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Is the validation accuracy high in Stack Overflow?
– Stack Overflow Validation loss oscillates a lot, validation accuracy > learning accuracy, but test accuracy is high. Is my model overfitting?
How to fix missing items in data validation?
To fix the missing item problem, follow these steps: 1 Select the data validation cells. 2 On the Excel Ribbon’s Data tab, click Data Validation. 3 On the Settings tab, change the range address* in the Source box, to include the new items. 4 Click OK, to complete the change.
What should the drop down be for data validation?
Make Drop Down Temporarily Wider. The Data Validation dropdown is the width of the cell that it’s in, to a minimum of about 3/4″. You could use a SelectionChange event to temporarily widen the column when it’s active, then make it narrower when you select a cell in another column.
Why is my data validation not working in Excel?
In the Data Validation dialog box, you can turn off the option for a dropdown list. To turn it back on: In you have a linked picture in an Excel 2013 workbook, on Window 8, the data validation arrow might not appear in the active cell, unless you are pressing the mouse button.
Why is my validation set not big enough?
It could also just be because your validation set is too small. Increasing the size of it can reduce the variance of the validation accuracy deltas that result from parameter changes, because they’re averaged over more examples.
Is it normal for validation loss to oscillate?
The validation loss at each epoch is usually computed on one minibatch of the validation set, so it is normal for it to be more noisey. Solution: You can report the Exponential Moving Average of the validation loss across different epochs to have less fluctuations.
How is validation loss calculated in machine learning?
At train step, you weigh your loss function based on class-weights, while at dev step you just calculate the un-weighted loss. In such case, though your network is stepping into convergence, you might see lots of fluctuations in validation loss after each train-step.
How is it possible that validation loss should increase?
After some time, validation loss started to increase, whereas validation accuracy is also increasing. The test loss and test accuracy continue to improve. How is this possible? It seems that if validation loss increase, accuracy should decrease.
Why is the validation loss more stable in machine learning?
The reason the validation loss is more stable is that it is a continuous function: It can distinguish that prediction 0.9 for a positive sample is more correct than a prediction 0.51. For accuracy, you round these continuous logit predictions to { 0; 1 } and simply compute the percentage of correct predictions.