/
eval-performance.Rmd
589 lines (515 loc) · 19.6 KB
/
eval-performance.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
---
title: "Evaluation of performances"
output:
workflowr::wflow_html:
includes:
in_header: header.html
editor_options:
chunk_output_type: console
author: "Patrick Schratz"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
fig.retina = 3,
fig.align = "center",
fig.width = 6.93,
fig.height = 6.13,
out.width = "70%",
echo = FALSE
)
options(
scipen = 999
)
R.utils::sourceDirectory("R")
library("drake")
# load drake objects
loadd(
bm_aggregated,
# for T4
benchmark_tune_results_hr_nri_vi
)
library("xtable")
library("flextable")
library("ggbeeswarm")
library("ggsci")
library("ggrepel")
library("ggpubr")
library("here")
library("mlr")
library("dplyr")
library("forcats")
```
Last update:
```{r}
date()
```
```{r, warning = FALSE}
df_perf <- getBMRPerformances(bm_aggregated, as.df = TRUE) %>%
mutate(task.id = recode_factor(task.id,
`hr` = "HR",
`vi` = "VI",
`nri` = "NRI",
`nri_vi` = "NRI-VI",
`hr_nri` = "HR-NRI",
`hr_vi` = "HR-VI",
`hr_nri_vi` = "HR-NRI-VI",
)) %>%
tidyr::separate(learner.id, c("learner_group", "filter"),
remove = FALSE,
sep = " MBO ",
fill = "right",
) %>%
mutate(learner_group = recode_factor(learner_group,
`XGBOOST` = "XGBoost"
))
```
```{r aggr-perf}
# Aggregate performances and add standard error column.
df_perf %<>%
dplyr::group_by(task.id, learner.id, filter) %>%
dplyr::mutate(rmse_aggr = round(mean(rmse), 3)) %>%
dplyr::mutate(se = round(sd(rmse), 3)) %>%
dplyr::select(-rmse, iter) %>%
dplyr::ungroup()
```
Fold performances of "SVM MBO No Filter" on the HR Task
- Fold 1: Laukiz1
- Fold 2: Laukiz2
- Fold 3: Luiando
- Fold 4: Oiartzun
```{r}
df <- mlr::getBMRPerformances(bm_aggregated,
"hr", "SVM MBO No Filter",
as.df = TRUE
)
df_tab <- df %>%
dplyr::select(iter, rmse) %>%
dplyr::rename(Plot = iter, RMSE = rmse) %>%
dplyr::mutate(`Test Plot` = as.character(Plot)) %>%
dplyr::mutate(`Test Plot` = forcats::fct_recode(`Test Plot`,
Laukiz1 = "1", Laukiz2 = "2",
Luiando = "3", Oiartzun = "4"
)) %>%
dplyr::select(-Plot) %>%
dplyr::mutate(RMSE = round(RMSE, 2))
df_tab %>%
xtable::xtable(
type = "latex",
caption = "Test fold performances in RMSE (p.p.) for learner SVM on the HR dataset without using a filter, showcasing performance variance on the fold level. For each row, the model was trained on observations from all others plots but the given one and tested on the observations of the given plot.",
label = "tab:svm-single-fold-perf"
) %>%
print(
file = here::here("docs/00-manuscripts/mdpi/performance-svm-single-plot.tex"),
include.rownames = TRUE,
latex.environments = c("center"),
table.placement = "ht!",
caption.placement = "top",
timestamp = NULL
)
saveRDS(df_tab, here("docs/00-manuscripts/presentation/table-svm-single-plot.rda"))
DT::datatable(df_tab)
```
## (Table) T1 All leaner/filter/task combinations ordered by performance.
Overall leaderboard across all settings, sorted ascending by performance.
```{r eval-performance-1, warning=FALSE}
table1 <- df_perf %>%
group_by(learner.id, task.id, filter) %>%
slice(which.min(rmse_aggr)) %>%
dplyr::rename(
"Model" = learner_group,
"Learner ID" = learner.id,
"Task" = task.id,
"Filter" = filter,
"RMSE" = rmse_aggr,
"SE" = se
) %>%
ungroup() %>%
mutate(Filter = replace(Filter, is.na(Filter), "No Filter")) %>%
select(-iter, -`Learner ID`, -rsq) %>%
relocate(RMSE, .after = Filter) %>%
relocate(`SE`, .after = RMSE) %>%
mutate(RMSE = round(RMSE, 3)) %>%
arrange(RMSE)
# save as latex table
table1 %>%
ungroup() %>%
arrange(RMSE) %>%
slice(1:10) %>%
xtable(
type = "latex",
caption = "Best ten results among all learner-task-filter combinations, sorted in decreasing order of RMSE (p.p.) and their respective standard error (SE).",
label = "tab:perf-top-10"
) %>%
print(
file = here("docs/00-manuscripts/mdpi/performance-top-10.tex"),
include.rownames = TRUE,
latex.environments = c("center"),
table.placement = "ht!",
caption.placement = "top",
timestamp = NULL,
sanitize.text.function = function(x) {
x
}
)
saveRDS(table1, here("docs/00-manuscripts/presentation/table-perf-top-10.rda"))
DT::datatable(table1)
```
## (Table) T2 Best learner/filter/task combination
Learners: On which task and using which filter did every learner score their best result on?
*CV: L2 penalized regression using the internal 10-fold CV tuning of the `glmnet` package
*MBO: L2 penalized regression using using MBO for hyperparameter optimization.
```{r eval-performance-2, warning=FALSE}
table2 <- df_perf %>%
group_by(learner_group) %>%
slice(which.min(rmse_aggr)) %>%
mutate(filter = replace(filter, is.na(filter), "No Filter")) %>%
arrange(rmse_aggr) %>%
# remove Task info for featureless.learner
dplyr::mutate(task.id = case_when(learner.id == "regr.featureless" ~ "-", TRUE ~ as.character(task.id))) %>%
dplyr::rename(
"Model" = learner_group,
"Learner ID" = learner.id,
"Task" = task.id,
"Filter" = filter,
"RMSE" = rmse_aggr,
"SE" = se
) %>%
select(-`Learner ID`, -rsq) %>%
relocate(RMSE, .after = Filter) %>%
relocate(`SE`, .after = RMSE) %>%
select(-iter)
# save as latex table
table2 %>%
xtable(
type = "latex",
caption = "The overall best individual learner performance across any task and filter method for RF, SVM, XGBoost, Lasso and Ridge, sorted ascending by RMSE (p.p.) including the respective standard error (SE) of the cross-validation run. For \texttt{regr.featureless} the Task is no applicable and was therefore removed.",
label = "tab:best-learner-perf"
) %>%
print(
file = here("docs/00-manuscripts/mdpi/performance-best-per-learner.tex"),
include.rownames = TRUE,
latex.environments = c("center"),
table.placement = "ht!",
scalebox = 0.90,
caption.placement = "top",
timestamp = NULL,
sanitize.text.function = function(x) {
x
}
)
saveRDS(table2, here("docs/00-manuscripts/presentation/table-best-learner-per-task.rda"))
DT::datatable(table2)
```
## (Table) T3 All leaner/filter/task combinations ordered descending by performance.
Overall leaderboard across all settings, sorted descending by performance.
```{r eval-performance-3, warning=FALSE}
table3 <- df_perf %>%
group_by(learner.id, task.id, filter) %>%
slice(which.min(rmse_aggr)) %>%
dplyr::rename(
"Model" = learner_group,
"Learner ID" = learner.id,
"Task" = task.id,
"Filter" = filter,
"RMSE" = rmse_aggr,
"SE" = se,
) %>%
ungroup() %>%
mutate(Filter = replace(Filter, is.na(Filter), "No Filter")) %>%
select(-iter, -`Learner ID`, -rsq) %>%
relocate(RMSE, .after = Filter) %>%
relocate(`SE`, .after = RMSE) %>%
mutate(RMSE = round(RMSE, 3)) %>%
arrange(RMSE)
# save as latex table
table3 %>%
ungroup() %>%
arrange(desc(RMSE)) %>%
slice(1:10) %>%
xtable(
type = "latex",
caption = "Worst ten results among all learner-task-filter combinations, sorted in decreasing order of RMSE (p.p.) and their respective standard error (SE).",
label = "tab:perf-worst-10"
) %>%
print(
file = here("docs/00-manuscripts/mdpi/performance-worst-10.tex"),
include.rownames = TRUE,
latex.environments = c("center"),
table.placement = "ht!",
caption.placement = "top",
timestamp = NULL,
sanitize.text.function = function(x) {
x
}
)
saveRDS(table3, here("docs/00-manuscripts/presentation/table-perf-worst-10.rda"))
DT::datatable(table3)
```
## (Plot) P1 Best learner/filter combs for all tasks
```{r performance-results, warning=FALSE, dev = c("png", "pdf"), out.width = "70%"}
results_aggr <- df_perf %>%
filter(learner.id != "regr.featureless") %>%
mutate(filter = replace(filter, is.na(filter), "NF")) %>%
mutate(learner_group = recode_factor(learner_group, `XG` = "XGBoost")) %>%
mutate(learner_group = recode_factor(learner_group, `Ridge-MBO` = "Ridge")) %>%
mutate(learner_group = recode_factor(learner_group, `Lasso-MBO` = "Lasso")) %>%
# reorder by min RMSE (reviewer request)
group_by(learner_group, filter, task.id) %>%
### get the best performance per learner and task
# this group_by() & slice() approach is better than summarise() because we can
# keep additional columns
# in constrast to summarise which only keeps the grouping columns and the
# summarised one
slice(which.min(rmse_aggr)) %>% # this groups the CV iters
ungroup() %>%
group_by(task.id, learner_group) %>%
slice(which.min(rmse_aggr)) %>%
ungroup() %>%
mutate(learner_group = fct_reorder(learner_group, .$rmse_aggr, min))
results_aggr %>%
ggplot(aes(x = rmse_aggr, y = task.id)) +
# geom_jitter(aes(color = learner_group), size = 2, width = 0, height = 0.3) +
# geom_dotplot(aes(fill = learner_group), binaxis="y",
# stackdir="up") +
ggbeeswarm2::geom_beeswarm(groupOnX = FALSE, aes(color = learner_group), size = 2.5) +
# scale_color_nejm(breaks = sort(levels(results_aggr$learner_group))) +
# scale_color_viridis_d(breaks = sort(levels(results_aggr$learner_group))) +
scale_color_viridis_d(breaks = results_aggr$learner_group) +
labs(x = "RMSE", y = "Task", color = "Learner") +
guides(size = FALSE) +
scale_x_continuous(limits = c(27, 40), breaks = seq(28, 40, 2)) +
geom_label_repel(
# subset data to remove out of bounds values
data = results_aggr[results_aggr$rmse_aggr < 100, ],
# from ggbeeswarm, avoid overlapping of points by labels
position = position_quasirandom(),
aes(label = paste0(filter, ",", round(rmse_aggr, 2))),
size = 4,
min.segment.length = 0.1,
seed = 123,
point.padding = 0.5
) +
theme_pubr(base_size = 14) +
theme(
panel.grid.major.y = element_line(size = 0.1, linetype = "dashed"),
axis.title.y = element_blank(),
legend.text = element_text(size = 13),
legend.title = element_text(size = 13),
axis.text.y = element_text(angle = 45),
plot.margin = unit(c(6, 6, 6, 0), "pt")
)
```
## (Plot) P2 Scatterplots of filter methods vs. no filter for each learner and task
Showing the final effect of applying feature selection to a learner for each task.
All filters are colored in the same way whereas using "no filter" appears in a different color.
```{r filter-effect-all-vs-no-filter, warning=FALSE, dev = c("png", "pdf"), out.width = "70%", message=FALSE}
results_aggr1 <- df_perf %>%
filter(learner_group != "Ridge-MBO") %>%
filter(learner_group != "Lasso-MBO") %>%
filter(learner.id != "regr.featureless") %>%
# mutate(filter = replace(filter, "No Filter", "NF")) %>%
mutate(learner_group = as.factor(learner_group)) %>%
mutate(learner_group = recode(learner_group, `XGBoost` = "XG")) %>%
mutate(filter = recode(filter, `No Filter` = "NF")) %>%
mutate(learner_group = fct_rev(learner_group)) %>%
group_by(learner_group, task.id, filter) %>%
# we actually took the mean already in chunk 'aggr-perf'. This is only to get
# summarise() working
summarise(perf = mean(rmse_aggr)) %>%
ungroup() %>%
# we need to reverse the order on purpose here so that ggplot reverses it
# again later
mutate(learner_group = fct_relevel(learner_group, "XG", "SVM", "RF"))
results_aggr1 %>%
ggplot(aes(x = perf, y = learner_group)) +
ggbeeswarm2::geom_beeswarm(
data = results_aggr1[results_aggr1$filter != "NF", ], size = 2.2, shape = 3,
groupOnX = FALSE, aes(color = "Filter")
) +
geom_point(
data = results_aggr1[results_aggr1$filter == "NF", ],
size = 2.2, shape = 19, aes(color = "No Filter")
) +
facet_wrap(~task.id) +
scale_color_nejm(guide = guide_legend(override.aes = list(shape = c(3, 19)))) +
labs(x = "RMSE", y = "Task", colour = NULL) +
guides(size = FALSE) +
scale_x_continuous(limits = c(27, 51)) +
theme_pubr(base_size = 14) +
theme(
panel.grid.major.y = element_line(size = 0.1, linetype = "dashed"),
axis.title.y = element_blank(),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
)
```
## (Plot) P3 Scatterplots of filter methods vs. Borda for each learner and task
Showing the final effect of applying feature selection to a learner for each task.
All filters are summarized into a a single color whereas the "Borda" filter appears in its own color.
```{r filter-effect-all-vs-borda-filter, warning=FALSE, dev = c("png", "pdf"), out.width = "70%", message=FALSE}
results_aggr2 <- df_perf %>%
na.omit() %>%
filter(learner_group != "Ridge-MBO") %>%
filter(learner_group != "Lasso-MBO") %>%
filter(learner.id != "regr.featureless") %>%
mutate(learner_group = recode_factor(learner_group, `XGBoost` = "XG")) %>%
group_by(learner_group, task.id, filter) %>%
# we actually took the mean already in chunk 'aggr-perf'. This is only to get
# summarise() working
summarise(perf = mean(rmse_aggr)) %>%
ungroup() %>%
# we need to reverse the order on porpuse here so that ggplot reverses it
# again later
mutate(learner_group = fct_relevel(learner_group, "XG", "SVM", "RF"))
results_aggr2 %>%
ggplot(aes(x = perf, y = learner_group)) +
ggbeeswarm2::geom_beeswarm(
data = results_aggr2[results_aggr2$filter != "Borda", ],
shape = 3, size = 2.2, aes(color = "Filter"),
groupOnX = FALSE
) +
geom_point(
data = results_aggr2[results_aggr2$filter == "Borda", ],
shape = 19, size = 2.2, aes(color = "Borda Filter")
) +
facet_wrap(~task.id) +
scale_color_manual(
guide = guide_legend(override.aes = list(shape = c(19, 3))),
values = c(
"Filter" = "#BC3C29FF",
"Borda Filter" = "#0072B5FF"
)
) +
labs(x = "RMSE", y = "Task", colour = NULL) +
guides(size = FALSE) +
scale_x_continuous(limits = c(27, 51)) +
theme_pubr(base_size = 14) +
theme(
panel.grid.major.y = element_line(size = 0.1, linetype = "dashed"),
axis.title.y = element_blank(),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
)
```
## (Table) T4 Number of features selected during tuning
The model/task combinations which were selected relate to the best performance of the respective algorithm on the HR-NRI-VI task in the overall benchmark.
Fold IDs are different for each learner, i.e. a specific plot does not always resolve to "fold 1" for each learner.
See `bmr_inspect_tune[["results"]][["hr_nri_vi"]][["RF MBO Relief"]][["pred"]][["instance"]][["test.inds"]]`.
Thus, we need to manually label the fold IDs to plot names for each learner.
Example for RF on fold 1 (Luiando):
- Percentage of selected features during tuning on all plots but Luiando: `bmr_inspect_tune[["results"]][["hr_nri_vi"]][["RF MBO Relief"]][["extract"]][[1]][["mbo.result"]][["x"]][["fw.perc"]]`: 99.972
- Overall features available in training set: sum(Laukiz1 + Laukiz2 + Oiartzun) = 1507
- Absolute number of selected features: 1507 * 0.99972 = 1507
**RF**
- Plot 1: Luiando
- Plot 2: Laukiz2
- Plot 3: Laukiz 1
- Plot 4: Oiartzun
**SVM**
- Plot 1: Laukiz1
- Plot 2: Oiartzun
- Plot 3: Luiando
- Plot 4: Laukiz2
**XGBoost**
- Plot 1: Luiando
- Plot 2: Laukiz1
- Plot 3: Oiartzun
- Plot 4: Laukiz2
```{r eval-performance-5}
bmr_inspect_tune <- mergeBenchmarkResults(benchmark_tune_results_hr_nri_vi)
df_tbl <- getBMRTuneResults(bmr_inspect_tune, as.df = TRUE) %>%
# replace iter IDs by individual fold names
dplyr::mutate(iter = as.character(iter)) %>%
dplyr::mutate(iter = case_when(
learner.id == "XGBOOST MBO CMIM" & iter == 1 ~ "Luiando",
learner.id == "XGBOOST MBO CMIM" & iter == 2 ~ "Laukiz1",
learner.id == "XGBOOST MBO CMIM" & iter == 3 ~ "Oiartzun",
learner.id == "XGBOOST MBO CMIM" & iter == 4 ~ "Laukiz2",
learner.id == "SVM MBO Relief" & iter == 1 ~ "Laukiz1",
learner.id == "SVM MBO Relief" & iter == 2 ~ "Oiartzun",
learner.id == "SVM MBO Relief" & iter == 3 ~ "Luiando",
learner.id == "SVM MBO Relief" & iter == 4 ~ "Laukiz2",
learner.id == "RF MBO Relief" & iter == 1 ~ "Luiando",
learner.id == "RF MBO Relief" & iter == 2 ~ "Laukiz2",
learner.id == "RF MBO Relief" & iter == 3 ~ "Laukiz1",
learner.id == "RF MBO Relief" & iter == 4 ~ "Oiartzun",
TRUE ~ as.character(iter)
)) %>%
dplyr::rename(Learner = learner.id) %>%
dplyr::rename(Plot = iter) %>%
dplyr::rename(RMSE = rmse.test.mean) %>%
dplyr::mutate(fw.perc = fw.perc * 100) %>%
dplyr::rename("Features (\\%)" = fw.perc) %>%
dplyr::mutate(`Test Plot` = as.character(Plot)) %>%
dplyr::mutate(Learner = forcats::fct_recode(Learner,
"RF \\\\ Relief" = "RF MBO Relief", "XGB \\\\ CMIM" = "XGBOOST MBO CMIM",
"SVM \\\\ Relief" = "SVM MBO Relief"
)) %>%
dplyr::mutate(Learner = as.character(Learner)) %>%
dplyr::mutate(`Test Plot` = as.character(`Test Plot`)) %>%
dplyr::mutate("Features (\\#)" = case_when(
# numbers here are the respective sums of the training sets (without the mentioned plot acting as the test set)
`Test Plot` == "Laukiz1" ~ paste0(as.character(ceiling((`Features (\\%)` * 1249) / 100)), "/1249"),
`Test Plot` == "Laukiz2" ~ paste0(as.character(ceiling((`Features (\\%)` * 1357) / 100)), "/1357"),
`Test Plot` == "Luiando" ~ paste0(as.character(ceiling((`Features (\\%)` * 1507) / 100)), "/1507"),
`Test Plot` == "Oiartzun" ~ paste0(as.character(ceiling((`Features (\\%)` * 1311) / 100)), "/1311"),
)) %>%
dplyr::group_by(Learner) %>%
dplyr::arrange(Learner, `Test Plot`) %>%
dplyr::select(Learner, `Test Plot`, "Features (\\%)", "Features (\\#)")
rle.lengths <- rle(df_tbl[[1]])$lengths
first <- !duplicated(df_tbl[[1]])
df_tbl[[1]][!first] <- ""
# define appearance of \multirow
df_tbl[[1]][first] <-
paste0("\\midrule\\multirow{", rle.lengths, "}{*}{\\specialcell{", df_tbl[[1]][first], "}}")
# remove redundant midrule from first entry
df_tbl[[1]][first][1] <- gsub("\\\\midrule", "", df_tbl[[1]][first][1])
table4 <- df_tbl %>%
xtable::xtable(
type = "latex",
caption = "Selected feature portions during tuning for the best performing learner-filter settings (SVM Relief, RF Relief, XGBoost CMIM) across folds for task HR-NRI-VI, sorted by plot name. 'Features (\\texttt{\\#})' denotes the absolute number of features selected and 'Features (\\texttt{\\%})' refers to the percentage relative to the overall features available in the training sets for each plot (Laukiz1 = 1249, Laukiz2 = 1357, Luiando = 1507, Oiartzun = 1311). Results were estimated in a separate model tuning step, not within the main cross-validation comparison.",
label = "tab:tune-perc-sel-features",
digits = c(0, 0, 0, 5, 0)
)
# save to file
table4 %>%
print(
file = here("docs/00-manuscripts/mdpi/tune-perc-sel-features.tex"),
include.rownames = FALSE,
latex.environments = c("center"),
table.placement = "ht!",
caption.placement = "top",
timestamp = NULL,
booktabs = TRUE,
# important to treat content of multirow as latex content
sanitize.text.function = force
)
saveRDS(table4, here("docs/00-manuscripts/presentation/tune-perc-sel-feature.rda"))
DT::datatable(table4)
```
Aggregated mean and standard deviation:
```{r}
getBMRTuneResults(bmr_inspect_tune, as.df = TRUE) %>%
dplyr::rename(Learner = learner.id) %>%
dplyr::rename(Plot = iter) %>%
dplyr::rename(RMSE = rmse.test.mean) %>%
dplyr::rename("Features (%)" = fw.perc) %>%
dplyr::mutate(Plot = as.character(Plot)) %>%
dplyr::mutate(Plot = forcats::fct_recode(Plot,
Oiartzun = "4", Luiando = "3",
Laukiz1 = "1", Laukiz2 = "2"
)) %>%
dplyr::mutate(Learner = forcats::fct_recode(Learner,
"RF \\\\ Relief" = "RF MBO Relief", "XGB \\\\ CMIM" = "XGBOOST MBO CMIM",
"SVM \\\\ Relief" = "SVM MBO Relief"
)) %>%
dplyr::mutate(Learner = as.character(Learner)) %>%
dplyr::mutate(Plot = as.character(Plot)) %>%
dplyr::group_by(Learner) %>%
dplyr::summarise(
"Mean (Features (%))" = mean(`Features (%)`),
"SD (Features (%))" = sd(`Features (%)`)
) %>%
DT::datatable()
```