How do you handle unbalanced data in Python?

How do you handle unbalanced data in Python?

Overcoming Class Imbalance using SMOTE Techniques

1. Random Under-Sampling.
2. Random Over-Sampling.
3. Random under-sampling with imblearn.
4. Random over-sampling with imblearn.
5. Under-sampling: Tomek links.
6. Synthetic Minority Oversampling Technique (SMOTE)
7. NearMiss.
8. Change the performance metric.

What is unbalanced data?

In this context, unbalanced data refers to classification problems where we have unequal instances for different classes. Having unbalanced data is actually very common in general, but it is especially prevalent when working with disease data where we usually have more healthy control samples than disease cases.

How to deal with an imbalanced dataset?

An imbalanced data can create problems in the classification task. Before delving into the handling of imbalanced data, we should know the issues that an imbalanced dataset can create. We will take an example of a credit card fraud detection problem to understand an imbalanced dataset and how to handle it in a better way.

Which is the best algorithm for imbalanced data?

There are two main types of algorithms that seem to be effective with imbalanced dataset problems. Decision trees seem to perform pretty well with imbalanced datasets. Since they work by coming up with conditions/rules at each stage of splitting, they end up taking both classes into consideration.

Which is an example of an unbalanced dataset?

Even more extreme unbalance is seen with fraud detection, where e.g. most credit card uses are okay and only very few will be fraudulent. In the example I used for my webinar, a breast cancer dataset, we had about twice as many benign than malignant samples.

What are the most common areas of imbalanced data?

The most common areas where you see imbalanced data are classification problems such as spam filtering, fraud detection and medical diagnosis. What makes Imbalanced Data a problem? Almost every dataset has an unequal represe n tation of classes. This isn’t a problem as long as the difference is small.