Which method improves efficiency during one-hot encoding particularly for large datasets?

Using sparse vectors for storage significantly improves efficiency during one-hot encoding, especially in the context of large datasets. One-hot encoding transforms categorical variables into a format that can be provided to machine learning algorithms, where each category is represented by a binary vector. In the case of a large dataset with many categorical variables, this can lead to a very high-dimensional space.

By using sparse vectors, you only store the non-zero entries of these binary vectors, which drastically reduces the amount of memory required. For instance, if a category has many levels, using sparse representation means that instead of allocating space for every possible category level (which could be numerous), you only store a value if it exists, along with its index. This results in substantial savings in both memory usage and computational efficiency, allowing for faster processing and reduced load on systems.

The other methods listed do not directly target the efficiency improvements in handling large datasets for one-hot encoding. Dense retrieval, compacting data into a single feature, and improving model interpretability do not effectively address the challenges posed by the high dimensionality and sparsity associated with one-hot encoding in large datasets.