When working with categorical data, encoding strategies can make or break your machine learning model. But what happens when you’re dealing with high cardinality columns, like compound drug names, where each value is essentially unique?
I recently stumbled upon this issue in a sentiment analysis project, where the drug names column had over 1,200 unique values, including variations like ‘Levonorgestrel’ and ‘Ethinyl estradiol / levonorgestrel’. The problem is, traditional encoding methods can either create too many columns (one-hot encoding) or imply false orders (label encoding).
The Challenge of High Cardinality
High cardinality occurs when a categorical column has a large number of unique values. In this case, the drug names column had over 1,200 unique values, making it difficult to encode effectively. The goal is to find an encoding strategy that balances dimensionality with information retention.
Encoding Strategies for High Cardinality
So, what are the best encoding strategies for high cardinality columns like compound drug names?
Frequency Encoding
Frequency encoding is a simple yet effective method. The idea is to assign a numerical value based on the frequency of each category. For example, the most frequent drug name would get a value of 1, the second most frequent would get a value of 2, and so on.
Target Encoding
Target encoding is another approach that uses the target variable (in this case, sentiment) to guide the encoding process. The idea is to assign a numerical value based on the target variable’s mean or median for each category.
Grouping Rares
Grouping rare categories is a common technique used to reduce dimensionality. The idea is to group infrequent categories into a single ‘rare’ category, reducing the number of unique values.
Using Category Encoders and Dirty-Cat
Category Encoders and Dirty-Cat are libraries that provide various encoding strategies, including frequency and target encoding. These libraries can be especially useful when dealing with high cardinality columns.
Conclusion
Encoding compound drug names for sentiment analysis requires careful consideration of the encoding strategy. By balancing dimensionality with information retention, you can create a more effective model. Frequency encoding, target encoding, and grouping rares are all viable options, and using libraries like Category Encoders and Dirty-Cat can simplify the process.
What’s your go-to encoding strategy for high cardinality columns? Share your experiences in the comments below!