Test accuracy measures the proportion of correct predictions made by a model on a test dataset, serving as a crucial indicator of its performance and reliability. It is essential for evaluating how well a model generalizes to unseen data, beyond the training set.

Test Accuracy

Correct predictions

A test dataset is a subset of data used to evaluate the performance and accuracy of a machine learning model after it has been trained on a separate training dataset. It is crucial for assessing how well the model generalizes to new, unseen data, ensuring that the model's predictions are reliable and not just memorized responses to the training data.

Test Dataset

Indicator of performance

Indicator of reliability

Evaluating model performance

Generalization to unseen data

A training set is a collection of data used to teach a machine learning model to recognize patterns and make predictions. It is crucial for the model's ability to generalize to new, unseen data by providing a diverse and representative sample of the problem domain.

Concepts

Your Lessons