Summary

Throughout this chapter, we explored various techniques for encoding categorical variables essential for ML tasks. Label encoding, which assigns unique integers to each category, is straightforward but may inadvertently impose ordinality where none exists. One-hot encoding transforms each category into a binary feature, maintaining categorical independence but potentially leading to high-dimensional datasets. Binary encoding condenses categorical values into binary representations, balancing interpretability, and efficiency particularly well for high-cardinality datasets. Frequency encoding replaces categories with their occurrence frequencies, capturing valuable information about distributional patterns. Target encoding incorporates target variable statistics into categorical encoding, enhancing predictive power while requiring careful handling to avoid data leakage.

Let’s summarize our learning in the following table:

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