10.46 Dealing with Correlations

20180726 From the final result we can identify pairs of variables where we might want to keep one but not the other variable because they are highly correlated. We will select them manually since it is a judgement call. Normally we might limit the removals to those correlations that are 0.90 or more. In our case here the three pairs of highly correlated variables make intuitive sense.

# Note the correlated variables that are redundant.

correlated <- c("temp_3pm", "pressure_3pm", "temp_9am")

# Add them to the variables to be ignored for modelling.

ignore <- union(ignore, correlated) %T>% print()
## [1] "date"         "location"     "risk_mm"      "temp_3pm"     "pressure_3pm"
## [6] "temp_9am"


Your donation will support ongoing development and give you access to the PDF version of this 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.