What does high training loss mean?

One of the most widely used metrics combinations is training loss + validation loss over time. The training loss indicates how well the model is fitting the training data, while the validation loss indicates how well the model fits new data.

Should training loss be higher than validation loss?

If your training loss is much lower than validation loss then this means the network might be overfitting . Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on. If your training/validation loss are about equal then your model is underfitting.

What does it mean when validation loss is less 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.

How do you interpret the loss and accuracy of a machine learning model?

Loss value implies how poorly or well a model behaves after each iteration of optimization. An accuracy metric is used to measure the algorithm’s performance in an interpretable way. The accuracy of a model is usually determined after the model parameters and is calculated in the form of a percentage.

What does training loss mean?

Training loss is the error on the training set of data. Validation loss is the error after running the validation set of data through the trained network. Train/valid is the ratio between the two. Unexpectedly, as the epochs increase both validation and training error drop.

Why training set should always be smaller than test set?

Larger test datasets ensure a more accurate calculation of model performance. Training on smaller datasets can be done by sampling techniques such as stratified sampling. It will speed up your training (because you use less data) and make your results more reliable.

Does lower loss indicate higher accuracy?

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. That means: a low accuracy and huge loss means you made huge errors on a lot of data.

What is training loss and training accuracy?

The loss is calculated on training and validation and its interperation is how well the model is doing for these two sets. Unlike accuracy, loss is not a percentage. It is a summation of the errors made for each example in training or validation sets.