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Validation loss: Difference between revisions
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Validation loss is like taking a test to assess how well you comprehend what was taught in class. The teacher presents you with new questions that test your knowledge, and if they are answered correctly, it shows that you understand and can apply the material in different contexts. On the other hand, failure indicates more practice is needed to fully grasp it. Validation loss also serves as an assessment for machine learning models to gauge their capacity for applying their insights on new problems they haven't faced before; if they perform well, it indicates their capacity for applying what they learned elsewhere. If they perform well, it indicates they can apply what they've learned across diverse problems without fail; otherwise they might fail miserably when presented with new problems. | Validation loss is like taking a test to assess how well you comprehend what was taught in class. The teacher presents you with new questions that test your knowledge, and if they are answered correctly, it shows that you understand and can apply the material in different contexts. On the other hand, failure indicates more practice is needed to fully grasp it. Validation loss also serves as an assessment for machine learning models to gauge their capacity for applying their insights on new problems they haven't faced before; if they perform well, it indicates their capacity for applying what they learned elsewhere. If they perform well, it indicates they can apply what they've learned across diverse problems without fail; otherwise they might fail miserably when presented with new problems. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | ||