{"id":4439,"date":"2023-07-11T02:36:29","date_gmt":"2023-07-11T02:36:29","guid":{"rendered":"https:\/\/statorials.org\/ko\/r-%e1%84%85%e1%85%a9%e1%84%8c%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%b5%e1%86%a8-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8\/"},"modified":"2023-07-11T02:36:29","modified_gmt":"2023-07-11T02:36:29","slug":"r-%e1%84%85%e1%85%a9%e1%84%8c%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%b5%e1%86%a8-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r-%e1%84%85%e1%85%a9%e1%84%8c%e1%85%b5%e1%84%89%e1%85%b3%e1%84%90%e1%85%b5%e1%86%a8-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8\/","title":{"rendered":"R\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uacfc \ud568\uaed8 predict()\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">R\uc5d0 <a href=\"https:\/\/statorials.org\/ko\/\u1105\u1169\u110c\u1175\u1109\u1173\u1110\u1175\u11a8-\u1112\u116c\u1100\u1171-1\/\" target=\"_blank\" rel=\"noopener\">\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744<\/a> \uc801\uc6a9\ud55c \ud6c4\uc5d0 <strong>\ub294 \uc608\uce21()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc774 \uc774\uc804\uc5d0 \ubcf8 \uc801\uc774 \uc5c6\ub294 \uc0c8\ub85c\uc6b4 \uad00\ucc30\uc758 \uc751\ub2f5 \uac12\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ud568\uc218\ub294 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\uc608\uce21(\uac1d\uccb4, newdata, \uc720\ud615 = &#8220;\uc751\ub2f5&#8221;)<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uae08:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\uac1d\uccb4:<\/strong> \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc758 \uc774\ub984<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>newdata:<\/strong> \uc608\uce21\ud560 \uc0c8 \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \uc774\ub984<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>type:<\/strong> \uc608\uce21\ud560 \uc720\ud615<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc774 \uae30\ub2a5\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\uc608: R\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uacfc \ud568\uaed8 Predict() \uc0ac\uc6a9<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\uc774 \uc608\uc5d0\uc11c\ub294 <a href=\"https:\/\/statorials.org\/ko\/mtcars-r-\u1103\u1166\u110b\u1175\u1110\u1165-\u1109\u1166\u1110\u1173\/\" target=\"_blank\" rel=\"noopener\">mtcars<\/a> \ub77c\ub294 \ub0b4\uc7a5 R \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view first six rows of <em>mtcars<\/em> dataset<\/span>\nhead(mtcars)\n\n                   mpg cyl disp hp drat wt qsec vs am gear carb\nMazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\nMazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\nDatsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\nHornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\nHornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\nValiant 18.1 6 225 105 2.76 3,460 20.22 1 0 3 1<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc6b0\ub9ac\ub294 \ubcc0\uc218 <strong>disp<\/strong> \uc640 <strong>hp\ub97c<\/strong> \uc0ac\uc6a9\ud558\uc5ec \uc751\ub2f5 \ubcc0\uc218 <strong>am<\/strong> (\uc790\ub3d9\ucc28\uc758 \ubcc0\uc18d\uae30 \uc720\ud615: 0 = \uc790\ub3d9, 1 = \uc218\ub3d9)\uc744 \uc608\uce21\ud558\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\ud569\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit logistic regression model<\/span>\nmodel &lt;- glm(am ~ disp + hp, data=mtcars, family=binomial)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = am ~ disp + hp, family = binomial, data = mtcars)\n\nDeviance Residuals: \n    Min 1Q Median 3Q Max  \n-1.9665 -0.3090 -0.0017 0.3934 1.3682  \n\nCoefficients:\n            Estimate Std. Error z value Pr(&gt;|z|)  \n(Intercept) 1.40342 1.36757 1.026 0.3048  \navailable -0.09518 0.04800 -1.983 0.0474 *\nhp 0.12170 0.06777 1.796 0.0725 .\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n    Null deviance: 43,230 on 31 degrees of freedom\nResidual deviance: 16,713 on 29 degrees of freedom\nAIC: 22,713\n\nNumber of Fisher Scoring iterations: 8\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uadf8\ub7f0 \ub2e4\uc74c \ubaa8\ub378\uc774 \uc774\uc804\uc5d0 \ubcf8 \uc801\uc774 \uc5c6\ub294 8\ub300\uc758 \uc790\ub3d9\ucc28\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \uc0c8 \ub370\uc774\ud130 \ud504\ub808\uc784\uc744 \uc0dd\uc131\ud558\uace0 <strong>\uc608\uce21()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0c8 \uc790\ub3d9\ucc28\uac00 \uc790\ub3d9 \ubcc0\uc18d\uae30(am=0) \ub610\ub294 \uc218\ub3d9 \ubcc0\uc18d\uae30\ub97c \uac00\uc9c8 \ud655\ub960\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4( \uc624\uc804 =1):<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define new data frame\n<span style=\"color: #000000;\">newdata = data. <span style=\"color: #3366ff;\">frame<\/span> (disp=c(200, 180, 160, 140, 120, 120, 100, 160),\n                     hp=c(100, 90, 108, 90, 80, 90, 80, 90),\n                     am=c(0, 0, 0, 1, 0, 1, 1, 1))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>newdata\n\n<span style=\"color: #008080;\">#use model to predict value of am for all new cars\n<\/span>newdata$am_prob &lt;- predict(model, newdata, type=\" <span style=\"color: #ff0000;\">response<\/span> \")\n\n<span style=\"color: #008080;\">#view updated data frame\n<\/span>newdata\n\n  disp hp am am_prob\n1 200 100 0 0.004225640\n2 180 90 0 0.008361069\n3 160 108 0 0.335916069\n4 140 90 1 0.275162866\n5 120 80 0 0.429961894\n6 120 90 1 0.718090728\n7 100 80 1 0.835013994\n8 160 90 1 0.053546152<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uacb0\uacfc\ub97c \ud574\uc11d\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\uc790\ub3d9\ucc28 1\uc774 \uc218\ub3d9\ubcc0\uc18d\uae30\uc77c \ud655\ub960\uc740 <strong>0.004<\/strong> \uc774\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc790\ub3d9\ucc28 2\uac00 \uc218\ub3d9\ubcc0\uc18d\uae30\uc77c \ud655\ub960\uc740 <strong>0.008<\/strong> \uc774\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\">\uc790\ub3d9\ucc28 3\uc774 \uc218\ub3d9\ubcc0\uc18d\uae30\uc77c \ud655\ub960\uc740 <strong>0.336<\/strong> \uc774\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\ub4f1\ub4f1.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub610\ud55c <strong>table()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc5d0\uc11c \uc608\uce21\ud55c \uac12\uacfc \uc2e4\uc81c am \uac12\uc744 \ud45c\uc2dc\ud558\ub294 \ud63c\ub3d9 \ud589\ub82c\uc744 \ub9cc\ub4e4 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create vector that contains 0 or 1 depending on predicted value of am\n<span style=\"color: #000000;\">am_pred = rep(0, dim(newdata)[1])\nam_pred[newdata$am_prob &gt; .5] = 1\n\n<span style=\"color: #008080;\">#create confusion matrix\n<\/span>table(am_pred, newdata$am)\n\nam_pred 0 1\n      0 4 2\n      1 0 2\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub9c8\uc9c0\ub9c9\uc73c\ub85c <strong>Mean()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc774 <strong>am<\/strong> \uac12\uc744 \uc62c\ubc14\ub974\uac8c \uc608\uce21\ud55c \uc0c8 \ub370\uc774\ud130\ubca0\uc774\uc2a4\uc758 \uad00\uce21\uce58 \ube44\uc728\uc744 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate percentage of observations the model correctly predicted response value for\n<span style=\"color: #000000;\">mean(am_pred == newdata$am)\n\n[1] 0.75\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ubaa8\ub378\uc774 \uc0c8 \ub370\uc774\ud130\ubca0\uc774\uc2a4\uc5d0 \uc788\ub294 \uc790\ub3d9\ucc28 \uc911 <strong>75%<\/strong> \uc5d0 \ub300\ud55c <strong>am<\/strong> \uac12\uc744 \uc62c\ubc14\ub974\uac8c \uc608\uce21\ud588\uc74c\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ko\/r\u110b\u1174-\u1103\u1161\u11ab\u1109\u116e\u11ab-\u1109\u1165\u11ab\u1112\u1167\u11bc-\u1112\u116c\u1100\u1171\/\" target=\"_blank\" rel=\"noopener\">R\uc5d0\uc11c \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1103\u1161\u110c\u116e\u11bc-\u1109\u1165\u11ab\u1112\u1167\u11bc-\u1112\u116c\u1100\u1171-r\/\" target=\"_blank\" rel=\"noopener\">R\uc5d0\uc11c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1103\u1161\u1112\u1161\u11bc\u1109\u1175\u11a8-\u1112\u116c\u1100\u1171-r\/\" target=\"_blank\" rel=\"noopener\">R\uc5d0\uc11c \ub2e4\ud56d\uc2dd \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u110b\u1168\u110e\u1173\u11a8-\u1100\u116e\u1100\u1161\u11ab-r\/\" target=\"_blank\" rel=\"noopener\">R\uc5d0\uc11c \uc608\uce21 \uad6c\uac04\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>R\uc5d0 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\uc6a9\ud55c \ud6c4\uc5d0 \ub294 \uc608\uce21() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc774 \uc774\uc804\uc5d0 \ubcf8 \uc801\uc774 \uc5c6\ub294 \uc0c8\ub85c\uc6b4 \uad00\ucc30\uc758 \uc751\ub2f5 \uac12\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc608\uce21(\uac1d\uccb4, newdata, \uc720\ud615 = &#8220;\uc751\ub2f5&#8221;) \uae08: \uac1d\uccb4: \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc758 \uc774\ub984 newdata: \uc608\uce21\ud560 \uc0c8 \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \uc774\ub984 type: \uc608\uce21\ud560 \uc720\ud615 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc774 \uae30\ub2a5\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. 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