Data Partition 편집하기 (부분)

==Types of Data Partitions==
Data partitioning generally involves dividing data into three main sets:
*'''Training Set''': Used to train the model and learn patterns from the data. The model’s parameters are adjusted based on this subset.
*'''Validation Set''': Used for model selection and hyperparameter tuning, helping to evaluate how well the model performs on unseen data during training. This helps in fine-tuning without affecting the final evaluation.
*'''Test Set''': A separate subset used to evaluate the model's final performance. The test set provides an unbiased measure of model accuracy and generalization.