How do you deal with imbalanced class problems?
7 Techniques to Handle Imbalanced Data
- Use the right evaluation metrics.
- Resample the training set.
- Use K-fold Cross-Validation in the right way.
- Ensemble different resampled datasets.
- Resample with different ratios.
- Cluster the abundant class.
- Design your own models.
How to deal with imbalanced classes in a machine?
The simplest implementation of over-sampling is to duplicate random records from the minority class, which can cause overfishing. In under-sampling, the simplest technique involves removing random records from the majority class, which can cause loss of information. Let’s implement this with the credit card fraud detection example.
How to handle imbalanced classification problems [ video ]?
A slight imbalance is often not a concern, and the problem can often be treated like a normal classification predictive modeling problem. A severe imbalance of the classes can be challenging to model and may require the use of specialized techniques. There are some real business problem where we can find severe Imbalance Dataset.
Which is an example of a class imbalance?
When observation in one class is higher than the observation in other classes then there exists a class imbalance. Example: To detect fraudulent credit card transactions. As you can see in the below graph fraudulent transaction is around 400 when compared with non-fraudulent transaction around 90000.
How to improve class imbalance using class weights in?
Here, the model is heavily accurate but not at all serving any value to our problem statement. That is why we will be using f1 score as the evaluation metric. F1 score is nothing but the harmonic mean of precision and recall. However, the evaluation metric is chosen based on the business problem and what type of error we want to reduce.