bethatkinson
commented
3 years ago The class of mod_tree is not "rpart" so there is no guarantee that it will work using downstream packages. This is caused by the "tidymodels" package, not rpart itself.
class(mod_tree) [1] "_rpart" "model_fit"
fit <- rpart(form, data=train)
plot(as.party(fit)) ## This works
From: thamron @.> Sent: Tuesday, August 24, 2021 9:29 PM To: bethatkinson/rpart @.> Cc: Subscribed @.***> Subject: [EXTERNAL] [bethatkinson/rpart] Error 'data' must be a data.frame, environment, or list (#30)
Fail to convert rpart object to class party for plotting. The codes are shown below:
`library(tidyverse) library(mdsr) url <- "http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data" census <- read_csv( url, col_names = c( "age", "workclass", "fnlwgt", "education", "education_1", "marital_status", "occupation", "relationship", "race", "sex", "capital_gain", "capital_loss", "hours_per_week", "native_country", "income" ) ) %>% mutate(income = factor(income))
library(tidymodels) set.seed(364) n <- nrow(census) census_parts <- census %>% initial_split(prop = 0.8) train <- census_parts %>% training() test <- census_parts %>% testing() pi_bar <- train %>% count(income) %>% mutate(pct = n / sum(n)) %>% filter(income == ">50K") %>% pull(pct) pi_bar
form <- as.formula( "income ~ age + workclass + education + marital_status + occupation + relationship + race + sex + capital_gain + capital_loss + hours_per_week" )
mod_tree <- decision_tree(mode = "classification") %>% set_engine("rpart") %>% fit(form, data = train) mod_tree
library(rpart) library(partykit) plot(as.party(mod_tree$fit)) `
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Fail to convert rpart object to class party for plotting. The codes are shown below:
`library(tidyverse) library(mdsr) url <- "http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data" census <- read_csv( url, col_names = c( "age", "workclass", "fnlwgt", "education", "education_1", "marital_status", "occupation", "relationship", "race", "sex", "capital_gain", "capital_loss", "hours_per_week", "native_country", "income" ) ) %>% mutate(income = factor(income))
library(tidymodels) set.seed(364) n <- nrow(census) census_parts <- census %>% initial_split(prop = 0.8) train <- census_parts %>% training() test <- census_parts %>% testing() pi_bar <- train %>% count(income) %>% mutate(pct = n / sum(n)) %>% filter(income == ">50K") %>% pull(pct) pi_bar
form <- as.formula( "income ~ age + workclass + education + marital_status + occupation + relationship + race + sex + capital_gain + capital_loss + hours_per_week" )
mod_tree <- decision_tree(mode = "classification") %>% set_engine("rpart") %>% fit(form, data = train) mod_tree
library(rpart) library(partykit) plot(as.party(mod_tree$fit)) `