Handling imbalanced text data in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does 'imbalanced text data' mean in machine learning?

Imbalanced text data means some classes or categories have many more examples than others, making it hard for models to learn equally well from all classes.

beginner

Name one simple method to handle imbalanced text data.

One simple method is 'oversampling' the minority class by duplicating its examples to balance the dataset.

intermediate

What is 'undersampling' and when is it used?

Undersampling means reducing the number of examples in the majority class to balance the dataset. It is used when the majority class is very large and can be safely reduced without losing important information.

intermediate

How can synthetic data generation help with imbalanced text data?

Synthetic data generation creates new, artificial examples of the minority class (like using SMOTE) to increase its size and help the model learn better.

beginner

Why is accuracy not a good metric for imbalanced text classification?

Accuracy can be misleading because a model can predict the majority class all the time and still get high accuracy, ignoring the minority class performance.

What is a common problem when training on imbalanced text data?

A The model ignores minority classes

B The model trains faster

C The model always predicts minority classes

D The model requires no preprocessing

Models tend to ignore minority classes because they see fewer examples, leading to poor predictions for those classes.

Which technique involves creating new examples for the minority class?

A Undersampling

B Oversampling

C Feature scaling

D Synthetic data generation

Synthetic data generation creates new artificial examples to increase minority class size.

Why might undersampling be risky?

A It can remove useful data from the majority class

B It increases dataset size

C It duplicates minority class data

D It always improves accuracy

Removing too many majority class examples can lose important information and hurt model performance.

Which metric is better than accuracy for imbalanced text classification?

A F1-score

B Recall

C All of the above

D Precision

Precision, recall, and F1-score give better insight into minority class performance.

What does oversampling do?

A Removes majority class examples

B Duplicates minority class examples

C Creates synthetic majority class data

D Normalizes text data

Oversampling duplicates or adds more examples to the minority class to balance the dataset.

Explain why handling imbalanced text data is important and describe two methods to address it.

Think about how models learn better with balanced examples.

Describe how synthetic data generation can help with imbalanced text data and name a technique used for it.

It’s like making new examples similar to existing minority data.