Can someone please explain why/how Count encoding of categorical features improve accuracy in classification when compared to simply label encoding them ?
I found one explanation in kaggle " Rare values tend to have similar counts (with values like 1 or 2), so you can classify rare values together at prediction time. Common values with large counts are unlikely to have the same exact count as other values. So, the common/important values get their own grouping. " which doesn't seem convincing or I don't understand the reasoning .
Can someone please explain why it performs better than label encoding ? Label encoding can also find frequent patterns to correlate with the target variable , right ?