Apply 4
Bella Kalinyak
2024-10-03
Import Data
ikea <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-03/ikea.csv')## New names:
## Rows: 3694 Columns: 14
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (7): name, category, old_price, link, other_colors, short_description, d... dbl
## (6): ...1, item_id, price, depth, height, width lgl (1): sellable_online
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`skimr::skim(ikea)| Name | ikea |
| Number of rows | 3694 |
| Number of columns | 14 |
| _______________________ | |
| Column type frequency: | |
| character | 7 |
| logical | 1 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| name | 0 | 1 | 3 | 27 | 0 | 607 | 0 |
| category | 0 | 1 | 4 | 36 | 0 | 17 | 0 |
| old_price | 0 | 1 | 4 | 13 | 0 | 365 | 0 |
| link | 0 | 1 | 52 | 163 | 0 | 2962 | 0 |
| other_colors | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| short_description | 0 | 1 | 3 | 63 | 0 | 1706 | 0 |
| designer | 0 | 1 | 3 | 1261 | 0 | 381 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| sellable_online | 0 | 1 | 0.99 | TRU: 3666, FAL: 28 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| …1 | 0 | 1.00 | 1846.50 | 1066.51 | 0 | 923.25 | 1846.5 | 2769.75 | 3693 | ▇▇▇▇▇ |
| item_id | 0 | 1.00 | 48632396.79 | 28887094.10 | 58487 | 20390574.00 | 49288078.0 | 70403572.75 | 99932615 | ▇▇▇▇▇ |
| price | 0 | 1.00 | 1078.21 | 1374.65 | 3 | 180.90 | 544.7 | 1429.50 | 9585 | ▇▁▁▁▁ |
| depth | 1463 | 0.60 | 54.38 | 29.96 | 1 | 38.00 | 47.0 | 60.00 | 257 | ▇▃▁▁▁ |
| height | 988 | 0.73 | 101.68 | 61.10 | 1 | 67.00 | 83.0 | 124.00 | 700 | ▇▂▁▁▁ |
| width | 589 | 0.84 | 104.47 | 71.13 | 1 | 60.00 | 80.0 | 140.00 | 420 | ▇▅▂▁▁ |
data <- ikea %>%
mutate(across(is.logical, as.factor)) %>%
select(-old_price, -link, -1) %>%
na.omit() %>%
mutate(price = log(price))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(is.logical, as.factor)`.
## Caused by warning:
## ! Use of bare predicate functions was deprecated in tidyselect 1.1.0.
## ℹ Please use wrap predicates in `where()` instead.
## # Was:
## data %>% select(is.logical)
##
## # Now:
## data %>% select(where(is.logical))Explore Data
category
data %>%
ggplot(aes(category, price)) +
geom_point()
other colors
data %>%
ggplot(aes(y = price, x = other_colors)) +
geom_point()
product height
data %>%
ggplot(aes(price, height)) +
geom_point()
product depth
data %>%
ggplot(aes(price, depth)) +
geom_point()
product width
data %>%
ggplot(aes(price, width)) +
geom_point()
title
# Step 1: Prepare data
data_binarized_tbl <- data %>%
select(-item_id, -short_description,) %>%
binarize
data_binarized_tbl %>% glimpse()## Rows: 1,899
## Columns: 82
## $ name__ALGOT <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__BEKANT <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__BESTÅ <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `name__BILLY_/_OXBERG` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__BRIMNES <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__BROR <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__EKET <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__GRÖNLID <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__HAVSTA <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__HAVSTEN <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__HEMNES <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__IVAR <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__JONAXEL <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__KALLAX <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__LIDHULT <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__LIXHULT <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__NORDLI <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__PAX <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__PLATSA <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `name__STUVA_/_FRITIDS` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__TROFAST <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__VALLENTUNA <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ name__VIMLE <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `name__-OTHER` <dbl> 1, 1, 1, 1, 1, 1, 1, …
## $ category__Bar_furniture <dbl> 1, 1, 1, 1, 1, 1, 1, …
## $ category__Beds <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Bookcases_&_shelving_units` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Cabinets_&_cupboards` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ category__Chairs <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Chests_of_drawers_&_drawer_units` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Children's_furniture` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ category__Nursery_furniture <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ category__Outdoor_furniture <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Sideboards,_buffets_&_console_tables` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Sofas_&_armchairs` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__Tables_&_desks` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__TV_&_media_furniture` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ category__Wardrobes <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `category__-OTHER` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `price__-Inf_5.68697535633982` <dbl> 1, 1, 0, 1, 1, 1, 0, …
## $ price__5.68697535633982_6.52209279817015 <dbl> 0, 0, 1, 0, 0, 0, 1, …
## $ price__6.52209279817015_7.37085996851068 <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ price__7.37085996851068_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ sellable_online__TRUE <dbl> 1, 1, 1, 1, 1, 1, 1, …
## $ `sellable_online__-OTHER` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ other_colors__No <dbl> 0, 1, 1, 1, 1, 1, 1, …
## $ other_colors__Yes <dbl> 1, 0, 0, 0, 0, 0, 0, …
## $ designer__Andreas_Fredriksson <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Carina_Bengs <dbl> 0, 0, 1, 0, 0, 0, 1, …
## $ designer__Carl_Öjerstam <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Ebba_Strandmark <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Ehlén_Johansson <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `designer__Ehlén_Johansson/IKEA_of_Sweden` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Eva_Lilja_Löwenhielm <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Francis_Cayouette <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Gillis_Lundgren <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Henrik_Preutz <dbl> 1, 0, 0, 0, 0, 0, 0, …
## $ designer__IKEA_of_Sweden <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `designer__IKEA_of_Sweden/Ehlén_Johansson` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `designer__IKEA_of_Sweden/Jon_Karlsson` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Johan_Kroon <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Jon_Karlsson <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `designer__Jon_Karlsson/IKEA_of_Sweden` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `designer__K_Hagberg/M_Hagberg` <dbl> 0, 0, 0, 1, 1, 1, 0, …
## $ designer__Mia_Lagerman <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Nike_Karlsson <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Ola_Wihlborg <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Studio_Copenhagen <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ designer__Tord_Björklund <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `designer__-OTHER` <dbl> 0, 1, 0, 0, 0, 0, 0, …
## $ `depth__-Inf_40` <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ depth__40_47 <dbl> 0, 0, 1, 1, 1, 1, 1, …
## $ depth__47_60 <dbl> 1, 1, 0, 0, 0, 0, 0, …
## $ depth__60_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `height__-Inf_71` <dbl> 0, 1, 0, 0, 0, 0, 0, …
## $ height__71_92 <dbl> 0, 0, 1, 0, 0, 0, 0, …
## $ height__92_171 <dbl> 1, 0, 0, 1, 1, 1, 1, …
## $ height__171_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ `width__-Inf_60` <dbl> 1, 0, 1, 1, 1, 1, 1, …
## $ width__60_93 <dbl> 0, 1, 0, 0, 0, 0, 0, …
## $ width__93_161.5 <dbl> 0, 0, 0, 0, 0, 0, 0, …
## $ width__161.5_Inf <dbl> 0, 0, 0, 0, 0, 0, 0, …# Step 2: Correlate
data_corr_tbl <- data_binarized_tbl %>%
correlate(price__7.37085996851068_Inf)
data_corr_tbl## # A tibble: 82 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 price 7.37085996851068_Inf 1
## 2 width 161.5_Inf 0.579
## 3 depth 60_Inf 0.447
## 4 category Sofas_&_armchairs 0.379
## 5 width -Inf_60 -0.374
## 6 price -Inf_5.68697535633982 -0.336
## 7 price 6.52209279817015_7.37085996851068 -0.333
## 8 price 5.68697535633982_6.52209279817015 -0.331
## 9 name PAX 0.302
## 10 category Wardrobes 0.279
## # ℹ 72 more rows#Step 3: Plot
data_corr_tbl %>%
plot_correlation_funnel()## Warning: ggrepel: 58 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Build models
# data <- sample_n(data, 500)
# Split into train and test dataset
set.seed(1234)
data_split <- rsample::initial_split(data)
data_train <- training(data_split)
data_test <- testing(data_split)
# Further split training dataset for cross-validation
set.seed(2345)
data_cv <- rsample::vfold_cv(data_train)
data_cv## # 10-fold cross-validation
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [1281/143]> Fold01
## 2 <split [1281/143]> Fold02
## 3 <split [1281/143]> Fold03
## 4 <split [1281/143]> Fold04
## 5 <split [1282/142]> Fold05
## 6 <split [1282/142]> Fold06
## 7 <split [1282/142]> Fold07
## 8 <split [1282/142]> Fold08
## 9 <split [1282/142]> Fold09
## 10 <split [1282/142]> Fold10library(usemodels)
usemodels::use_ranger(price ~ ., data = data_train)## ranger_recipe <-
## recipe(formula = price ~ ., data = data_train)
##
## ranger_spec <-
## rand_forest(mtry = tune(), min_n = tune(), trees = 1000) %>%
## set_mode("classification") %>%
## set_engine("ranger")
##
## ranger_workflow <-
## workflow() %>%
## add_recipe(ranger_recipe) %>%
## add_model(ranger_spec)
##
## set.seed(41506)
## ranger_tune <-
## tune_grid(ranger_workflow, resamples = stop("add your rsample object"), grid = stop("add number of candidate points"))# Specify recipe
ranger_recipe <-
recipe(formula = price ~ ., data = data_train) %>%
recipes::update_role(item_id, new_role = "id variable") %>%
step_tokenize(short_description) %>%
step_tokenfilter(short_description, max_tokens = 100) %>%
step_tfidf(short_description) %>%
step_other(designer, name, category) %>%
step_dummy(designer, name, category, other_colors, sellable_online, one_hot = TRUE) %>%
step_YeoJohnson(height, width, depth)
ranger_recipe %>%
prep() %>% juice() %>% glimpse()## Rows: 1,424
## Columns: 124
## $ item_id <dbl> 19282962, 29320911, 49276549…
## $ depth <dbl> 6.921938, 5.331305, 4.942159…
## $ height <dbl> 12.95187, 21.33670, 14.11837…
## $ width <dbl> 9.034855, 7.442056, 5.426853…
## $ price <dbl> 7.595890, 7.833996, 6.429719…
## $ tfidf_short_description_1 <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_120x40x64 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_140x200 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_147x147 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_150x44x236 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_150x60x236 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_150x66x236 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_2 <dbl> 0.8840660, 0.0000000, 0.5304…
## $ tfidf_short_description_200x66x236 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_25x51x70 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_3 <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_4 <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_41x101 <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_41x61 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_5 <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_50x51x70 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_60x50x128 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_61x101 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_74 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_75 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_80x30x202 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_99x44x56 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_and <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_armchair <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_armrest <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_armrests <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_backrest <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_bar <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_baskets <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_bed <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_bench <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_bookcase <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_box <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_cabinet <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_cabinets <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_castors <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_chair <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_chaise <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_changing <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_chest <dbl> 0.0000000, 0.0000000, 0.5867…
## $ `tfidf_short_description_children's` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_clothes <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_cm <dbl> 0.0000000, 0.2905183, 0.1743…
## $ tfidf_short_description_combination <dbl> 0.0000000, 0.6909039, 0.0000…
## $ tfidf_short_description_corner <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_cover <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_desk <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_door <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_doors <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_drawer <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_drawers <dbl> 0.0000000, 0.0000000, 0.5004…
## $ tfidf_short_description_feet <dbl> 0.00000, 0.00000, 0.00000, 0…
## $ tfidf_short_description_foldable <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_for <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_frame <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_glass <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_highchair <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_in <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_inserts <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_junior <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_leg <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_legs <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_lock <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_longue <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_mesh <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_modular <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_module <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_mounted <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_of <dbl> 0.0000000, 0.0000000, 0.5753…
## $ tfidf_short_description_on <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_outdoor <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_panel <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_plinth <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_rail <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_seat <dbl> 0.7549716, 0.0000000, 0.0000…
## $ tfidf_short_description_section <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_sections <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_shelf <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_shelves <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_shelving <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_sliding <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_smart <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_sofa <dbl> 0.7660576, 0.0000000, 0.0000…
## $ tfidf_short_description_step <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_stool <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_storage <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_table <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_top <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_tv <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_underframe <dbl> 0.000000, 0.000000, 0.000000…
## $ tfidf_short_description_unit <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_upright <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_w <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_wall <dbl> 0.0000000, 0.0000000, 0.0000…
## $ tfidf_short_description_wardrobe <dbl> 0.0000000, 0.7815769, 0.0000…
## $ tfidf_short_description_wire <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ tfidf_short_description_with <dbl> 0.0000000, 0.0000000, 0.0000…
## $ designer_IKEA.of.Sweden <dbl> 0, 0, 0, 0, 0, 1, 1, 0, 0, 0…
## $ designer_Ola.Wihlborg <dbl> 1, 0, 1, 0, 1, 0, 0, 0, 0, 0…
## $ designer_other <dbl> 0, 1, 0, 1, 0, 0, 0, 1, 1, 1…
## $ name_BESTÅ <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ name_PAX <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ name_other <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ category_Bookcases...shelving.units <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ category_Cabinets...cupboards <dbl> 0, 0, 0, 0, 0, 1, 0, 1, 0, 0…
## $ category_Chairs <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ category_Chests.of.drawers...drawer.units <dbl> 0, 0, 1, 0, 1, 0, 1, 0, 1, 0…
## $ category_Sofas...armchairs <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ category_Tables...desks <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ category_TV...media.furniture <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ category_Wardrobes <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0…
## $ category_other <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…
## $ other_colors_No <dbl> 1, 1, 0, 1, 1, 0, 0, 0, 1, 0…
## $ other_colors_Yes <dbl> 0, 0, 1, 0, 0, 1, 1, 1, 0, 1…
## $ sellable_online_FALSE. <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ sellable_online_TRUE. <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…#Specify model
ranger_spec <-
rand_forest(trees = tune()) %>%
set_mode("regression") %>%
set_engine("ranger")
# Combine recipe and model using workflow
ranger_workflow <-
workflow() %>%
add_recipe(ranger_recipe) %>%
add_model(ranger_spec)
# Tune hyperparameters
set.seed(91707)
ranger_tune <-
tune_grid(ranger_workflow, resamples = data_cv, grid = 5)Evaluate Models
tune::show_best(ranger_tune, metric = "rmse")## # A tibble: 5 × 7
## trees .metric .estimator mean n std_err .config
## <int> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 1346 rmse standard 0.443 10 0.0101 Preprocessor1_Model2
## 2 1836 rmse standard 0.444 10 0.0101 Preprocessor1_Model3
## 3 1191 rmse standard 0.444 10 0.00977 Preprocessor1_Model5
## 4 558 rmse standard 0.444 10 0.0104 Preprocessor1_Model1
## 5 200 rmse standard 0.447 10 0.00992 Preprocessor1_Model4# Update the model by selecting the best hyperparameter
ranger_fw <- tune::finalize_workflow(ranger_workflow,
tune::select_best(ranger_tune, metric = "rmse"))
# Fit the model of the entire training data and test it on the test data.
data_fit <- tune::last_fit(ranger_fw, data_split)tune::collect_metrics(data_fit)## # A tibble: 2 × 4
## .metric .estimator .estimate .config
## <chr> <chr> <dbl> <chr>
## 1 rmse standard 0.416 Preprocessor1_Model1
## 2 rsq standard 0.898 Preprocessor1_Model1tune::collect_metrics(data_fit)## # A tibble: 2 × 4
## .metric .estimator .estimate .config
## <chr> <chr> <dbl> <chr>
## 1 rmse standard 0.416 Preprocessor1_Model1
## 2 rsq standard 0.898 Preprocessor1_Model1tune::collect_predictions(data_fit) %>%
ggplot(aes(price, .pred)) +
geom_point(alpha = 0.3, fill = "midnightblue") +
geom_abline(lty = 2, color = "gray50") +
coord_fixed()
Make Predictions
The dots are mostly centered around the prediction line. This model would make good predictions.
Changes
I added one_hot = TRUE to the step_dummy function and used rand_forest instead of xgboost. And added “category” to step_other function