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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
[[Test loss]] is a [[metric]] that measures a [[model]]'s [[loss]] against the [[test data set]]. Note that the [[test dataset]] is a separate [[dataset]] from the [[training data set]] and the [[validation data set]]. Testing the model on the test set is like a final test for an already trained [[machine learning model]]. | [[Test loss]] is a [[metric]] that measures a [[model]]'s [[loss]] against the [[test data set]]. Note that the [[test dataset]] is a separate [[dataset]] from the [[training data set]] and the [[validation data set]]. Testing the model on the test set is like a final test for an already trained [[machine learning model]]. The lower the test loss is, the better the model is. | ||
[[Machine learning]] [[algorithm]]s measure their model's ability to make accurate predictions on unseen [[data]]. The test loss provides an assessment of a model's generalization ability, or its capacity for making accurate predictions when presented with new information that was not seen during [[training]]. | [[Machine learning]] [[algorithm]]s measure their model's ability to make accurate predictions on unseen [[data]]. The test loss provides an assessment of a model's generalization ability, or its capacity for making accurate predictions when presented with new information that was not seen during [[training]]. | ||
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==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||
Test loss is like a test we give our model to see how well it understands what we taught it. Just like taking an exam in school to demonstrate your mastery of content, test loss helps us determine just how well our model comprehends what was presented to it. | |||
When teaching a model, we provide it with examples to learn from and also keep some unknown so we can test its knowledge later on. Test loss is an indicator of how well the model has learned what we taught it; the lower this number, the better equipped it will be to guess answers when faced with questions that have never been asked before. | |||
Just like when you receive a high grade on a test, a low test loss indicates that our model is doing an effective job of understanding what we have taught it. Conversely, a high loss indicates our model is struggling with understanding, much like when receiving an unsatisfactory grade on your exam. | |||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] | ||