The validation dataset is used to evaluate the model's performance on data it hasn't seen during training. This helps in fine-tuning the model's hyperparameters and assessing its ability to generalize to new data, thereby preventing overfitting. The training dataset is used to learn the model's parameters through optimization, while the test dataset (if used) assesses the final model's performance after training is complete. Simply increasing the amount of training data does not serve the purpose of validation.
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Microsoft Azure AI Fundamentals AI-900
Describe Fundamental Principles of Machine Learning on Azure
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