What is NOT a valid data preprocessing strategy?

Compression is not considered a valid data preprocessing strategy in the context of preparing data for machine learning or data analysis. Preprocessing typically involves techniques that enhance the quality and usability of the data for modeling purposes. Normalization adjusts the values into a specific range, scaling alters the range of features, and encoding converts categorical data into a numerical format suitable for algorithms.

While compression can reduce the overall size of the dataset, it doesn't directly relate to modifying the data for improved performance in machine learning processes. Instead, it focuses on storage efficiency rather than preparing the data for analysis or learning. Thus, the other strategies listed—normalization, scaling, and encoding—are integral to ensuring the data is appropriately conditioned for training models and drawing meaningful insights.