Oversampling with Text Augmentation

We discuss the following two common issues with training data in this lesson:

• Usually, our training datasets contain many examples of no-matches and only a few matches. In machine learning jargon, this is a severe class imbalance between the majority (no-matches) and minority classes (matches).

• The few examples from the minority class do not cover all class-invariant transformations well, which we have seen in similar tasks (prior knowledge). Our model will not generalize well to unseen examples.

Let’s see how text augmentation can help reveal such problems using the following dataset of restaurant records: