Tidymodel 包：R 中的通用线性模型 (glm) 和决策树（袋装树、提升树和随机森林）模型
[英]Tidymodel Package: General linear models (glm) and decision tree (bagged trees, boosted trees, and random forest) models in R
问题描述
Issue
问题
I am attempting to undertake an analysis using the Tidymodels Package in R .我正在尝试使用R 中的Tidymodels 包进行分析。 I am following this tutorial below regarding decision tree learning in R:-我正在关注以下有关 R 中决策树学习的教程：-
Tutorial教程
https://bcullen.rbind.io/post/2020-06-02-tidymodels-decision-tree-learning-in-r/ https://bcullen.rbind.io/post/2020-06-02-tidymodels-decision-tree-learning-in-r/
I have a data frame called FID (see below) where the dependent variable is the frequency (numeric) , and the predictor variables are:- Year (numeric), Month (factor), Monsoon (factor), and Days (numeric).我有一个名为 FID （见下文）的数据框，其中因变量是频率（数字） ，预测变量是：-年（数字）、月（因子）、季风（因子）和天（数字）。
I believe I have successfully followed the tutorial named "Tidymodels: Decision Tree Learning in R" by building a bagged tree, random forest, and boosted tree model.我相信我已经通过构建袋装树、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。
For this analysis, I would also like to construct a general linear model (glm) in order to make model comparisons between all models (ie the random forest, bagged tree, boosted tree, and general linear models) to establish the best model fit.对于这个分析，我还想构建一个通用线性模型（glm） ，以便在所有模型（即随机森林、袋装树、提升树和通用线性模型）之间进行模型比较，以建立最佳模型拟合。 All models are subject to 10-fold cross-validation to decrease the bias of overfitting.所有模型都经过10 倍交叉验证以减少过度拟合的偏差。
Problem问题
Subsequently, I have attempted to adapt the code (please see below) from the tutorial to fit a glm model, but I am confused whether I have tuned the model appropriately.随后，我尝试调整教程中的代码（请参见下文）以适应 glm 模型，但我很困惑我是否适当地调整了模型。 I am unsure if this element of glm R-code is producing the error message below when I am attempting to produce the rmse values after the models have all been fit:-当我尝试在模型全部拟合后生成rmse值时，我不确定 glm R-code 的这个元素是否会产生以下错误消息：-
Error message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.
In addition, I am unsure if the R code expressed in the recipe() function for these models is adequate or correct, which is very important during the processing steps before fitting each model.此外，我不确定这些模型的 recipe() 函数中表达的R 代码是否足够或正确，这在拟合每个模型之前的处理步骤中非常重要。 From my perspective, I was wondering if the recipe for the models could be improved.从我的角度来看，我想知道模型的配方是否可以改进。
If this is possible, I was wondering if anyone could please help me regarding the error message when fitting the glm model, in conjunction with correcting the recipe (if this is necessary).如果这是可能的，我想知道是否有人可以帮助我了解拟合 glm 模型时的错误消息，并结合更正配方（如果有必要）。
I am not an advanced R coder, and I have made a thorough attempt to try and find a solution by researching other Tidymodel tutorials;我不是一个高级的 R 编码员，我已经通过研究其他 Tidymodel 教程进行了彻底的尝试，试图找到解决方案； but, I do not understand what this error message means.但是，我不明白此错误消息的含义。 Therefore, if anyone is able to help, I would like to express my deepest appreciation.因此，如果有人能够提供帮助，我要表示最深切的感谢。
Many thanks in advance.提前谢谢了。
R-Code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)
Data Frame - FID数据帧 - 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")
2 个解决方案
解决方案1
1 已采纳 2020-11-18 20:58:08
I believe the error from fitting the linear model is coming from how Month and Monsoon are related to each other.我相信拟合线性模型的错误来自Month和Monsoon彼此之间的关系。 They are perfectly correlated:它们完全相关：
library(tidyverse)
fid_df <- 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")
fid_df %>%
count(Month, Monsoon)
#> Month Monsoon n
#> 1 January North_Monsoon 3
#> 2 February North_Monsoon 3
#> 3 March First_Inter_Monssoon 3
#> 4 April First_Inter_Monssoon 3
#> 5 May South_Monsson 3
#> 6 June South_Monsson 3
#> 7 July South_Monsson 3
#> 8 August South_Monsson 3
#> 9 September South_Monsson 3
#> 10 October Second_Inter_Monsoon 3
#> 11 November Second_Inter_Monsoon 3
#> 12 December North_Monsoon 3
If you use variables like this in a linear model, the model cannot find estimates for both sets of coefficients:如果您在线性模型中使用这样的变量，则模型无法找到两组系数的估计值：
lm(Frequency ~ ., data = fid_df) %>% summary()
#>
#> Call:
#> lm(formula = Frequency ~ ., data = fid_df)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -15.0008 -3.9357 0.6564 2.9769 12.7681
#>
#> Coefficients: (3 not defined because of singularities)
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -7286.9226 3443.9292 -2.116 0.0459 *
#> Year 3.6155 1.7104 2.114 0.0461 *
#> MonthFebruary -3.2641 6.6172 -0.493 0.6267
#> MonthMarch 0.1006 6.5125 0.015 0.9878
#> MonthApril 0.4843 6.5213 0.074 0.9415
#> MonthMay -4.0308 11.0472 -0.365 0.7187
#> MonthJune 1.0135 15.5046 0.065 0.9485
#> MonthJuly -2.6910 13.6106 -0.198 0.8451
#> MonthAugust -4.9307 6.6172 -0.745 0.4641
#> MonthSeptember -1.7105 7.1126 -0.240 0.8122
#> MonthOctober -7.6981 6.5685 -1.172 0.2538
#> MonthNovember -8.7484 6.5493 -1.336 0.1953
#> MonthDecember -1.6981 6.5685 -0.259 0.7984
#> MonsoonNorth_Monsoon NA NA NA NA
#> MonsoonSecond_Inter_Monsoon NA NA NA NA
#> MonsoonSouth_Monsson NA NA NA NA
#> Days 1.1510 0.4540 2.535 0.0189 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 7.968 on 22 degrees of freedom
#> Multiple R-squared: 0.8135, Adjusted R-squared: 0.7033
#> F-statistic: 7.381 on 13 and 22 DF, p-value: 2.535e-05
Created on 2020-11-18 by the reprex package (v0.3.0.9001)由reprex 包(v0.3.0.9001) 于 2020 年 11 月 18 日创建
Since you have this info, I would recommend using some domain knowledge to decide whether to use Month or Monsoon in the model but not both.由于您拥有此信息，我建议您使用一些领域知识来决定是在模型中使用Month还是Monsoon ，但不要同时使用两者。
解决方案2
1 2020-11-19 02:31:43
Answer with Julia Silge's suggestions回答 Julia Silge 的建议
#split this single dataset into two: a training set and a testing set
data_split <- initial_split(Tidy_df)
# 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
rec <- recipe(Frequency_Blue ~ ., data = Tidy_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", "glm")) %>%
dplyr::select(model, everything()) %>%
knitr::kable()
##rmse values for all models
|model |.metric |.estimator | mean| n| std_err|
|:-----|:-------|:----------|---------:|--:|--------:|
|bag |rmse |standard | 8.929936| 10| 1.544587|
|rf |rmse |standard | 10.188710| 10| 1.144354|
|boost |rmse |standard | 9.249904| 10| 1.489482|
|glm |rmse |standard | 11.348420| 10| 2.217807|
#####################################################
##Out-of-sample performance
#####################################################
#glm
# bagged trees
final_fit_glm <- last_fit(
wflow_glm,
split = split)
# 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)
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