Contents
What happens when the accuracy of a model decreases?
If the loss decreases, the training process is going well. The (validation I suppose) accuracy, instead, it’s a measure of how good the predictions of your model are. If the model is learning, the accuracy increases. If the model is overfitting, instead, the accuracy stops to increase and can even start to decrease.
What happens if you optimize for accuracy instead of loss?
If done in that way, wouldn’t the model give us even higher accuracy, since it knows it has to maximize it during it’s training?
Is there a way to minimize the loss function?
Your optimization process is just minimizing the loss function, and cannot do better than a model that predicts uninteresting regardless of the input, due to the fact that your training set is very imbalanced. Moreover, you are not overfitting, since your training accuracy is lower than your validation accuracy.
How to improve accuracy and loss in machine learning?
In order to have a model that learns something less dummy than your model (and you might have to pay the price of having a lower accuracy), I would do the following: when providing a mini-batch to your optimizer, generate a mini-batch that is more balanced, that is, bias the elements you select towards the interesting articles.
When does validation loss increase and accuracy decrease?
Training acc increases and loss decreases as expected. But validation loss and validation acc decrease straight after the 2nd epoch itself. The overall testing after training gives an accuracy around 60s. The total accuracy is : 0.6046845041714888
What is the relationship between the accuracy and the loss?
Loss can be seen as a distance between the true values of the problem and the values predicted by the model. Greater the loss is, more huge is the errors you made on the data. Accuracy can be seen as the number of error you made on the data. your situation: a great accuracy but a huge loss, means you made huge errors on a few data.
What are the results of the testing model?
The results of the testing model as the following: First Model: Accuracy: 98.1% Loss: 0.1882 Second Model: Accuracy: 98.5% Loss: 0.0997 Third Model: Accuracy: 99.1% Loss: 0.2544 What is the relationship between the loss and accuracy values?