Description: Test accuracy is a fundamental metric in the field of machine learning that evaluates the performance of a model when applied to a dataset that has not been used during its training. This metric is defined as the proportion of correct predictions made by the model on the test set, compared to the total predictions made. Test accuracy is crucial for determining a model’s generalization ability, that is, its ability to make accurate predictions on unseen data. A model with high test accuracy indicates that it has learned relevant patterns in the training data and can effectively apply them to new data. Conversely, low test accuracy may indicate overfitting, where the model has adapted too closely to the training data and cannot generalize well. Test accuracy is used in various applications across diverse domains in artificial intelligence and machine learning, and is one of the most common metrics for evaluating models in data science competitions and academic research.
