Data Partition 편집하기 (부분)

==Importance of Data Partitioning==
Data partitioning is crucial in machine learning for several reasons:
*'''Prevents Overfitting''': By testing the model on unseen data, partitioning reduces the risk of overfitting, where the model performs well on training data but poorly on new data.
*'''Ensures Generalization''': Using separate data for training, validation, and testing helps ensure that the model generalizes to new data, a critical aspect for real-world applications.
*'''Improves Model Selection''': Data partitioning with validation sets enables effective hyperparameter tuning, allowing for optimized model performance without impacting the final test results.