tune (version 0.1.2)

example_ames_knn: Example Analysis of Ames Housing Data

Description

Example Analysis of Ames Housing Data

Value

ames_wflow

A workflow object

ames_grid_search,ames_iter_search

Results of model tuning.

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_ns(Longitude, deg_free = tune("lon")) %>%
  step_ns(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 <-
  parameters(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

# NOT RUN {
library(tune)

ames_grid_search
ames_iter_search
# }