7.12. Supervised Learning Principles: Class Balancing
Supervised learning is a fundamental approach in the field of Machine Learning, where the model is trained on a dataset that includes the desired inputs and outputs. One of the most common challenges faced when training supervised learning models is class imbalance. Class balancing is crucial to ensure that the model does not develop bias towards the majority class and ignore the minority class, which can lead to misleading results and poor performance on unseen data.
In many real data sets, the distribution of classes is not uniform. For example, in a fraud detection dataset, the number of legitimate transactions is much greater than the number of fraudulent transactions. If a model is trained on this dataset without any class balancing treatment, it can simply learn to predict the majority class (legitimate transactions) and still achieve high accuracy, simply because that is the predominant class.
There are several techniques for dealing with class imbalance, and they can be divided into three main categories: resampling methods, algorithm-based methods, and cost-based methods.
Resampling Methods
Resampling methods adjust the distribution of classes in the dataset. They can be divided into two types: oversampling and undersampling.
- Oversampling: This technique involves replicating examples of the minority class to increase their representation in the data set. A popular oversampling approach is SMOTE (Synthetic Minority Over-sampling Technique), which creates synthetic examples of the minority class rather than simply replicating existing ones.
- Undersampling: On the other hand, undersampling involves removing examples from the majority class to reduce its representation. While this can help balance classes, it can also lead to the loss of important information.
It's important to note that both oversampling and undersampling have their drawbacks. Oversampling can increase the risk of overfitting, as the model may end up memorizing replicated examples. Undersampling can discard valuable information that could be crucial for model learning.
Algorithm-Based Methods
Some machine learning algorithms can be tweaked to better deal with class imbalance. For example, decision trees and tree-based algorithms such as Random Forest and Gradient Boosting allow you to weight classes during training, which can help mitigate bias toward the majority class. Another approach is to modify the algorithm so that it focuses more on examples from the minority class during training.
Cost-Based Methods
Cost-based methods assign a higher cost to misclassifying the minority class. The idea is that the model will be penalized more severely for making mistakes in the minority class than in the majority class, encouraging it to pay more attention to the minority class during training.
Regardless of the method chosen, it is crucial to evaluate the model on a test data set that reflects the real distribution of classes. This can be done using evaluation metrics that take class imbalance into account, such as the confusion matrix, precision, recall, F1-score and the area under the ROC (Receiver Operating Characteristic) curve.
In addition, it is important to perform careful analysis of the problem and dataset to understand the nature of class imbalance. In some cases, the minority class may be more important and therefore warrant greater focus during model training. In other cases, it may be more appropriate to collect more data for the minority class if possible.
In summary, class balancing is an essential aspect of supervised learning in Machine Learning. It requires a careful and considered approach, and the choice of balancing technique should be guided by the specific context of the problem at hand. By properly addressing class imbalance, it is possible to develop fairer, more accurate, and more robust models that perform well across all segments of the dataset and provide valuable insights for data-driven decision making.
