Mutual information features

The credit_df data frame contains a number of continuous features. When two continuous features are correlated, they contain the same information — something called mutual information. Highly correlated features are not just redundant. They can cause problems in modeling. For instance, in regression, highly correlated features (i.e., multicollinearity) can cause nonsensical results. To get a sense of mutual information, you will create a correlation plot to identify features with mutual information.

The tidyverse and corrr packages have been loaded for you.

Este exercício faz parte do curso

Dimensionality Reduction in R

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Instruções do exercício

Use correlate() and rplot() to create a correlation plot of the numeric features of credit_df.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Create a correlation plot
___ %>% 
  select(where(is.numeric)) %>% 
  ___() %>% 
  shave() %>% 
  ___(print_cor = TRUE) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

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