What are the best practices for imbalanced binary classification problems?

Resampling techniques are like adjusting the weights in a scale to ensure fair measurement. For instance, imagine you're in a classroom where 90% of the students are right-handed and only 10% are left-handed. If you want to understand both groups equally, you might either invite more left-handed people (oversampling) or limit the number of right-handed people in the room (undersampling). This is similar to what we do in machine learning to give equal attention to all categories in a dataset.

It's a tricky one. But here are some tips, use the resampling technique i.e. over-sample the minority class and under-sample the majority class. Secondly, do not solely rely on accuracy as an evaluation metric, use ROC or Balanced Accuracy and F1 score. Lastly, use a robust model, and tune its parameters.

No meu método proposto no doutorado, utilizamos a biblioteca "class weights" do "Sklearn" para dar um peso maior às classes minoritárias. Isso ajudou no aumento da precis?o do modelo.

Weighting samples in your cost function based on how predominant their class is in the data can be essential for ensuring your model has a decent performance on both your classes. On top of this, make sure to be using a balanced metric such as the F1-score, and not plain accuracy, to assess your model's performance. Note that this notion of the "cost" of a sample can be extended to give an even higher (and not equal) value to the minority class - for example, in the case of detecting ilness from medical data, we would probably want our model to be very heavily penalized for failing to identify a positive case! For an easy example on how to achieve this, check sklearn's and keras' models' class_weight arguments.

Oversampling e undersampling. Técnicas possíveis quando nao ha uma base equilibrada. Minha dissertacao do mestrado tb exercitei esses conceitos

Assign different misclassification costs to each class, reflecting their relative importance. This can help the model focus on correctly classifying the minority class.

This is the trickiest. Imagine a spam detection model, roughly only 1% of the emails are spam. It's a lazy model and labels all the emails as "Not spam" The accuracy of this model will be 99% (99% of the emails were not spam and the model labeled them correctly). But the model is useless, it didn't do the job and didn't catch the 1% spam emails. Deciding on which of the other metrics such as recall or precision depends on the use case also. If you are aiming to avoid something bad (who is covid, who has a heart problem, will the bridge collapse etc.) recall helps you be more risk averse and don't miss any case. If you are not sure to distinguish between precision and recall, f1 score is the balanced of the two.

When you're evaluating how well a model works with unbalanced data, keep in mind that the test data should mirror the real-life situations you are trying to model as closely as possible. So, if you're dealing with something rare, its occurrence on the test data should be rare too. This is necessary to prevent biased results. So, if you've used techniques for upsampling or downsampling during training, make sure not to use it in the test data, so it remains “pure”. This guarantees that the model's performance is accurate under the unbalanced conditions. Thus, another imporant step for the correct evaluation is selecting the appropriate metrics. For example, the metrics ROC AUC and PRC AUC are good choice for unbalanced classsification models

Choose a robust algorithm: Select an algorithm that is less sensitive to class imbalance, such as random forests, support vector machines with class weights, or AdaBoost. Ensemble methods: Combine multiple models trained on resampled data to improve overall performance and reduce the impact of class imbalance. Choose appropriate evaluation metrics: Avoid relying solely on accuracy, which can be misleading in imbalanced datasets. Use metrics that consider both classes, such as precision, recall, F1-score, or AUC-ROC. Resample the data: Balance the class distribution by oversampling the minority class, undersampling the majority class, or using a combination of both.

Image or text augmentation doesn't just artificially inflate dataset size. It introduces a level of complexity that trains models to be more adaptive and discerning. This approach not only addresses the immediate issue of imbalance but also contributes to the model's ability to generalize better in real-world scenarios. Essentially, data augmentation is not just filling gaps; it's about creating a rich, varied dataset that pushes the boundaries of what our models can learn and achieve.

AI practitioners have to be very careful when dealing with imbalanced datasets. You might not even be aware of it, it's important to check at the beginning that especially the target variable is balanced or not. Then also during the train-test split, the sampling should be stratified, each sample should keep the original ratio of imbalanced categories. And finally correct evaluation metrics should be used. On top of these, ethical aspects of this situation is that you can trick a non-technical person with the performance of your model by using accuracy or other not sufficient metrics. This is a whole another story.

In binary classification, data imbalance can skew results, favouring the majority class. Best practices to counter this include resampling techniques: oversampling the minority class or under-sampling the majority, to balance the scales. Alternatively, synthetic data generation, creates artificial samples, enriching the dataset without losing valuable information. Adjusting the decision threshold can also be effective, calibrating the model to be more sensitive to the minority class. Focusing on metrics like the F1-score or the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), rather than mere accuracy, gives a more nuanced picture of model performance. These practices ensure fair representation and robust performance.