Edit: changed the title, removed call for opinions

This post compares several methods of encoding categorical data. Binary encoding (convert categories to integers, then to binary; assign each digit a separate column) seems to provide the best combination of predictive accuracy and dimensionality control.

However, the top answer to this post advocates applying PCA to one-hot encoded (convert categories to integers; assign each integer value a separate column, e.g. 5 = 0, 0, 0, 0, 1) data to isolate the most descriptive dimensions within. This would seem to be even better, dimension-wise, than binary encoding, without the associated distortion of distances. Has anyone compared binary vs. one-hot + PCA?


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