You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Description: The current implementation of the XGBoost classifier in the Ivy library does not appear to support Scipy sparse matrices. This feature would be beneficial for handling large, sparse datasets efficiently.
Suggested Implementation: I propose adding support for Scipy sparse matrix inputs to the XGBoost classifier within the Ivy library. This would involve ensuring compatibility with Scipy’s CSR or CSC matrix formats.
Benefits:
Enables the use of sparse data representations, reducing memory usage and potentially improving computational performance.
Aligns the Ivy library’s XGBoost classifier with other machine learning frameworks that already support sparse matrices.
The text was updated successfully, but these errors were encountered:
Description: The current implementation of the XGBoost classifier in the Ivy library does not appear to support Scipy sparse matrices. This feature would be beneficial for handling large, sparse datasets efficiently.
Suggested Implementation: I propose adding support for Scipy sparse matrix inputs to the XGBoost classifier within the Ivy library. This would involve ensuring compatibility with Scipy’s CSR or CSC matrix formats.
Benefits:
Enables the use of sparse data representations, reducing memory usage and potentially improving computational performance.
Aligns the Ivy library’s XGBoost classifier with other machine learning frameworks that already support sparse matrices.
The text was updated successfully, but these errors were encountered: