9 votes
Accepted

What is "early stopping" in machine learning?

In some iterative learning methods the more iterations you apply the more specific your model becomes about the training set. If there are too many iterations, your model will become too specifically ...
Didami's user avatar
  • 391
3 votes
Accepted

Do different models using early stopping have the same validation set to check model training performance?

Short answer: Yes, the validation set should be the same otherwise you risk that a "lucky" set of validation samples is responsible for better performance. Long answer: A fair comparison of ...
Luca Anzalone's user avatar
3 votes

What is better to use: early stopping, model checkpoint or both?

Early stopping: stop the training when a condition is met Checkpoint : frequently save the model The purpose of Early Stopping is to avoid overfitting by stopping the model before it happens using a ...
malioboro's user avatar
  • 2,819
3 votes
Accepted

What is the difference between TensorFlow's callbacks and early stopping?

Early stopping and callbacks are two different concepts: Early stopping is a machine learning concept about when to stop training your model to avoid overfitting: You monitor a target value (e.g. ...
Feodoran's user avatar
  • 166
2 votes
Accepted

Should I choose the model with highest validation accuracy or the model with highest mean of training and validation accuracy?

Neither of the above mentioned methods could be a potent indicator of the performance of a model. A simple way to train the model just enough so that it generalizes well on unknown datasets would be ...
s_bh's user avatar
  • 360
2 votes

What happens if I train a network for more epochs, without using early stopping?

Training a neural network for "too many" epochs than needed without using early stopping criterion leads to overfitting, where your model's ability to generalize decreases.
Arun's user avatar
  • 225
1 vote
Accepted

What happens if I train a network for more epochs, without using early stopping?

Running for "to many" epochs can indeed lead to over fitting. You should look at the validation loss. If on AVERAGE it continues to decrease then you are not yet over fitting. You may be tempted to ...
Gerry P's user avatar
  • 714
1 vote
Accepted

Should I prefer the model with the lowest validation loss or the highest validation accuracy to deploy?

Okay, I think it's better if we distinguish loss and accuracy first via Jeremy's answer, and I agree with him with the sentence "low or huge loss is a subjective metric". The loss value is ...
CuCaRot's user avatar
  • 912
1 vote

Should I prefer the model with the lowest validation loss or the highest validation accuracy to deploy?

In highly imbalanced classification problems, the highest accuracy can often be achieved simply by assigning the majority class to all observations. This is why learning algorithms do not maximize ...
perenniallydisappointed's user avatar

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