Example Analysis of Ames Housing Data
Details
These objects are the results of an analysis of the Ames
housing data. A K-nearest neighbors model was used with a small
predictor set that included natural spline transformations of
the Longitude and Latitude predictors. The code used to
generate these examples was:
library(tidymodels)
library(tune)
library(AmesHousing)
# ------------------------------------------------------------------------------
ames <- make_ames()
set.seed(4595)
data_split <- initial_split(ames, strata = "Sale_Price")
ames_train <- training(data_split)
set.seed(2453)
rs_splits <- vfold_cv(ames_train, strata = "Sale_Price")
# ------------------------------------------------------------------------------
ames_rec <-
recipe(Sale_Price ~ ., data = ames_train) %>%
step_log(Sale_Price, base = 10) %>%
step_YeoJohnson(Lot_Area, Gr_Liv_Area) %>%
step_other(Neighborhood, threshold = .1) %>%
step_dummy(all_nominal()) %>%
step_zv(all_predictors()) %>%
step_spline_natural(Longitude, deg_free = tune("lon")) %>%
step_spline_natural(Latitude, deg_free = tune("lat"))
knn_model <-
nearest_neighbor(
mode = "regression",
neighbors = tune("K"),
weight_func = tune(),
dist_power = tune()
) %>%
set_engine("kknn")
ames_wflow <-
workflow() %>%
add_recipe(ames_rec) %>%
add_model(knn_model)
ames_set <-
extract_parameter_set_dials(ames_wflow) %>%
update(K = neighbors(c(1, 50)))
set.seed(7014)
ames_grid <-
ames_set %>%
grid_max_entropy(size = 10)
ames_grid_search <-
tune_grid(
ames_wflow,
resamples = rs_splits,
grid = ames_grid
)
set.seed(2082)
ames_iter_search <-
tune_bayes(
ames_wflow,
resamples = rs_splits,
param_info = ames_set,
initial = ames_grid_search,
iter = 15
)important note: Since the rsample split columns contain a reference
to the same data, saving them to disk can results in large object sizes when
the object is later used. In essence, R replaces all of those references with
the actual data. For this reason, we saved zero-row tibbles in their place.
This doesn't affect how we use these objects in examples but be advised that
using some rsample functions on them will cause issues.
Examples
library(tune)
ames_grid_search
#> # Tuning results
#> # 10-fold cross-validation using stratification
#> # A tibble: 10 × 4
#> splits id .metrics .notes
#> <list> <chr> <list> <list>
#> 1 <split [1978/0]> Fold01 <tibble [20 × 9]> <tibble [0 × 1]>
#> 2 <split [1979/0]> Fold02 <tibble [20 × 9]> <tibble [0 × 1]>
#> 3 <split [1979/0]> Fold03 <tibble [20 × 9]> <tibble [0 × 1]>
#> 4 <split [1979/0]> Fold04 <tibble [20 × 9]> <tibble [0 × 1]>
#> 5 <split [1979/0]> Fold05 <tibble [20 × 9]> <tibble [0 × 1]>
#> 6 <split [1979/0]> Fold06 <tibble [20 × 9]> <tibble [0 × 1]>
#> 7 <split [1979/0]> Fold07 <tibble [20 × 9]> <tibble [0 × 1]>
#> 8 <split [1979/0]> Fold08 <tibble [20 × 9]> <tibble [0 × 1]>
#> 9 <split [1979/0]> Fold09 <tibble [20 × 9]> <tibble [0 × 1]>
#> 10 <split [1981/0]> Fold10 <tibble [20 × 9]> <tibble [0 × 1]>
ames_iter_search
#> # Tuning results
#> # 10-fold cross-validation using stratification
#> # A tibble: 110 × 5
#> splits id .metrics .notes .iter
#> <list> <chr> <list> <list> <int>
#> 1 <split [1978/0]> Fold01 <tibble [20 × 9]> <tibble [0 × 1]> 0
#> 2 <split [1979/0]> Fold02 <tibble [20 × 9]> <tibble [0 × 1]> 0
#> 3 <split [1979/0]> Fold03 <tibble [20 × 9]> <tibble [0 × 1]> 0
#> 4 <split [1979/0]> Fold04 <tibble [20 × 9]> <tibble [0 × 1]> 0
#> 5 <split [1979/0]> Fold05 <tibble [20 × 9]> <tibble [0 × 1]> 0
#> 6 <split [1979/0]> Fold06 <tibble [20 × 9]> <tibble [0 × 1]> 0
#> 7 <split [1979/0]> Fold07 <tibble [20 × 9]> <tibble [0 × 1]> 0
#> 8 <split [1979/0]> Fold08 <tibble [20 × 9]> <tibble [0 × 1]> 0
#> 9 <split [1979/0]> Fold09 <tibble [20 × 9]> <tibble [0 × 1]> 0
#> 10 <split [1981/0]> Fold10 <tibble [20 × 9]> <tibble [0 × 1]> 0
#> # ℹ 100 more rows