【发布时间】：2021-03-01 15:51:54
【问题描述】：
问题
我正在尝试使用 R 中的 Tidymodels 包进行分析。我正在关注以下关于 R 中决策树学习的教程：-
教程
https://bcullen.rbind.io/post/2020-06-02-tidymodels-decision-tree-learning-in-r/
我有一个名为 FID 的数据框（见下文），其中的因变量是频率（数字），而预测变量分别是：- 年（数字）、月（因子）、季风（因子）和天（数字）。
我相信我通过构建袋装树、随机森林和提升树模型成功地遵循了名为“Tidymodels：R 中的决策树学习”的教程。
为了这个分析，我还想构建一个一般线性模型（glm），以便在所有模型（即随机森林、袋装树、提升树和一般模型）之间进行模型比较线性模型）来建立最佳模型拟合。所有模型都经过10倍交叉验证以减少过拟合的偏差。
问题
随后，我尝试调整教程中的代码（请参见下文）以适应 glm 模型，但我很困惑是否已适当调整模型。当我在模型全部拟合后尝试生成 rmse 值时，我不确定这个 glm R 代码元素是否会生成以下错误消息：-
错误信息
Error: Problem with `mutate()` input `model`.
x Input `model` can't be recycled to size 4.
ℹ Input `model` is `c("bag", "rf", "boost")`.
ℹ Input `model` must be size 4 or 1, not 3.
此外，我不确定这些模型在 recipe() 函数中表达的 R 代码是否足够或正确，这在处理步骤中非常重要在拟合每个模型之前。从我的角度来看，我想知道模型的配方是否可以改进。
如果可能的话，我想知道是否有人可以帮助我解决在拟合 glm 模型时出现的错误消息，同时更正配方（如果有必要）。
我不是一个高级的 R 编码器，我已经做了彻底的尝试，通过研究其他 Tidymodel 教程来尝试找到解决方案；但是，我不明白此错误消息的含义。因此，如果有人能提供帮助，我谨表示最深切的谢意。
非常感谢。
R 代码
##Open the tidymodels package
library(tidymodels)
library(glmnet)
library(parsnip)
library(rpart.plot)
library(rpart)
library(tidyverse) # manipulating data
library(skimr) # data visualization
library(baguette) # bagged trees
library(future) # parallel processing & decrease computation time
library(xgboost) # boosted trees
library(ranger)
###########################################################
# Put 3/4 of the data into the training set
#split this single dataset into two: a training set and a testing set
data_split <- initial_split(Tidy_df, prop = 3/4)
# Create data frames for the two sets:
train_data <- training(data_split)
test_data <- testing(data_split)
# resample the data with 10-fold cross-validation (10-fold by default)
cv <- vfold_cv(train_data)
###########################################################
##Produce the recipe
##Preprocessing
############################################################
rec <- recipe(Frequency ~ ., data = fid_df) %>%
step_nzv(all_predictors(), freq_cut = 0, unique_cut = 0) %>% # remove variables with zero variances
step_novel(all_nominal()) %>% # prepares test data to handle previously unseen factor levels
step_medianimpute(all_numeric(), -all_outcomes(), -has_role("id vars")) %>% # replaces missing numeric observations with the median
step_dummy(all_nominal(), -has_role("id vars")) # dummy codes categorical variables
###########################################################
##Create Models
###########################################################
##########################################################
##General Linear Models
#########################################################
##glm
mod_glm<-linear_reg(mode="regression",
penalty = 0.1,
mixture = 1) %>%
set_engine("glmnet")
##Create workflow
wflow_glm <- workflow() %>%
add_recipe(rec) %>%
add_model(mod_glm)
##Fit the model
plan(multisession)
fit_glm <- fit_resamples(
wflow_glm,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(save_pred = TRUE)
)
##########################################################
##Bagged Trees
##########################################################
#####Bagged Trees
mod_bag <- bag_tree() %>%
set_mode("regression") %>%
set_engine("rpart", times = 10) #10 bootstrap resamples
##Create workflow
wflow_bag <- workflow() %>%
add_recipe(rec) %>%
add_model(mod_bag)
##Fit the model
plan(multisession)
fit_bag <- fit_resamples(
wflow_bag,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(save_pred = TRUE)
)
###################################################
##Random forests
###################################################
mod_rf <-rand_forest(trees = 1e3) %>%
set_engine("ranger",
num.threads = parallel::detectCores(),
importance = "permutation",
verbose = TRUE) %>%
set_mode("regression")
##Create Workflow
wflow_rf <- workflow() %>%
add_model(mod_rf) %>%
add_recipe(rec)
##Fit the model
plan(multisession)
fit_rf<-fit_resamples(
wflow_rf,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(save_pred = TRUE)
)
############################################################
##Boosted Trees
############################################################
mod_boost <- boost_tree() %>%
set_engine("xgboost", nthreads = parallel::detectCores()) %>%
set_mode("regression")
##Create workflow
wflow_boost <- workflow() %>%
add_recipe(rec) %>%
add_model(mod_boost)
##Fit model
plan(multisession)
fit_boost <-fit_resamples(
wflow_boost,
cv,
metrics = metric_set(rmse, rsq),
control = control_resamples(save_pred = TRUE)
)
##############################################
##Evaluate the models
##############################################
collect_metrics(fit_bag) %>%
bind_rows(collect_metrics(fit_rf)) %>%
bind_rows(collect_metrics(fit_boost)) %>%
bind_rows(collect_metrics(fit_glm)) %>%
dplyr::filter(.metric == "rmse") %>%
dplyr::mutate(model = c("bag", "rf", "boost")) %>%
dplyr::select(model, everything()) %>%
knitr::kable()
####Error message
Error: Problem with `mutate()` input `model`.
x Input `model` can't be recycled to size 4.
ℹ Input `model` is `c("bag", "rf", "boost")`.
ℹ Input `model` must be size 4 or 1, not 3.
Run `rlang::last_error()` to see where the error occurred.
#####################################################
##Out-of-sample performance
#####################################################
# bagged trees
final_fit_bag <- last_fit(
wflow_bag,
split = split)
# random forest
final_fit_rf <- last_fit(
wflow_rf,
split = split)
# boosted trees
final_fit_boost <- last_fit(
wflow_boost,
split = split)
数据框 - FID
structure(list(Year = c(2015, 2015, 2015, 2015, 2015, 2015, 2015,
2015, 2015, 2015, 2015, 2015, 2016, 2016, 2016, 2016, 2016, 2016,
2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 2017, 2017,
2017, 2017, 2017, 2017, 2017, 2017, 2017), Month = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L), .Label = c("January", "February", "March",
"April", "May", "June", "July", "August", "September", "October",
"November", "December"), class = "factor"), Monsoon = structure(c(2L,
2L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 4L,
4L, 4L, 4L, 4L, 3L, 3L, 2L, 2L, 2L, 1L, 1L, 4L, 4L, 4L, 4L, 4L,
3L, 3L, 2L), .Label = c("First_Inter_Monssoon", "North_Monsoon",
"Second_Inter_Monsoon", "South_Monsson"), class = "factor"),
Frequency = c(36, 28, 39, 46, 5, 0, 0, 22, 10, 15, 8,
33, 33, 29, 31, 23, 8, 9, 7, 40, 41, 41, 30, 30, 44, 37,
41, 42, 20, 0, 7, 27, 35, 27, 43, 38), Days = c(31,
28, 31, 30, 6, 0, 0, 29, 15, 29, 29, 31, 31, 29, 30, 30,
7, 0, 7, 30, 30, 31, 30, 27, 31, 28, 30, 30, 21, 0, 7, 26,
29, 27, 29, 29)), row.names = c(NA, -36L), class = "data.frame")
【问题讨论】：
标签： r regression decision-tree glm tidymodels