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Is it possible to get high accuracy in the first epoch?
Yes, it is entirely possible to get high accuracy on first epoch and then only modest improvements.
Which is better the validation score or the training score?
For the naive Bayes, both the validation score and the training score converge to a value that is quite low with increasing size of the training set. Thus, we will probably not benefit much from more training data. In contrast, for small amounts of data, the training score of the SVM is much greater than the validation score.
How many epochs should I stop the training to avoid?
If you’re using keras or tensorflow.keras, this parameter is known as patience in the EarlyStopping callback. It equals the number of epochs with no validation accuracy improvement to trigger the end of the training phase. I usually set it to 2 or 3, 1 is usually too sensitive to noise.
Is it bad to use epoch 280 in machine learning?
Although it is right that your model overfits a little since epoch 280, it is not necessarily a bad thing provided that your validation accuracy is high. In general, most machine learning models will have higher training accuracy compared to validation accuracy, but this doesn’t have to be bad.
When does validation accuracy go up or down?
There is no class imbalance, there were approximately 20k samples in each of pos and neg classes. These are the epochs for the training progress, where the validation accuracy flucatuates in a linear fashion. It first gets high and then low by nearly the same amount.
How many images are in validation and train sets?
I’ve divided approximately 41k images into training, validation and test sets in the ratio 70:25:5 giving 29k images in train set, 10k in validation and 2k in test set. There is no class imbalance, there were approximately 20k samples in each of pos and neg classes.
What’s the difference between validation and training accuracy?
Training accuracy increases from ~50% to ~85% in the first epoch, with 85% validation accuracy. Subsequent epochs increase the training accuracy consistently, however, validation accuracy stays in the 80-90% region.
Why does validation loss decrease after first epoch?
After first epoch, my training loss decreases constantly while validation loss increases. Could overfitting happen that soon? Or is there a problem with my data being confusing? Should I also balance my testing set? Balancing the training set makes sense, there is no need to balance the test set.