Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE,eval = FALSE)
## -----------------------------------------------------------------------------
# library(magrittr)
# library(fastai)
# df = data.table::fread('datasets_236694_503227_HR_comma_sep.csv')
# str(df)
## -----------------------------------------------------------------------------
# df[['left']] = as.factor(df[['left']])
## -----------------------------------------------------------------------------
# dep_var = 'left'
# cat_names = c('sales', 'salary')
# cont_names = c("satisfaction_level", "last_evaluation", "number_project",
# "average_montly_hours", "time_spend_company",
# "Work_accident", "promotion_last_5years")
## -----------------------------------------------------------------------------
# tot = 1:nrow(df)
# tr_idx = sample(nrow(df), 0.8 * nrow(df))
# ts_idx = tot[!tot %in% tr_idx]
## -----------------------------------------------------------------------------
# procs = list(FillMissing(),Categorify(),Normalize())
## -----------------------------------------------------------------------------
# dls = TabularDataTable(df, procs, cat_names, cont_names,
# y_names = dep_var, splits = list(tr_idx, ts_idx) ) %>%
# dataloaders(bs = 50)
## -----------------------------------------------------------------------------
# model = dls %>% tabular_learner(layers=c(200,100,100,200),
# config = tabular_config(embed_p = 0.3, use_bn = FALSE),
# metrics = list(accuracy, RocAucBinary(),
# Precision(), Recall(),
# F1Score()))
## -----------------------------------------------------------------------------
# lrs = model %>% lr_find()
# # SuggestedLRs(lr_min=0.002754228748381138, lr_steep=1.5848931980144698e-06)
#
# model %>% plot_lr_find()
## -----------------------------------------------------------------------------
# res = model %>% fit(5, lr = 0.005)
## -----------------------------------------------------------------------------
# model %>% get_confusion_matrix() %>%
# fourfoldplot(conf.level = 0, color = c("#ed3b3b", "#0099ff"),
# margin = 1,main = paste("Confusion Matrix",
# round(sum(diag(.))/sum(.)*100,0),"%",sep = ' '))
## -----------------------------------------------------------------------------
# model %>% predict(df[1000:1010,])
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