What is the primary purpose of using Cross-Validation in Machine Learning?
- To enhance the model's complexity
- To estimate the model's performance on unseen data
- To increase the training speed
- To select optimal hyperparameters
Cross-Validation's primary purpose is to estimate the model's performance on unseen data by dividing the dataset into training and validation sets. It provides a more reliable evaluation than using a single static validation set.
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