When to not worry about data imbalance

Class imbalance may not always negatively impact performance, and using imbalance-specific methods can sometimes worsen results [5]. Therefore, it’s crucial to accurately assess whether a task is genuinely affected by class imbalance before applying any specialized techniques. One such strategy can be as simple as setting up a baseline model without worrying about class imbalance and observing the model’s performance on various classes using various performance metrics.

Let’s explore scenarios where data imbalance may not be a concern and no corrective measures may be needed:

• When the imbalance is small: If the imbalance in the dataset is relatively small, with the ratio of the minority class to the majority class being only slightly skewed (say 4:5 or 2:3), the impact on the model’s performance may be minimal. In such cases, the model may still perform reasonably well without requiring any special techniques to handle the imbalance.
• When the goal is to predict the majority class: In some cases, the focus may be on predicting the majority class accurately, and the minority class may not be of particular interest. For example, in online ad placement, the focus can be on targeting users (majority class) likely to click on ads to maximize click-through rates and immediate revenue, while less attention is given to users (minority class) who may find ads annoying.
• When the cost of misclassification is nearly equal for both classes: In some applications, the cost of misclassifying a positive class example is not high (that is, false negative). An example is classifying emails as spam or non-spam. It’s totally fine to miss a spam email once in a while and misclassify it as non-spam. In such cases, the impact of misclassification on the performance metrics may be negligible, and the imbalance may not be a concern.
• When the dataset is sufficiently large: Even if the ratio of minority to majority class samples is very low, such as 1:100, and if the dataset is sufficiently large, with a large number of samples in both the minority and majority classes, the impact of data imbalance on the model’s performance may be reduced. With a larger dataset, the model may be able to learn the patterns in the minority class more effectively. However, it would still be advisable to compare the baseline model’s performance with the performance of models that take the data imbalance into account. For example, compare a baseline model to models with threshold adjustment, oversampling, and undersampling (Chapter 2, Oversampling Methods, and Chapter 3, Undersampling Methods), and algorithm-based techniques such as cost-sensitive learning (Chapter 5, Cost-Sensitive Learning).

In the next section, we will become familiar with a library that can be very useful when dealing with imbalanced data. We will train a model on an imbalanced toy dataset and look at some metrics to evaluate the performance of the trained model.