Class imbalance is a common problem in machine learning, where some classes are overrepresented or underrepresented in the training data. This can lead to poor performance, bias, and unfairness in the models. How can you address class imbalance in machine learning models? Here are some strategies you can try.
One way to deal with class imbalance is to resample the data, either by oversampling the minority class or undersampling the majority class. Oversampling means creating more synthetic or duplicated examples of the minority class, while undersampling means removing some examples of the majority class. This can help balance the distribution of the classes and reduce the impact of the dominant class. However, resampling can also introduce noise, overfitting, or loss of information, so you need to be careful about how you apply it.
Imbalance in machine learning can be due to the sample data having lot of outliers or skewed towards one outcome . For example, in a credit card industry, we are trying to understand factors responsible for 90+ delinquency in a 1000 sample set; here if we get 1 accounts as delinquent and rest 999 as non delinquent ; we say data is skewed towards one outcome. We need to understand the data before forming any models. May be we need to do some filters like removing secured accounts, corporate accounts or any other factors. Imbalance in models may be majorly due to not working on proper data and we know data cleaning is the first crucial step towards forming a model.
One way to handle class imbalance is the use of sampling techniques like SMOTE (Synthetic Minority Over-sampling Technique) which is a popular technique used to handle class imbalance in a dataset by generating synthetic samples for the minority class. Handling class imbalance is important because imbalanced datasets can lead to biased models that favor the majority class. By addressing this issue, you can improve the model's performance and ensure fair predictions for all classes in the dataset. Though, it's not all the time you apply sampling techniques, if the classes value counts are greatly differs especially when you're trying to do classification task under supervised machine learning then you can consider resampling your dataset.
Approaches such as under- or oversampling seek to fix the learning difficulties experienced by many algorithms by changing the priors of the classes directly whereas the within-class distribution is kept the same. Tinkering with the data in such a way can lead to miscalibrated models, where the predicted proportions of classes does not match the actual proportions present in the original data. If the data is "rebalanced" during training, it violates the fundamental assumptions that training and test data are drawn from the same distribution, which will inherently introduce a bias. Class imbalance is not necessarily a problem, but rather a feature of the data.
You can use either upsampling/downsampling to rectify the class imbalance. Upsampling - duplicating instance of minority class. downsampling - removing instances of majority class to balance the class proportions in a dataset.
Use resampling techniques such as oversampling the minority class or under sampling the majority class to balance the class distribution in the training data.
Another way to address class imbalance is to use weighted loss functions, which assign different weights to different classes based on their frequency or importance. Weighted loss functions can penalize the model more for misclassifying the minority class than the majority class, or vice versa, depending on your objective. This can help the model learn from both classes and improve its accuracy and fairness. However, weighted loss functions can also be tricky to tune and may not work well for all types of models or problems.
Weighted loss functions, such as Balanced Cross-Entropy, use a factor such as the inverse class frequency to penalize misclassifications on the minority class. This is a s sort of quick fix and often these function introduce additional hyper-parameters that have to be tuned, which can increase computational load or manual trial-and-error. But, if there is limited data science expertise on a team to develop custom solutions targeting the learning algorithms themselves, Focal Loss can provide satisfactory performance off-the-gate as it pays more attention to hard examples and will produce similar models in the end.
Use weighted loss functions during model training to give more importance to minority class samples. This helps the model learn from the minority class samples more effectively.
A third way to deal with class imbalance is to choose appropriate metrics to evaluate your model. Metrics such as accuracy, precision, or recall can be misleading or biased when the classes are imbalanced, as they can favor the majority class or ignore the minority class. Instead, you should use metrics that account for the imbalance, such as F1-score, ROC-AUC, or Cohen's kappa. These metrics can measure the balance between sensitivity and specificity, or the agreement between the model and the true labels, regardless of the class distribution.
Use metrics that are robust to class imbalance, such as F1 score, precision-recall curve, or ROC-AUC score, to evaluate the performance of the model.
Along with these metrics, even visualizing these performance scores would help to keep a track of how each of the models are behaving with context to the training and test data. It would involve trial and error, where we would have to keep training various ML models, run classification reports and detect the accuracy and MSE scores of each, so that we can judge which model is the best for the data based on how closely they can predict the actual data.
A fourth way to address class imbalance is to try different algorithms that are more robust or adaptable to the imbalance. Some algorithms, such as decision trees, random forests, or gradient boosting, can handle imbalanced data better than others, such as logistic regression, k-nearest neighbors, or support vector machines. This is because they can split the data based on different criteria, create multiple models, or assign different weights to the features or samples. You can also use algorithms that are specifically designed for imbalanced data, such as SMOTE, ADASYN, or RUSBoost.
While resorting to simpler algorithms such as Random Forests or SVM’s might seem promising, it often comes at the cost of performance and might not be suitable for more complex, often highly-dimensional tasks. Newer methods such as Per-Class Normalized Gradient Descent (PCN-GD) in synergy with other approaches directly address the poor learning behavior of Gradient Descent and have best performance. Techniques such as Ensembling (boosting and bagging) can also increase the performance on the minority class but might increase inference time and might not be suitable for applications where low latency is required.
Experiment with different machine learning algorithms that are known to perform well with imbalanced datasets, such as Random Forests, Gradient Boosting Machines, or Support Vector Machines.
A fifth way to deal with class imbalance is to explore the data and understand the reasons and implications of the imbalance. Sometimes, the imbalance may reflect the true nature of the problem or the population, and you may not want to change it. Other times, the imbalance may be caused by errors, biases, or missing values in the data collection or processing, and you may want to fix them. You should also analyze the characteristics and relationships of the classes, and see if there are any patterns, outliers, or confounding factors that affect the model.
Class imbalance is a challenging but important issue in machine learning. By applying these strategies, you can address class imbalance in your models and improve their performance, bias, and fairness.
Take the time to look outside of the data set and immediate analytical context, in deciding how to approach your data. Deliberately step right back and use critical thinking in approaching your analysis. What tools are best practice in research outside the machine learning context? How can these research tools be integrated into machine learning and AI analyses? Your critical thinking is required to equip quality machine learning design. Use your opportunities well.
Thanks for insights Elise, I agree with your views that optimal decisions require counter factual and confrontational ‘meta reflections’ . Only practiced by the mature self aware #pilgrims????
Conduct a thorough analysis of the data to understand the underlying causes of class imbalance and identify potential data preprocessing steps that can help mitigate the imbalance.
Addressing class imbalance in a machine learning model involves several strategies. Firstly, one can use resampling techniques such as oversampling the minority class or undersampling the majority class to rebalance the dataset. Alternatively, one can apply algorithmic approaches such as cost-sensitive learning, where misclassifying minority class instances incurs a higher penalty, or ensemble methods like bagging or boosting, which can effectively handle imbalanced datasets by combining multiple classifiers. Moreover, utilizing appropriate evaluation metrics such as precision, recall, F1-score, or area under the ROC curve (AUC-ROC) is crucial for accurately assessing model performance in the presence of class imbalance.
One thing I do whenever a dataset contains a class or an attribute that is skewed is to employ Stratified Sampling. Stratified Sampling makes sure that all classes are represented in the right proportion, for any portion of your dataset. In order to perform cross validation, you can also employ a Stratified KFold. This splits the dataset into non overlapping k subsets where each subset contains the right proportion of the concerned class. For example, if my dataset contains an attribute `sex` containing 56% male and 44% female, then each subset will also follow this proportion according to the sex attribute.
In addition to the above, it's important to fine-tune the hyperparameters of the model and perform cross-validation to ensure that the model generalizes well to unseen data. Regular monitoring and retraining of the model may also be necessary to maintain its performance over time.