Why is validation error less than training error?

Why is validation error less than training error?

A lower validation than training error can be caused by fluctuations associated with dropout or else, but if it persists in the long run this may indicate that the training and validation datasets were not actually drawn from the same statistical ensembles.

Why is my validation accuracy higher than my training accuracy?

The training loss is higher because you’ve made it artificially harder for the network to give the right answers. However, during validation all of the units are available, so the network has its full computational power – and thus it might perform better than in training.

Why is validation loss smaller than training loss?

Reason #2: Training loss is measured during each epoch while validation loss is measured after each epoch. The second reason you may see validation loss lower than training loss is due to how the loss value are measured and reported: Training loss is measured during each epoch.

What occurs when the gap between the training error and test error is too large?

A large gap between the training error and the test error indicates overfitting of the model, which can usually be remedied by training with more data.

What model would have the lowest training error?

A model that is underfit will have high training and high testing error while an overfit model will have extremely low training error but a high testing error. This graph nicely summarizes the problem of overfitting and underfitting.

How do you read a validation loss?

The loss is calculated on training and validation and its interpretation is based on how well the model is doing in these two sets. It is the sum of errors made for each example in training or validation sets. Loss value implies how poorly or well a model behaves after each iteration of optimization.

What is train set error?

Train set error — 1% and dev set error — 10% means that our model is overfitting train set and not being able to generalise unseen examples. This is called High Variance and can be reduced by introducing regularisation and then training model again.

What’s the difference between validation error and training error?

Your performance on the training data/the training error does not tell you how well your model is overall, but only how well it has learned the training data. The validation error tells you how well your learned model generalises, that means how well it fits to data that it has not been trained on.

Where does the model validation error come from?

Figure 2: The test error comes from using two disjoint datasets: one to train the model and a separate one to calculate the classification error. Calculating any form of error rate for a predictive model is called model validation.

What causes high training error and high test error?

If your training error and test error are both high, your algorithm is not the right choice for the problem. You might be using too simple a model, for example. If the training error is very low and the test error is high, you are over-fitting (you are memorizing too much about the training set).

How to prevent model errors in machine learning?

Since the consequences are often dire, I’m going to discuss how to prevent mistakes in model validation and the necessary components of a correct validation. To kick off the discussion, let’s get grounded in some of the basic concepts of validating machine learning models: predictive modeling, training error, test error and cross validation