Deep-Learning-with-PyTorch

356 CHAPTER 12 Improving training with metrics and augmentation
12.9 Summary
• A binary label and a binary classification threshold combine to partition the
dataset into four quadrants: true positives, true negatives, false negatives, and
false positives. These four quantities provide the basis for our improved performance
metrics.
• Recall is the ability of a model to maximize true positives. Selecting every single
item guarantees perfect recall—because all the correct answers are included—
but also exhibits poor precision.
• Precision is the ability of a model to minimize false positives. Selecting nothing
guarantees perfect precision—because no incorrect answers are included—but
also exhibits poor recall.
• The F1 score combines precision and recall into a single metric that describes
model performance. We use the F1 score to determine what impact changes to
training or the model have on our performance.
• Balancing the training set to have an equal number of positive and negative
samples during training can result in the model performing better (defined as
having a positive, increasing F1 score).
• Data augmentation takes existing organic data samples and modifies them such
that the resulting augmented sample is non-trivially different from the original,
but remains representative of samples of the same class. This allows additional
training without overfitting in situations where data is limited.
• Common data augmentation strategies include changes in orientation, mirroring,
rescaling, shifting by an offset, and adding noise. Depending on the project,
other more specific strategies may also be relevant.