Create a missing values recipe

In the previous exercises, you manually calculated the missing value ratio and created a filter to reduce the dimensionality of house_sales_df. The tidymodels package contains a recipe step to apply a missing values ratio automatically—step_filter_missing(). The advantages of the tidymodels approach is that it allows you reuse the recipe on other data sets and simplifies the move to a production environment. In this exercise, you will use the step_filter_missing() function to perform dimensionality reduction house_sales_df based on missing values.

The tidyverse and tidymodels packages have been loaded for you.

यह अभ्यास पाठ्यक्रम का हिस्सा है

Dimensionality Reduction in R

पाठ्यक्रम देखें

अभ्यास निर्देश

Use recipe() to create a missing values filter with a threshold of 0.5.
Apply the missing_vals_recipe to house_sales_df.

इंटरैक्टिव व्यावहारिक अभ्यास

इस अभ्यास को इस नमूना कोड को पूरा करके आज़माएँ।

# Create missing values recipe
missing_vals_recipe <- 
  ___(___ ~ ., data = ___) %>% 
  ___(___(), ___ = ___) %>% 
  prep()
  
# Apply recipe to data
___ <- 
  ___(___, ___ = ___)

# Display the first five rows of data
___ %>% ___(___)

कोड संपादित करें और चलाएँ