我不确定我哪里出错了。当我运行以下调整网格内的模型失败。我收到此警告消息：“所有模型在 tune_grid() 中均失败”。任何帮助将不胜感激。
# PREPROCESSING -- RECIPE ---------------------------------------------------------------------
library(recipes)
xgb_recipe <- recipe(EVENT ~ ., data = train_data) %>% # define target & data
#step_string2factor(all_nominal()) %>%
#step_dummy(all_predictors()) %>%
recipes::step_other(all_nominal(), threshold = 0.01) %>%
recipes::step_nzv(all_nominal()) %>%
#step_downsample(EVENT) %>%
prep()
> xgb_recipe
数据配方
输入：
角色 #variables 结果 1 预测器 272
训练数据包含 2427 个数据点，没有缺失数据。
操作：
PROGRAM_TYPE_CODE、PREFERENCE_NUMBER、...的折叠因子级别... [已训练] 已删除稀疏、不平衡变量过滤器 PRIOR_PGRD_PRG_YR、PRIOR_TF_SC_PRG_YR、ETHNIC_GROUP_DESC、HASEMAIL、... [已训练]
# XGB SPEC ------------------------------------------------------------------------------------
xgb_spec <- boost_tree(
trees = 600, ## nround=6000
tree_depth = tune(), min_n = tune(), ## max_depth = 6
loss_reduction = tune(), ## first three: model complexity
sample_size = tune(), mtry = tune(), ## randomness
learn_rate = tune(), ## step size,
#num_class=4,
#objective = 'multi:softprob' #%>%
#nthreads=20 %>%
#print_every_n = 300
) %>%
set_engine("xgboost") %>%
set_mode("classification")
xgb_spec
提升树模型规范（分类）
主要参数： mtry = tune() trees = 600 min_n = tune()
tree_depth = tune() learn_rate = tune() loss_reduction = tune()
sample_size = tune()计算引擎：xgboost
# GRID ----------------------------------------------------------------------------------------
xgb_grid <- grid_latin_hypercube(
tree_depth(),
min_n(),
loss_reduction(),
sample_size = sample_prop(),
finalize(mtry(), train_data),
learn_rate(),
size = 20
)
xgb_grid
一个小标题： 20 x 6 tree_depth min_n loss_reduction sample_size mtry learn_rate 1 4 15 1.71e- 6 0.256 110 2.14e- 9 2 7
29 4.08e- 8 0.836 97 2.07e-10 3 10 26
6.44e- 7 59 0.883 66e 7. 8 4 8 28 9.77e- 1 0.964 270 3.90e- 8 5 1 19 4.27e- 4 0.733
208 8.00e- 4 6 3 5 1.61e+ 1 0.392 220 4.04e-10 7 5 9 1.48e- 9 3e-
3 - 7 8 11 34 4.20e- 5 0.569 178 1.69e- 8 9 12 38 7.80e+ 0 0.143 79 8.67e- 7 10
4 12 5.58e- 9 0.946 173 1.17e- 2 11 14
2 1.30e- 4 0.805 202 1.10e- 4 12 15 21
9.15e- 3 0.454 134 3.82e- 3 13 9 21 4.99e- 6 0.500 10 2.91e- 9 14 7 17 7.60e-10
0.232 6.25 15 12 11 4.85e- 1 0.297 21 1.23e- 5 16 7 35 7.63e- 8 0.516 95
9.60e- 2 17 2 6 1.01e- 1 0.353 48 3.57e- 6 18 10 23 2.57e-10.4616 33e-10 0. - 2 19
13 40 2.00e- 3 0.715 150 3.44e- 5 20 5
32 1.25e- 2 0.610 234 4.95e- 4
# WORKFLOW ------------------------------------------------------------------------------------
xgb_wf <- workflow() %>%
add_recipe(xgb_recipe) %>%
add_model(xgb_spec)
xgb_wf
══工作流═══════════════════════════════════════════════ ══════════════════════════════════════════════════ ══════════════ 预处理器：配方模型：boost_tree()
── 预处理器 ──────────────────────────────────────────────── ────────────────────────────────────────────────── ────────── 2个配方步骤
● step_other() ● step_nzv()
── 型号────────────────────────────────────────────── ────────────────────────────────────────────────── ──────────────── 提升树模型规范（分类）
主要参数： mtry = tune() trees = 600 min_n = tune()
tree_depth = tune() learn_rate = tune() loss_reduction = tune()
sample_size = tune()计算引擎：xgboost
# CROSS-VALIDATION Resamples -----------------------------------------------------------------
# 10 fold CV
set.seed(123)
cv_folds <- vfold_cv(train_data)
cv_folds10折交叉验证
小标题：10 x 2 拆分 id 1 Fold01 2 Fold02 3
折叠 03 4 折叠 04 5 折叠 05 6 折叠 06 7 折叠 07 8 折叠 08 9 折叠 09 10 折叠 10
# TUNING --------------------------------------------------------------------------------------
all_cores <- parallel::detectCores(logical = FALSE)
library(doParallel)
cl <- makePSOCKcluster(all_cores)
registerDoParallel(cl)
s <- Sys.time()
set.seed(2020)
xgb_res <- tune_grid(
xgb_wf,
resamples = cv_folds,
grid = xgb_grid,
control = control_grid(save_pred = TRUE,
verbose = TRUE)
)
Sys.time() - s
xgb_res
xgb_res # 10-fold cross-validation # A tibble: 10 x 5 splits id .metrics .notes .predictions
1 Fold01
2 Fold02
3 Fold03
4 Fold04
5 Fold05
6 Fold06
7 Fold07
8 Fold08
9 Fold09
10 Fold10
警告消息：所有模型在 tune_grid() 中均失败。见.notes专栏。