Convert decision tree objects into tidy data frames with broomstick.
The goal of broomstick is to extend the broom package to work with decision trees. It is currently borrowing heavily from the prototype package treezy.
rpart
library(rpart)
library(broomstick)
fit_rpart <- rpart(Kyphosis ~ Age + Number + Start,
data = kyphosis)
tidy(fit_rpart)
#> # A tibble: 3 x 2
#> variable importance
#> <chr> <dbl>
#> 1 Start 8.20
#> 2 Age 3.10
#> 3 Number 1.52
augment(fit_rpart)
#> # A tibble: 81 x 6
#> Kyphosis Age Number Start .fitted[,"absen… [,"present"] .resid[,"absent…
#> <fct> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 absent 71 3 5 0.421 0.579 -0.579
#> 2 absent 158 3 14 0.857 0.143 -0.143
#> 3 present 128 4 5 0.421 0.579 -1.58
#> 4 absent 2 5 1 0.421 0.579 -0.579
#> 5 absent 1 4 15 1 0 0
#> 6 absent 1 2 16 1 0 0
#> 7 absent 61 2 17 1 0 0
#> 8 absent 37 3 16 1 0 0
#> 9 absent 113 2 16 1 0 0
#> 10 present 59 6 12 0.429 0.571 -1.57
#> # … with 71 more rows, and 1 more variable: [,"present"] <dbl>gbm (Boosted Regression Tree)
library(gbm)
#> Loaded gbm 2.1.8
library(MASS)
fit_gbm <- gbm(calories ~., data = UScereal)
#> Distribution not specified, assuming gaussian ...
tidy(fit_gbm)
#> # A tibble: 10 x 2
#> variable importance
#> <chr> <dbl>
#> 1 1 30.7
#> 2 2 23.5
#> 3 3 14.3
#> 4 4 11.5
#> 5 5 8.14
#> 6 6 6.75
#> 7 7 2.96
#> 8 8 1.95
#> 9 9 0.198
#> 10 10 0random forest
library(randomForest)
#> randomForest 4.6-14
#> Type rfNews() to see new features/changes/bug fixes.
ozone_rf <- randomForest(Ozone ~ .,
data = airquality,
importance = TRUE,
na.action = na.omit)
tidy(ozone_rf)
#> Warning: This function is deprecated as of broom 0.7.0 and will be removed from
#> a future release. Please see tibble::as_tibble().
#> # A tibble: 5 x 4
#> term X.IncMSE IncNodePurity imp_sd
#> <chr> <dbl> <dbl> <dbl>
#> 1 Solar.R 151. 18103. 11.0
#> 2 Wind 326. 29153. 17.6
#> 3 Temp 485. 35513. 18.6
#> 4 Month 107. 10803. 10.4
#> 5 Day 62.5 16332. 10.6
glance(ozone_rf)
#> mean_mse mean_rsq
#> 1 342.6329 0.6877532
augment(ozone_rf)
#> Warning in augment.randomForest.method(x, data, ...): casewise importance
#> measures are not available. Run randomForest(..., localImp = TRUE) for more
#> detailed results.
#> Ozone Solar.R Wind Temp Month Day .oob_times .fitted
#> 1 41 190 7.4 67 5 1 188 42.25838
#> 2 36 118 8.0 72 5 2 197 26.11229
#> 3 12 149 12.6 74 5 3 197 24.96252
#> 4 18 313 11.5 62 5 4 168 24.20249
#> 5 NA NA 14.3 56 5 5 NA NA
#> 6 28 NA 14.9 66 5 6 NA NA
#> 7 23 299 8.6 65 5 7 180 29.26588
#> 8 19 99 13.8 59 5 8 181 18.78945
#> 9 8 19 20.1 61 5 9 184 14.48941
#> 10 NA 194 8.6 69 5 10 NA NA
#> 11 7 NA 6.9 74 5 11 NA NA
#> 12 16 256 9.7 69 5 12 160 20.25724
#> 13 11 290 9.2 66 5 13 164 22.67090
#> 14 14 274 10.9 68 5 14 174 22.40194
#> 15 18 65 13.2 58 5 15 199 13.99352
#> 16 14 334 11.5 64 5 16 202 23.91045
#> 17 34 307 12.0 66 5 17 185 19.91413
#> 18 6 78 18.4 57 5 18 194 18.64684
#> 19 30 322 11.5 68 5 19 175 19.05428
#> 20 11 44 9.7 62 5 20 175 11.79332
#> 21 1 8 9.7 59 5 21 183 13.96870
#> 22 11 320 16.6 73 5 22 174 24.16417
#> 23 4 25 9.7 61 5 23 190 14.33131
#> 24 32 92 12.0 61 5 24 176 18.18554
#> 25 NA 66 16.6 57 5 25 NA NA
#> 26 NA 266 14.9 58 5 26 NA NA
#> 27 NA NA 8.0 57 5 27 NA NA
#> 28 23 13 12.0 67 5 28 180 20.88715
#> 29 45 252 14.9 81 5 29 189 46.32726
#> 30 115 223 5.7 79 5 30 191 55.56009
#> 31 37 279 7.4 76 5 31 186 46.92523
#> 32 NA 286 8.6 78 6 1 NA NA
#> 33 NA 287 9.7 74 6 2 NA NA
#> 34 NA 242 16.1 67 6 3 NA NA
#> 35 NA 186 9.2 84 6 4 NA NA
#> 36 NA 220 8.6 85 6 5 NA NA
#> 37 NA 264 14.3 79 6 6 NA NA
#> 38 29 127 9.7 82 6 7 188 27.59685
#> 39 NA 273 6.9 87 6 8 NA NA
#> 40 71 291 13.8 90 6 9 191 49.68501
#> 41 39 323 11.5 87 6 10 168 54.05047
#> 42 NA 259 10.9 93 6 11 NA NA
#> 43 NA 250 9.2 92 6 12 NA NA
#> 44 23 148 8.0 82 6 13 188 34.53176
#> 45 NA 332 13.8 80 6 14 NA NA
#> 46 NA 322 11.5 79 6 15 NA NA
#> 47 21 191 14.9 77 6 16 179 26.24771
#> 48 37 284 20.7 72 6 17 171 22.30556
#> 49 20 37 9.2 65 6 18 191 15.30853
#> 50 12 120 11.5 73 6 19 170 22.07452
#> 51 13 137 10.3 76 6 20 183 22.79000
#> 52 NA 150 6.3 77 6 21 NA NA
#> 53 NA 59 1.7 76 6 22 NA NA
#> 54 NA 91 4.6 76 6 23 NA NA
#> 55 NA 250 6.3 76 6 24 NA NA
#> 56 NA 135 8.0 75 6 25 NA NA
#> 57 NA 127 8.0 78 6 26 NA NA
#> 58 NA 47 10.3 73 6 27 NA NA
#> 59 NA 98 11.5 80 6 28 NA NA
#> 60 NA 31 14.9 77 6 29 NA NA
#> 61 NA 138 8.0 83 6 30 NA NA
#> 62 135 269 4.1 84 7 1 184 71.89886
#> 63 49 248 9.2 85 7 2 167 64.37778
#> 64 32 236 9.2 81 7 3 187 45.91065
#> 65 NA 101 10.9 84 7 4 NA NA
#> 66 64 175 4.6 83 7 5 174 75.24531
#> 67 40 314 10.9 83 7 6 183 49.88416
#> 68 77 276 5.1 88 7 7 200 85.00543
#> 69 97 267 6.3 92 7 8 173 86.20607
#> 70 97 272 5.7 92 7 9 173 87.64815
#> 71 85 175 7.4 89 7 10 153 73.97870
#> 72 NA 139 8.6 82 7 11 NA NA
#> 73 10 264 14.3 73 7 12 178 28.75746
#> 74 27 175 14.9 81 7 13 164 39.57709
#> 75 NA 291 14.9 91 7 14 NA NA
#> 76 7 48 14.3 80 7 15 187 25.77671
#> 77 48 260 6.9 81 7 16 184 46.63660
#> 78 35 274 10.3 82 7 17 194 37.74192
#> 79 61 285 6.3 84 7 18 192 69.86593
#> 80 79 187 5.1 87 7 19 184 73.84940
#> 81 63 220 11.5 85 7 20 195 51.65887
#> 82 16 7 6.9 74 7 21 187 27.20911
#> 83 NA 258 9.7 81 7 22 NA NA
#> 84 NA 295 11.5 82 7 23 NA NA
#> 85 80 294 8.6 86 7 24 197 58.13895
#> 86 108 223 8.0 85 7 25 177 72.65444
#> 87 20 81 8.6 82 7 26 181 45.41705
#> 88 52 82 12.0 86 7 27 176 43.16430
#> 89 82 213 7.4 88 7 28 178 73.00805
#> 90 50 275 7.4 86 7 29 190 73.45676
#> 91 64 253 7.4 83 7 30 188 62.06175
#> 92 59 254 9.2 81 7 31 169 52.61721
#> 93 39 83 6.9 81 8 1 171 40.68605
#> 94 9 24 13.8 81 8 2 181 27.34251
#> 95 16 77 7.4 82 8 3 184 37.33898
#> 96 78 NA 6.9 86 8 4 NA NA
#> 97 35 NA 7.4 85 8 5 NA NA
#> 98 66 NA 4.6 87 8 6 NA NA
#> 99 122 255 4.0 89 8 7 179 91.82286
#> 100 89 229 10.3 90 8 8 191 66.99773
#> 101 110 207 8.0 90 8 9 200 75.82581
#> 102 NA 222 8.6 92 8 10 NA NA
#> 103 NA 137 11.5 86 8 11 NA NA
#> 104 44 192 11.5 86 8 12 208 56.88581
#> 105 28 273 11.5 82 8 13 193 38.49338
#> 106 65 157 9.7 80 8 14 195 30.41331
#> 107 NA 64 11.5 79 8 15 NA NA
#> 108 22 71 10.3 77 8 16 200 21.41058
#> 109 59 51 6.3 79 8 17 193 37.36250
#> 110 23 115 7.4 76 8 18 184 29.75137
#> 111 31 244 10.9 78 8 19 196 34.67882
#> 112 44 190 10.3 78 8 20 206 34.74004
#> 113 21 259 15.5 77 8 21 176 25.74759
#> 114 9 36 14.3 72 8 22 181 16.11446
#> 115 NA 255 12.6 75 8 23 NA NA
#> 116 45 212 9.7 79 8 24 203 56.69674
#> 117 168 238 3.4 81 8 25 194 67.14026
#> 118 73 215 8.0 86 8 26 166 85.68238
#> 119 NA 153 5.7 88 8 27 NA NA
#> 120 76 203 9.7 97 8 28 191 74.62102
#> 121 118 225 2.3 94 8 29 182 104.91248
#> 122 84 237 6.3 96 8 30 186 90.26471
#> 123 85 188 6.3 94 8 31 192 81.15797
#> 124 96 167 6.9 91 9 1 181 72.59516
#> 125 78 197 5.1 92 9 2 179 81.48244
#> 126 73 183 2.8 93 9 3 183 93.03619
#> 127 91 189 4.6 93 9 4 173 77.15980
#> 128 47 95 7.4 87 9 5 181 52.03776
#> 129 32 92 15.5 84 9 6 174 37.75896
#> 130 20 252 10.9 80 9 7 184 40.05420
#> 131 23 220 10.3 78 9 8 196 35.00435
#> 132 21 230 10.9 75 9 9 188 28.14067
#> 133 24 259 9.7 73 9 10 187 28.94787
#> 134 44 236 14.9 81 9 11 174 29.27522
#> 135 21 259 15.5 76 9 12 183 22.60115
#> 136 28 238 6.3 77 9 13 193 46.98184
#> 137 9 24 10.9 71 9 14 200 16.55020
#> 138 13 112 11.5 71 9 15 173 21.91143
#> 139 46 237 6.9 78 9 16 186 33.19160
#> 140 18 224 13.8 67 9 17 181 22.12481
#> 141 13 27 10.3 76 9 18 190 16.28911
#> 142 24 238 10.3 68 9 19 181 22.70507
#> 143 16 201 8.0 82 9 20 170 43.56690
#> 144 13 238 12.6 64 9 21 178 22.78182
#> 145 23 14 9.2 71 9 22 196 15.44979
#> 146 36 139 10.3 81 9 23 171 25.90560
#> 147 7 49 10.3 69 9 24 187 20.33940
#> 148 14 20 16.6 63 9 25 188 21.48303
#> 149 30 193 6.9 70 9 26 175 36.76303
#> 150 NA 145 13.2 77 9 27 NA NA
#> 151 14 191 14.3 75 9 28 181 25.86641
#> 152 18 131 8.0 76 9 29 188 32.89799
#> 153 20 223 11.5 68 9 30 174 34.24170
