14.3 ML Modelling Setup
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For the rattle::weatherAUS dataset we similarly define the following template variables (Graham J. Williams 2017) used for predictive modelling. See Chapter 8 for details.
<- "risk_mm"
risk <- c("date", "location")
id <- c(risk, id)
ignore <- setdiff(vars, ignore)
vars <- setdiff(vars, target)
inputs
<- formula(target %s+% " ~ .")
form
%<>% na.roughfix()
ds[vars]
<- c(0.70, 0.15, 0.15)
SPLIT
%>% sample(SPLIT[1]*nobs) -> tr
nobs %>% seq_len() %>% setdiff(tr) %>% sample(SPLIT[2]*nobs) -> tu
nobs %>% seq_len() %>% setdiff(tr) %>% setdiff(tu) -> te
nobs
%>% slice(tr) %>% pull(target) -> actual_tr
ds %>% slice(tu) %>% pull(target) -> actual_tu
ds %>% slice(te) %>% pull(target) -> actual_te
ds
%>% slice(tr) %>% pull(risk) -> risk_tr
ds %>% slice(tu) %>% pull(risk) -> risk_tu
ds %>% slice(te) %>% pull(risk) -> risk_te ds
The 176,747 observations from the dataset have been randomly partitioned into a training dataset with 123,722 observations, a tuning dataset with 26,512 observations, and a testing dataset with 26,513 observations. The target variable (rain_tomorrow) has the classes: No (139670), Yes (37077).
References
Williams, Graham J. 2017. The Essentials of Data Science: Knowledge Discovery Using r. The r Series. CRC Press.
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