\/span><\/h3>\n
#fit several LOESS regression models to dataset\n<\/span>loess50 <- loess(y ~ x, data=df, span= .5<\/span> )\nsmooth50 <- predict(loess50) \n\nloess75 <- loess(y ~ x, data=df, span= .75<\/span> )\nsmooth75 <- predict(loess75) \n\nloess90 <- loess(y ~ x, data=df, span= .9<\/span> )\nsmooth90 <- predict(loess90) \n\n#create scatterplot with each regression line overlaid\n<\/span>plot(df$x, df$y, pch= 19<\/span> , main=' Loess Regression Models<\/span> ')\nlines(smooth50, x=df$x, col=' red<\/span> ')\nlines(smooth75, x=df$x, col=' purple<\/span> ')\nlines(smooth90, x=df$x, col=' blue<\/span> ')\nlegend(' bottomright<\/span> ', legend=c(' .5<\/span> ', ' .75<\/span> ', ' .9<\/span> '),\n col=c(' red<\/span> ', ' purple<\/span> ', ' blue<\/span> '), pch= 19<\/span> , title=' Smoothing Span<\/span> ')\n<\/strong><\/span><\/pre>\n![\"R'de](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/loess1.jpg\")
<\/p>\n Ad\u0131m 3: En \u0130yi Modeli Bulmak i\u00e7in K-Fold \u00c7apraz Do\u011frulamay\u0131 Kullan\u0131n<\/strong><\/span><\/h3>\n
library<\/span> (caret)\n\n#define k-fold cross validation method\n<\/span>ctrl <- trainControl(method = \" cv<\/span> \", number = 5<\/span> )\ngrid <- expand. grid<\/span> (span = seq( 0.5<\/span> , 0.9<\/span> , len = 5<\/span> ), degree = 1<\/span> )\n\n#perform cross-validation using smoothing spans ranging from 0.5 to 0.9\n<\/span>model <- train(y ~ x, data = df, method = \" gamLoess<\/span> \", tuneGrid=grid, trControl = ctrl)\n\n#print results of k-fold cross-validation\n<\/span>print<\/span> (model)\n\n14 samples\n 1 predictor\n\nNo pre-processing\nResampling: Cross-Validated (5 fold) \nSummary of sample sizes: 12, 11, 11, 11, 11 \nResampling results across tuning parameters:\n\n span RMSE Rsquared MAE \n 0.5 10.148315 0.9570137 6.467066\n 0.6 7.854113 0.9350278 5.343473\n 0.7 6.113610 0.8150066 4.769545\n 0.8 17.814105 0.8202561 11.875943\n 0.9 26.705626 0.7384931 17.304833\n\nTuning parameter 'degree' was held constant at a value of 1\nRMSE was used to select the optimal model using the smallest value.\nThe final values used for the model were span = 0.7 and degree = 1.<\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
R’de \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de lojistik regresyon nas\u0131l ger\u00e7ekle\u015ftirilir
R’de niceliksel regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de a\u011f\u0131rl\u0131kl\u0131 regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"