Description: Validation accuracy is a fundamental metric in the field of machine learning, referring to a model’s ability to make correct predictions when evaluated on a validation dataset. This dataset is distinct from the training set, allowing for the assessment of the model’s generalization to unseen data. Accuracy is calculated as the proportion of correct predictions over the total predictions made. High validation accuracy indicates that the model has not only learned to memorize the training data but has also captured relevant patterns that enable it to make accurate predictions on new data. This metric is crucial for avoiding overfitting, where a model performs exceptionally well on the training set but fails to generalize to other data. In the context of machine learning frameworks, validation accuracy can be easily calculated using built-in functions that allow monitoring the model’s performance during training and adjusting hyperparameters accordingly. Thus, validation accuracy is not only an indicator of model performance but also guides the optimization and continuous improvement process.
