21.2 Decision Trees Modelling Setup
20200815
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 191,431 observations from the dataset have been randomly partitioned into a training dataset with 134,001 observations, a tuning dataset with 28,714 observations, and a testing dataset with 28,716 observations. The target variable (rain_tomorrow) has the classes: No (151118), Yes (40313).
Your donation will support ongoing development and give you access to the PDF version of the book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984.
Copyright © 1995-2021 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0.