Create a low-variance filter
In this exercise, you are given house_sales_df which contains seventeen continuous features. Some of those features do not have any variance. Some of them have very little variance. You will explore the variances and establish a filter using an appropriate variance threshold. This approach is useful for reducing dimensions with little to no information, but as you'll see, it has a few drawbacks.
The tidyverse and tidymodels packages have been loaded for you.
यह अभ्यास पाठ्यक्रम का हिस्सा है
Dimensionality Reduction in R
इंटरैक्टिव व्यावहारिक अभ्यास
इस अभ्यास को इस नमूना कोड को पूरा करके आज़माएँ।
# Calculate feature variances
houses_sales_variances <- ___ %>%
summarize(across(everything(), ~ ___(___(., ___ = ___), na.rm = ___))) %>%
pivot_longer(everything(), names_to = "feature", values_to = "variance") %>%
___(desc(___))
houses_sales_variances