Summary

Let’s summarize what we’ve learned so far. Imbalanced data is a common problem in machine learning, where there are significantly more instances of one class than another. Imbalanced datasets can arise from various situations, including rare event occurrences, high data collection costs, noisy labels, labeling errors, sampling bias, and data cleaning. This can be a challenge for machine learning models as they may be biased toward the majority class.

Several techniques can be used to deal with imbalanced data, such as oversampling, undersampling, and cost-sensitive learning. The best technique to use depends on the specific problem and the data.

In some cases, data imbalance may not be a concern. When the dataset is sufficiently large, the impact of data imbalance on the model’s performance may be reduced. However, it is still advisable to compare the baseline model’s performance with the performance of models that have been built using techniques that address data imbalance, such as threshold adjustment, data-based techniques (oversampling and undersampling), and algorithm-based techniques.

Traditional performance metrics such as accuracy can fail in imbalanced datasets. Some more useful metrics for imbalanced datasets are the ROC curve, the PR curve, precision, recall, and F1 score. While ROC curves are suitable for balanced datasets, PR curves are more suitable for imbalanced datasets when one class is more important than the other.

The imbalanced-learn library is a Python package that offers several techniques to deal with data imbalance.

There are some general rules to follow, such as splitting the data into train/test/validation sets before applying any techniques to handle the imbalance in the data, ensuring that the validation data closely resembles the test data and that test data represents the data on which the model will make final predictions, and avoiding applying any sampling techniques or modifications to the validation set and test set.

One-class classification or anomaly detection is another technique that can be used for dealing with unsupervised imbalanced data problems. In this book, we will focus our discussion on supervised learning algorithms only.

In the next chapter, we will look at one of the common ways to handle the data imbalance problem in datasets by applying oversampling techniques.