While working on synthetic data generation your model fails to replicate rare event patterns How do you fix this

Question

With the help of a proper code example, can you tell me, While working on synthetic data generation, your model fails to replicate rare event patterns? How do you fix this?

Answer 1

To fix this issue, apply oversampling techniques like SMOTE or use conditional generative models (e.g., GANs or VAEs) that specifically focus on generating rare event patterns.

Here is the code snippet you can refer to:

In the above, we are using the following key points:

• Data Generation: Uses make_classification to create an imbalanced dataset with rare events.
• Train-Test Split: Uses train_test_split with stratify=y to maintain the class ratio.
• SMOTE Application: Uses SMOTE to oversample the minority class.
• Resampling Validation: Prints class distributions before and after applying SMOTE.

Hence, by using SMOTE, we effectively generate synthetic samples for rare events, balancing the dataset and improving model performance on minority classes.