There are problems where a class imbalance is not just common, it is expected. Most classification data sets do not have exactly equal number of instances in each class, but a small difference often does not matter. The remaining discussions will assume a two-class classification problem because it is easier to think about and describe. You can have a class imbalance problem on two-class classification problems as well as multi-class classification problems. This is an imbalanced dataset and the ratio of Class-1 to Class-2 instances is 80:20 or more concisely 4:1. A total of 80 instances are labeled with Class-1 and the remaining 20 instances are labeled with Class-2. Imbalanced data typically refers to a problem with classification problems where the classes are not represented equally.įor example, you may have a 2-class (binary) classification problem with 100 instances (rows). It is possible, you can build predictive models for imbalanced data. Relax, there are many options and we’re going to go through them all. The next wave of frustration hits when the books, articles and blog posts don’t seem to give you good advice about handling the imbalance in your data. You feel very frustrated when you discovered that your data has imbalanced classes and that all of the great results you thought you were getting turn out to be a lie. Imbalanced data can cause you a lot of frustration. Perhaps one of your upcoming blog posts could address the problem of training a model to perform against highly imbalanced data, and outline some techniques and expectations. I finally took the advice of one of my students:
I write long lists of techniques to try and think about the best ways to get past this problem. Can you please suggest how can I solve this problem? Most of time my results are overfit to A. But in the training dataset I have A dataset with 70% volume, B with 25% and C with 5%. In my dataset I have three different labels to be classified, let them be A, B and C. I used the logistic regression and the result seems to just ignores one class. I have a binary classification problem and one class is present with 60:1 ratio in my training set. I get emails about class imbalance all the time, for example: Find some balance in your machine learning.
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