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Early stopping: Difference between revisions
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==How does early stopping work?== | ==How does early stopping work?== | ||
Early stopping occurs by comparing the performance of a model on both the [[training set]] and validation set during training. The validation set is an omitted portion of data from the training set that allows us to assess its suitability for unseen data. As the model learns on this set, its performance on the validation set is monitored at regular intervals; if | Early stopping occurs by comparing the performance of a model on both the [[training set]] and validation set during training. The validation set is an omitted portion of data from the training set that allows us to assess its suitability for unseen data. As the model learns on this set, its performance on the validation set is monitored at regular intervals; if [[validation loss]] starts to increase, then training is stopped and the model with the best [[validation]] performance is selected as the final model. | ||
Early stopping can be implemented using various criteria, such as monitoring validation loss or [[accuracy]]. The criteria used to terminate training can be set based on domain knowledge or determined empirically through experimentation. One popular approach involves using a [[patience]] [[parameter]] which controls how many [[epoch]]s of training may continue without improvement in validation performance. If after these specified number of epochs have passed without improvement in validation performance, training is then terminated. | Early stopping can be implemented using various criteria, such as monitoring validation loss or [[accuracy]]. The criteria used to terminate training can be set based on domain knowledge or determined empirically through experimentation. One popular approach involves using a [[patience]] [[parameter]] which controls how many [[epoch]]s of training may continue without improvement in validation performance. If after these specified number of epochs have passed without improvement in validation performance, training is then terminated. | ||
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Early stopping is a technique used to stop training a computer program when it becomes too adept at remembering what it has already seen and not good at predicting new information. It's like practicing riding your bike without your mom telling you to stop, so as not to get hurt. When the computer program gets too proficient in what it already knows, it may not be capable of learning new things; thus, early stopping helps the program be equally proficient at both what it already knows and what it will discover. | Early stopping is a technique used to stop training a computer program when it becomes too adept at remembering what it has already seen and not good at predicting new information. It's like practicing riding your bike without your mom telling you to stop, so as not to get hurt. When the computer program gets too proficient in what it already knows, it may not be capable of learning new things; thus, early stopping helps the program be equally proficient at both what it already knows and what it will discover. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | ||