What is the difference between training and testing metrics in neural networks?

1 Training metrics

Training metrics are the measures of how well your neural network fits the data that it is trained on. They are calculated by applying your model to the same data that you used to adjust its weights and biases. Examples of training metrics include accuracy, precision, recall, and loss. Training metrics can help you monitor the progress of your model during the training process, and identify potential issues such as overfitting or underfitting.

2 Testing metrics

Testing metrics are the measures of how well your neural network generalizes to new and unseen data. They are calculated by applying your model to a separate set of data that you did not use for training. Examples of testing metrics include accuracy, precision, recall, and loss. Testing metrics can help you evaluate the performance of your model on the actual problem that you want to solve, and compare it with other models or benchmarks.

3 The gap between training and testing metrics

Ideally, you want your neural network to have high training and testing metrics, meaning that it can both fit the data well and generalize to new situations. However, in practice, there is often a gap between the training and testing metrics, meaning that your model performs better on the training data than on the testing data. This gap is also known as the generalization error or the generalization gap.

5 The solutions for the gap

The gap between the training and testing metrics can be reduced by applying techniques that seek to strike a balance between fitting and generalizing. Regularization, for example, adds constraints or penalties to the model to prevent it from overfitting the training data and learning more general and robust features applicable to the testing data. Cross-validation, on the other hand, splits the data into multiple subsets for training and testing, allowing for more reliable estimation of testing metrics and avoiding overfitting or underfitting. Data augmentation is another technique which increases the size and diversity of data by applying transformations or modifications such as flipping, cropping, or adding noise. This helps the model learn from more varied and realistic scenarios that resemble the testing data.

6 The importance of the gap

The gap between the training and testing metrics is an important indicator of the quality and reliability of your neural network. By measuring and minimizing the gap, you can improve your model's performance and avoid wasting time and resources on ineffective or misleading models. You can also gain more insights and confidence in your model's capabilities and limitations.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?