What can you do with imbalanced datasets?
There are various approaches in ensemble learning such as Bagging, Boosting, etc. Imbalanced data is one of the potential problems in the field of data mining and machine learning. This problem can be approached by properly analyzing the data.
How to balance different subsets of datasets?
When using ensemble classifiers, bagging methods become popular and it works by building multiple estimators on a different randomly selected subset of data. In the scikit-learn library, there is an ensemble classifier named BaggingClassifier. However, this classifier does not allow to balance each subset of data.
Do You Split Your dataset before balancing it?
You should always split your dataset into training and testing sets before balancing the data. That way, you ensure that the test dataset is as unbiased as it can be and reflects a true evaluation for your model.
Why do I get naive results with imbalanced data?
The kind of “naive” results you obtained is due to the imbalanced dataset you are working with. The goal of this article is to review the different methods that can be used to tackle classification problems with imbalanced classes.
Which is the best method for imbalanced classification?
The most popular solution to an imbalanced classification problem is to change the composition of the training dataset. Techniques designed to change the class distribution in the training dataset are generally referred to as sampling methods or resampling methods as we are sampling an existing data sample.
How are sampling methods used to deal with imbalanced data?
The difference between so-called relative and absolute rarity of examples in a minority class. Sampling methods are a very popular method for dealing with imbalanced data. These methods are primarily employed to address the problem with relative rarity but do not address the issue of absolute rarity.