{"id":1213,"date":"2023-07-27T06:34:51","date_gmt":"2023-07-27T06:34:51","guid":{"rendered":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%83%e1%85%a1%e1%84%87%e1%85%a7%e1%86%ab%e1%84%85%e1%85%a3%e1%86%bc-%e1%84%8c%e1%85%a5%e1%86%a8%e1%84%8b%e1%85%b3%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%89\/"},"modified":"2023-07-27T06:34:51","modified_gmt":"2023-07-27T06:34:51","slug":"r%e1%84%8b%e1%85%b4-%e1%84%83%e1%85%a1%e1%84%87%e1%85%a7%e1%86%ab%e1%84%85%e1%85%a3%e1%86%bc-%e1%84%8c%e1%85%a5%e1%86%a8%e1%84%8b%e1%85%b3%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%89","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%83%e1%85%a1%e1%84%87%e1%85%a7%e1%86%ab%e1%84%85%e1%85%a3%e1%86%bc-%e1%84%8c%e1%85%a5%e1%86%a8%e1%84%8b%e1%85%b3%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%89\/","title":{"rendered":"R\uc758 \ub2e4\ubcc0\ub7c9 \uc801\uc751 \ud68c\uadc0 \uc2a4\ud50c\ub77c\uc778"},"content":{"rendered":"

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MARS( \ub2e4\ubcc0\ub7c9 \uc801\uc751\ud615 \ud68c\uadc0 \uc2a4\ud50c\ub77c\uc778<\/a> )\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc77c\ub828\uc758 \uc608\uce21 \ubcc0\uc218\uc640 \ubc18\uc751 \ubcc0\uc218<\/a> \uac04\uc758 \ube44\uc120\ud615 \uad00\uacc4\ub97c \ubaa8\ub378\ub9c1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \uc791\ub3d9\ud569\ub2c8\ub2e4.<\/span><\/p>\n

1.<\/strong> \ub370\uc774\ud130 \uc138\ud2b8\ub97c k<\/em> \uac1c \uc870\uac01\uc73c\ub85c \ub098\ub215\ub2c8\ub2e4.<\/span><\/p>\n

2.<\/strong> \ud68c\uadc0 \ubaa8\ub378\uc744 \uac01 \ubd80\ubd84\uc5d0 \ub9de\ucda5\ub2c8\ub2e4.<\/span><\/p>\n

3.<\/strong> k-\uacb9 \uad50\ucc28 \uac80\uc99d\uc744 \uc0ac\uc6a9\ud558\uc5ec k<\/em> \uac12\uc744 \uc120\ud0dd\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 MARS \ubaa8\ub378\uc744 R\uc758 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \ub9de\ucd94\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n

1\ub2e8\uacc4: \ud544\uc694\ud55c \ud328\ud0a4\uc9c0 \ub85c\ub4dc<\/strong><\/h3>\n

\uc774 \uc608\uc5d0\uc11c\ub294 ISLR Wage<\/strong> \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4 .  <\/em><\/strong> \uc5f0\ub839, \uad50\uc721, \uc778\uc885 \ub4f1\uacfc \uac19\uc740 \ub2e4\uc591\ud55c \uc608\uce21 \ubcc0\uc218\uc640 \ud568\uaed8 3,000\uba85\uc758 \uc5f0\ubd09\uc774 \ud3ec\ud568\ub41c \ud328\ud0a4\uc9c0\uc785\ub2c8\ub2e4.<\/span><\/p>\n

MARS \ubaa8\ub378\uc744 \ub370\uc774\ud130\uc5d0 \ub9de\ucd94\uae30 \uc804\uc5d0 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \ub85c\ub4dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n

 library<\/span> (ISLR) #contains Wage dataset<\/span>\nlibrary<\/span> (dplyr) #data wrangling<\/span>\nlibrary<\/span> (ggplot2) #plotting<\/span>\nlibrary<\/span> (earth) #fitting MARS models<\/span>\nlibrary<\/span> (caret) #tuning model parameters<\/span>\n<\/strong><\/pre>\n
 2\ub2e8\uacc4: \ub370\uc774\ud130 \ubcf4\uae30<\/strong><\/h3>\n
 \ub2e4\uc74c\uc73c\ub85c \uc791\uc5c5 \uc911\uc778 \ub370\uc774\ud130 \uc138\ud2b8\uc758 \ucc98\uc74c 6\uac1c \ud589\uc744 \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #view first six rows of data<\/span>\nhead<\/span> (Wage)\n\n       year age maritl race education region\n231655 2006 18 1. Never Married 1. White 1. < HS Grad 2. Middle Atlantic\n86582 2004 24 1. Never Married 1. White 4. College Grad 2. Middle Atlantic\n161300 2003 45 2. Married 1. White 3. Some College 2. Middle Atlantic\n155159 2003 43 2. Married 3. Asian 4. College Grad 2. Middle Atlantic\n11443 2005 50 4. Divorced 1. White 2. HS Grad 2. Middle Atlantic\n376662 2008 54 2. Married 1. White 4. College Grad 2. Middle Atlantic\n             jobclass health health_ins logwage wage\n231655 1. Industrial 1. <=Good 2. No 4.318063 75.04315\n86582 2. Information 2. >=Very Good 2. No 4.255273 70.47602\n161300 1. Industrial 1. <=Good 1. Yes 4.875061 130.98218\n155159 2. Information 2. >=Very Good 1. Yes 5.041393 154.68529\n11443 2. Information 1. <=Good 1. Yes 4.318063 75.04315\n376662 2. Information 2. >=Very Good 1. Yes 4.845098 127.11574\n<\/strong><\/pre>\n
 3\ub2e8\uacc4: MARS \ubaa8\ub378 \uc0dd\uc131 \ubc0f \ucd5c\uc801\ud654<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c\uc73c\ub85c, \uc774 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \ub300\ud55c MARS \ubaa8\ub378\uc744 \uc0dd\uc131\ud558\uace0 k-\uacb9 \uad50\ucc28 \uac80\uc99d\uc744<\/a> \uc218\ud589\ud558\uc5ec \uc5b4\ub5a4 \ubaa8\ub378\uc774 \uac00\uc7a5 \ub0ae\uc740 \ud14c\uc2a4\ud2b8 RMSE(\ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28)\ub97c \uc0dd\uc131\ud558\ub294\uc9c0 \uacb0\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #create a tuning grid\n<\/span>hyper_grid <- expand. grid<\/span> (degree = 1:3,\n                          nprune = seq<\/span> (2, 50, length.out = 10) %>%<\/span>\nfloor<\/span> ())\n\n#make this example reproducible\n<\/span>set.seed(1)\n\n#fit MARS model using k-fold cross-validation\n<\/span>cv_mars <- train(\n  x = subset(Wage, select = -c(wage, logwage)),\n  y = Wage$wage,\n  method = \" earth<\/span> \",\n  metric = \" RMSE<\/span> \",\n  trControl = trainControl(method = \" cv<\/span> \", number = 10),\n  tuneGrid = hyper_grid)\n\n#display model with lowest test RMSE<\/span>\ncv_mars$results %>%<\/span>\n  filter<\/span> (nprune==cv_mars$bestTune$nprune, degree =cv_mars$bestTune$degree)    \ndegree nprune RMSE Rsquared MAE RMSESD RsquaredSD MAESD\t\t\n1 12 33.8164 0.3431804 22.97108 2.240394 0.03064269 1.4554\n<\/span><\/strong><\/pre>\n
 \uacb0\uacfc\uc5d0\uc11c \uac00\uc7a5 \ub0ae\uc740 \ud14c\uc2a4\ud2b8 MSE\ub97c \uc0dd\uc131\ud55c \ubaa8\ub378\uc740 1\ucc28 \ud6a8\uacfc\ub9cc \uc788\uace0(\uc989, \uc0c1\ud638 \uc791\uc6a9 \ud56d \uc5c6\uc74c) 12\uac1c\uc758 \ud56d\uc774 \uc788\ub294 \ubaa8\ub378\uc784\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 33.8164<\/strong> \uc758 RMSE(\uc81c\uacf1\ud3c9\uade0\uc81c\uacf1\uadfc \uc624\ucc28)\ub97c \uc0dd\uc131\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ub610\ud55c \uc6a9\uc5b4\uc758 \uc815\ub3c4\uc640 \uc218\ub97c \uae30\ubc18\uc73c\ub85c RMSE \ud14c\uc2a4\ud2b8\ub97c \uc2dc\uac01\ud654\ud558\ub294 \ucc28\ud2b8\ub97c \ub9cc\ub4e4 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 #display test RMSE by terms and degree<\/span>\nggplot(cv_mars)\n<\/span><\/strong><\/pre>\n
\"R\uc758<\/p>\n
 \uc2e4\uc81c\ub85c \uc6b0\ub9ac\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc5ec\ub7ec \ub2e4\ub978 \uc720\ud615\uc758 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec MARS \ubaa8\ub378\uc744 \uc801\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ucc38\uace0:<\/strong> MARS \ubaa8\ub378\uc744 \uc9c0\uc815\ud558\uae30 \uc704\ud574 method=”earth”\ub97c \uc0ac\uc6a9\ud588\uc2b5\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc5d0 \ub300\ud55c \ubb38\uc11c\ub294 \uc5ec\uae30\uc5d0\uc11c<\/a> \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/em><\/p>\n