In machine learning (ML), Overfitting happens when a model grows overly complex and starts to perform well on training data but poorly on untrained data. It suggests that the model has learned noise from the training data rather than the underlying patterns, lowering generalization ability.
Overfitting
SHARE
Related Links
Many enterprises using Databricks for ETL workflows face challenges with isolated data management across workspaces. This…
Businesses are embracing the scalability and flexibility offered by cloud solutions. However, cloud migration often poses…
