What is data sparsity in machine learning?

What is data sparsity in machine learning?

Data sparsity refers to the situation where the dataset has a lot of missing or incomplete data points, making it challenging for machine learning models to learn effectively.

Why is data sparsity a problem for ML models?

Data sparsity can lead to overfitting, where models learn noise instead of patterns, and can also reduce the model's ability to generalize to new data.

How can we handle data sparsity?

Common techniques include data augmentation, using algorithms that can handle missing data, and employing regularization methods.

Can you give an example of data sparsity?

Sure! In a recommendation system, if only a few users have rated a large number of items, the resulting user-item matrix is sparse.