77 lines
1.7 KiB
R
77 lines
1.7 KiB
R
rm(list = ls(all.names = TRUE)) # Clear the memory of variables from previous run.
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cat("\014") # Clear the console
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# load packages -----------------------------------------------------------
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box::use(
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magrittr[`%>%`]
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,here[here]
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,readr
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,gp2 = ggplot2[ggplot, aes]
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,rsample
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,r = recipes
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,wf = workflows
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,p = parsnip
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,ys = yardstick
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)
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# globals -----------------------------------------------------------------
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set.seed(070823) #set seed for reproducible research
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# load-data ---------------------------------------------------------------
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model_data <- readr$read_rds(here("ML","data-unshared","model_data.RDS"))
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# split data --------------------------------------------------------------
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model_data_split <- rsample$initial_split(
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model_data
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,prop = 0.80
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,strata = ft4_dia
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)
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ds_train <- rsample$training(model_data_split)
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ds_test <- rsample$testing(model_data_split)
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# verify distribution of data
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table(ds_train$ft4_dia) %>% prop.table()
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table(ds_test$ft4_dia) %>% prop.table()
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# random forest -----------------------------------------------------------
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rf_model <- p$rand_forest(trees = 1900) %>%
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p$set_engine("ranger") %>% p$set_mode("classification")
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rf_recipe <- r$recipe(ft4_dia ~ . , data = ds_train) %>%
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r$update_role(subject_id, new_role = "id") %>%
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r$update_role(charttime, new_role = "time") %>%
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r$step_impute_bag(r$all_numeric())
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rf_workflow <- wf$workflow() %>%
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wf$add_model(rf_model) %>%
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wf$add_recipe(rf_recipe)
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rf_fit <- p$fit(rf_workflow, ds_train)
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rf_predict <- ds_train %>%
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dplyr::select(ft4_dia) %>%
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dplyr::bind_cols(
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predict(rf_fit, ds_train)
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,predict(rf_fit, ds_train, type = "prob")
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)
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ys$accuracy(rf_predict, ft4_dia, .pred_class)
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ys$conf_mat(rf_predict, ft4_dia, .pred_class)
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