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When working with very sparse data in machine learning and boosting tasks, the following techniques are applied:

• Using sparse storage formats: For example, CSR (Compressed Sparse Row) or COO, to save memory and speed up calculations.
• Feature engineering considering sparsity: Creating features that treat the absence of data as a separate piece of information.
• Regularization and boosting: Using models resistant to sparsity, such as gradient boosting (XGBoost, LightGBM), which work effectively with missing and sparse features.
• Handling missing values: Special imputation methods or using the built-in capabilities of models to work with missing data.
• Feature selection: Removing overly rare features that carry little information and can degrade model quality.

Example: LightGBM automatically handles sparse data and missing values, simplifying working with such datasets.