It's the 21st century, and we’ve started relying on machine learning for accurate results more than ever.
But what if machine learning needs a little fixing itself?
Yes, you guessed it right, we’re talking about class imbalance in machine learning.
If you’re someone who is frequently looking for solutions and techniques to handle a class imbalance in machine learning, let us tell you that your quest is over now because we’re about to discuss seven surefire techniques to resolve the class imbalance in machine learning.
But what exactly does it mean?
Class imbalance is a common challenge in machine learning where the distribution of classes in the training dataset is significantly skewed.
This imbalance can adversely affect the performance of machine learning models, particularly in scenarios where the minority class (the class with fewer instances) is of high interest.
Class imbalance occurs when the distribution of instances across different classes is highly disproportionate. As a result, the majority class dominates the dataset, resulting in biased model performance.
Class imbalance can have a negative impact on the way machine learning trains. As a result, some cases and scenarios significantly suffer.
Some real-world examples of areas/domains affected by class imbalance are:
Regarding fraud detection, the amount of fraud transactions becomes negligible to the number of lawful transactions in case of class imbalance.
This makes training the machine learning model impossible because there is insufficient data.
Data imbalance also poorly affects medical diagnoses. For example, if your flawed data is trained to predict every sample for an x-ray as healthy, you’d get an insanely high accuracy rate on test results.
Now one can only imagine the repercussions of it.
Some SaaS businesses use machine learning to predict the number of users who are “active” and the ones who have “churned.” And just like other use cases, a class imbalance will give flawed results.
In a nutshell, no matter what you’re using the machine learning model for, if there’s a class imbalance, the results are not reliable.
Now that we know the problems it can cause, let’s discuss how we should evaluate and fix it.
While training a model on imbalanced data, traditional evaluation metrics such as accuracy can be misleading. Instead, it is recommended to use evaluation metrics that provide a more comprehensive understanding of the model's performance.
Such as precision, recall, F1-score, and area under the Receiver Operating Characteristic (ROC) curve are widely used to assess model performance in imbalanced datasets.
Now you’ve landed on the meaty section of this article. So without further ado, let’s dive in.
The techniques to handle class imbalance can be divided into three main categories which are:
⚫ Data Processing techniques
⚫ Algorithmic techniques
⚫ Advanced techniques
Let’s discuss them one by one;
The following types of data processing techniques can be leveraged:
The resampling methods can be further divided into:
This technique reduces the number of instances in the majority class to match the minority class. Randomly selecting samples or using specific algorithms like Cluster Centroids or NearMiss can help achieve undersampling.
Oversampling aims to increase the number of instances in the minority class. Techniques like Random Oversampling, SMOTE (Synthetic Minority Over-sampling Technique), or ADASYN (Adaptive Synthetic Sampling) generate synthetic instances to balance the dataset.
These methods combine both undersampling and oversampling techniques to create a balanced dataset. Examples include SMOTEENN (SMOTE + Edited Nearest Neighbors) and SMOTETomek (SMOTE + Tomek links).
Generative models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) can generate synthetic data for the minority class, augmenting the training dataset and addressing the class imbalance.
Feature engineering techniques can be applied to extract informative features that help the model better distinguish between classes.
In addition, domain knowledge can be leveraged to create or transform new features, enabling the model to capture the underlying patterns more effectively.
Besides data processing techniques, you can also use algorithmic techniques such as the following;
Assigning different misclassification costs to different classes can help alleviate the impact of class imbalance. By penalizing misclassifications in the minority class more than the majority class, the model can be trained to pay more attention to the minority class.
Ensemble techniques such as Bagging, Boosting (e.g., AdaBoost), and Stacking can improve model performance on imbalanced data. Combining multiple models or assigning higher weights to the minority class during ensemble learning can enhance the model's ability to capture minority class patterns.
This is not it. We’ve yet another technique, i.e., to reveal that might solve your problem.
Furthermore, you can also leverage the following advanced techniques to resolve the imbalance class issue in machine learning.
In specific scenarios where the minority class is poorly defined or difficult to represent, one-class classification techniques like One-Class SVM or Isolation Forest can be employed. These techniques aim to build a model representing only the minority class, distinguishing it from the majority class.
Transfer learning involves leveraging pre-trained models on large and diverse datasets to extract generic features, which can be fine-tuned on imbalanced data. This approach allows the model to benefit from the knowledge acquired during pre-training, potentially improving performance in the minority class.
Class imbalance poses a significant challenge in machine learning. It can affect the performance and reliability of models and, as a result, influence the results we derive from them.
By adopting appropriate techniques and best practices, such as resampling, generating synthetic data, feature engineering, algorithmic modifications, and advanced techniques, it is possible to mitigate the impact of class imbalance and develop robust models.
But the first step to solving any problem is identifying it accurately.
Therefore, understanding the nature of the problem, selecting suitable evaluation metrics, and tailoring the techniques to the specific application are essential steps in handling class imbalance effectively and Data Pilot can help you with that.
With Data Pilot's expertise, we will aid your business with careful consideration and implementation of these strategies with machine learning models to achieve improved performance and accuracy, even in imbalanced datasets.
Written by: Misha Zahid & Rida Ali Khan