Imbalanced data is a term used to describe an issue with classification problems in which the classes are not evenly represented.
Both two-class and multi-class classification issues can have a class imbalance problem. The majority of approaches may be used either. Most categorization data sets do not include a precise number of occurrences in each class, although this is usually insignificant.
There are several issues where a class divide is not just frequent but anticipated. Datasets that characterize fraudulent transactions, for example, are unbalanced. The great majority of transactions will fall into the “Not-Fraud” category, with only a small percentage falling into the “Fraud” category.
Customer churn datasets are another example, where the great majority of customers continue with the service but a small proportion cancels.
How to battle imbalance data?
Change the way you measure performance
When working with an unbalanced dataset, accuracy isn’t the best statistic to utilize. We’ve seen how deceptive it is.
When working with unequal classes, some measures have been developed to tell you the truth.
You should consider the following performance measurements, which can provide more information about the model’s accuracy than typical classification accuracy: Recall, Precision, F1 score, and Confusion Matrix.
More information is needed
Even if you think it’s ridiculous, gathering extra data is nearly always disregarded.
Is it possible for you to acquire further information? Consider whether you can acquire more information on your situation for a moment.
A larger dataset might reveal a different, more balanced view of the classes.
More minor class examples may come in later when we look at resampling your dataset.
Experiment with Different Algorithms
On every given task, you should at the very least be spot-checking a range of various sorts of methods.
On the other hand, decision trees typically perform well on unbalanced datasets. Splitting rules based on the class variable used in tree construction might compel both classes to be addressed.
Because ML systems are more fragile than you think. All based on our open-source core.
Open SourceConsider it from a different angle
Unbalanced datasets have their fields of research. They have their own set of algorithms, metrics, and jargon.
Looking at and thinking about your challenge from various angles might lead to the loss of certain ideas. Anomaly detection and change detection are two options to examine.
The identification of anomalies is the discovery of infrequent events. This might be a machine fault detected by vibrations or malicious behavior detected by a program’s sequence of system calls. When compared to regular operations, these occurrences are quite infrequent.
The minor class is now considered the outliers class, which may help you conceive of novel methods to segregate and categorize data.
Change detection is similar to anomaly detection, however, it looks for a change or difference rather than an abnormality. This might be a shift in a user’s behavior as evidenced by use patterns or financial transactions.
Both of these approaches take a more real-time approach to the categorization challenge, which may provide you with fresh ways of thinking about your problem as well as other solutions to attempt.
Because ML systems are more fragile than you think. All based on our open-source core.
Final thoughts
To create accurate and dependable models from unbalanced datasets, you don’t need to be an algorithm genius or a scientist.
Hopefully, one or two of these strategies, such as adjusting your accuracy metric and resampling your dataset, are ones you can grab off the shelf and use right now. Both are quick and will have an immediate impact.
