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Generalization curve: Difference between revisions
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
Generalization curve is a plot that displays [[training loss]] and [[validation loss]] as a function of the number of [[iterations]]. Iterations would be the x-axis while loss would be the y-axis. | |||
Machine learning strives to build models that accurately predict unseen data. To do this, machine learning models are trained on a dataset consisting of input features and their corresponding target values. Unfortunately, the performance of the model on this training dataset does not guarantee its performance when faced with new information - known as overfitting. To address this issue, evaluation of the model's performance on another dataset called the validation set must take place. | Machine learning strives to build models that accurately predict unseen data. To do this, machine learning models are trained on a dataset consisting of input features and their corresponding target values. Unfortunately, the performance of the model on this training dataset does not guarantee its performance when faced with new information - known as overfitting. To address this issue, evaluation of the model's performance on another dataset called the validation set must take place. | ||