Validation Data
What is Validation Data?
In the artificial intelligence industry, validation data is a crucial subset of the dataset that is utilized to fine-tune and evaluate the performance of a machine learning model. Unlike training data, which the model learns from, validation data helps in adjusting hyperparameters and making decisions about model improvements without being directly used in the training process. This ensures that the model does not overfit to the training data and can generalize well to unseen data. Validation data acts as an intermediary step before testing the model with the test data, providing an unbiased evaluation metric to monitor the model's performance. By using validation data, data scientists aim to develop models that perform consistently well on new, real-world data.
Data used to evaluate the performance of a machine learning model during training.
Examples
- A company developing a speech recognition system might use a dataset of recorded conversations. They would split this dataset into training, validation, and test sets. The validation set would include conversations not seen by the model during training, allowing the developers to fine-tune the model's accuracy and performance.
- In developing a facial recognition system, a tech firm might collect thousands of images. They would divide these images into training, validation, and test sets. The validation data would include images that the model hasn't been trained on, helping the firm adjust the model's parameters for better generalization.
Additional Information
- Validation data helps prevent overfitting by providing a check on the model's ability to generalize.
- It is a standard practice to split the dataset into approximately 70% training, 15% validation, and 15% testing.