Working With Sparse Features In Machine Learning Models

Posted by365Data SciencePosted inData Science News
Sparse features can cause problems like overfitting and suboptimal results in learning models, and understanding why this happens is crucial when developing models. Multiple methods, including dimensionality reduction, are available to overcome issues due to sparse features.

Originally from KDnuggets https://ift.tt/3bydpfi

source https://365datascience.weebly.com/the-best-data-science-blog-2020/working-with-sparse-features-in-machine-learning-models

Published by 365Data Science

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