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Why is my validation accuracy better than training?
The validation accuracy is greater than training accuracy. This means that the model has generalized fine. If you don’t split your training data properly, your results can result in confusion. so you either have to reevaluate your data splitting method by adding more data, or changing your performance metric.
Why is validation accuracy lower than training?
If your model’s accuracy on your testing data is lower than your training or validation accuracy, it usually indicates that there are meaningful differences between the kind of data you trained the model on and the testing data you’re providing for evaluation.
When can validation accuracy be greater than training accuracy?
If you are using data augmentation to “noisify” your training data, then it can make sense that you are getting better accuracy on the validation set, because it will be an easier dataset. If this is the case, then you don’t really have a problem. As a rule, your validation set should be as close as possible to your test set or real-life use case.
Is the validation set the same as the training set?
It’s meant to be a substitute for the data in the real world that you’re actually interested in classifying. It functions very similarly to the validation set, except you never touched this data while building or tuning your model.
Which is better validation data or training data?
Even though the data is under fitted, the validation data may perform well under circumstances that the validation data fits better in your model than does training data. Increase more convolution layers and loosen up on Dropout layers, lesser dropout layers or lower percentage of units dropped out.
Why is my validation loss lower than my training loss?
Regularization methods often sacrifice training accuracy to improve validation/testing accuracy — in some cases that can lead to your validation loss being lower than your training loss. Secondly, keep in mind that regularization methods such as dropout are not applied at validation/testing time.