Data
In the world of machine learning, the data that you have is not used in its entirety to train your model. Instead, you need to separate your data into three sets, as mentioned here:
- A training dataset, which is used to train your model and measure the training loss.
- An evaluation or validation dataset, which you use to measure the validation loss of the model to see whether the validation loss continues to reduce as well as the training loss.
- A test dataset for final testing to see how well the model performs before you put it into production.
The Ratio for Dataset Splits
The evaluation dataset is set aside from your entire training data and is never used for training. There are various schools of thought around the particular ratio that is set aside for evaluation, but it generally ranges from a high of 30% to a low of 10%. This evaluation dataset is normally further split into a validation dataset that is used during training and a test dataset...
