A machine learning engineer is tuning a deep neural network and has implemented early stopping to mitigate overfitting. They are concerned about the configuration of the patience hyperparameter. What is the most likely negative consequence of setting the patience parameter to a very low value?
The model will be more prone to overfitting the training data.
The computational cost of training will significantly increase.
It reduces the effectiveness of the optimizer, such as Adam or RMSprop.
The training may terminate prematurely, resulting in an underfit model.
The correct answer is that setting the patience parameter too low may cause training to terminate prematurely, resulting in an underfit model. Early stopping is a regularization technique that halts training when a monitored metric (like validation loss) stops improving. The patience parameter defines how many epochs to wait for an improvement before stopping. A very low value for patience means the training process has little tolerance for temporary plateaus or noisy fluctuations in the validation score. It might stop at the first sign of non-improvement, preventing the model from training long enough to learn the underlying patterns in the data, which leads to underfitting.
A high patience value, not a low one, would allow the model to train longer and potentially overfit. A low patience value would cause training to stop sooner, thereby reducing, not increasing, the computational cost. Early stopping is a training-loop control mechanism and does not directly reduce the mathematical effectiveness of an optimizer's update rule.
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