Day #92 - Mean Encoding

Mean Coding

• Add new variables based on certain features
• Label encoding is done usually
• Mean encoding is done as variable count / distinct unique variables
• The proportion of label encoding also is included in this step
• Min encoding with label encoding
• Label encoding - No logical order
• Mean encoding - Classes are separable
• We can reach better loss with sorted trees
• Trees need huge number of splits 
• Model tries to treat all categories differently

Constructing Mean Encoding

• Goods - Number of ones in a group
• Bads - Number of zeros

Likelihood = Goods/(Goods + Bads) = mean(target)
Weight of Evidence = In(Goods/Bads)*100
Count = Goods = sum(target)
Diff = Goods-Bads

Happy Learning!!!