Data Science Desktop Survival Guide
by Graham Williams |
|||||
ML Setup |
20210102 Packages used in this chapter include ROCR, ggplot2, rpart, and rattle.
Packages are loaded into the currently running R session from your
local library directories on disk. Missing packages can be installed
using utils::install.packages() within R. On Ubuntu, for
example, R packages can be installed using
wajig install r-cran-<pkgname>
.
# Load required packages from local library into the R session.
library(ROCR) # Use prediction() for evaluation. library(ggplot2) # Display evaluations. library(rattle) # Dataset: weather. library(rpart) # ML: decision tree rpart(). library(scales) # Support: commas(), percent().
The rattle::weatherAUS dataset is loaded into the template variable ds and further template variables are setup as introduced in Williams (2017). See Chapter 7 for details.
|
dsname <- "weatherAUS"
ds <- get(dsname) nobs <- nrow(ds) vnames <- names(ds) ds %<>% clean_names(numerals="right") names(vnames) <- names(ds) vars <- names(ds) target <- "rain_tomorrow" vars <- c(target, vars) %>% unique() %>% rev()
It is always useful to remind ourselves of the dataset with a random sample:
|
ds %>% sample_frac() %>% select(date, location, sample(3:length(vars), 5))
|