Have you ever struggled with unbalanced training datasets in machine learning? You’re not alone. I’ve been there too, and it’s frustrating when your models lean towards the dominant class, ignoring the characteristics of the other classes.
I recently worked on a classification project with 5 output categories, and my training dataset was heavily skewed. Category 5 had a whopping 47,874 records, while Category 1 had only 160 records. No wonder my models were biased towards Category 5!
To tackle this issue, I knew I had to balance my training dataset. But how? I tried using SMOTETomek for oversampling, but my models didn’t respond well. That’s when I realized I needed to explore other options.
Why Balancing Matters
Balancing your training dataset is crucial because it allows your models to learn the characteristics of all classes, not just the dominant one. This leads to more accurate predictions and better performance.
Options for Balancing
Here are some techniques you can try to balance your training dataset:
- Oversampling: Increase the number of instances in the minority classes by creating new samples. You can use techniques like SMOTE, Random Oversampling, or ADASYN.
- Undersampling: Decrease the number of instances in the majority class. This can be done randomly or using techniques like Tomek Links.
- Class weighting: Assign higher weights to the minority classes during training. This tells the model to pay more attention to these classes.
- Data generation: Create synthetic data to augment the minority classes. This can be done using techniques like Generative Adversarial Networks (GANs).
Combining Models
In my project, I’m using an ensemble of 6 classification models: RandomForest, DecisionTree, ExtraTrees, AdaBoost, NaiveBayes, KNN, GradientBoosting, and SVM. Combining these models can help improve overall performance, but it’s essential to balance the dataset first.
Standardization Matters
Don’t forget to standardize your data using techniques like StandardScaler. This ensures that all features are on the same scale, which is essential for many machine learning algorithms.
Conclusion
Balancing your training dataset is a critical step in machine learning. By trying out different techniques, you can ensure that your models learn the characteristics of all classes, leading to better performance and more accurate predictions.
What’s your favorite technique for balancing unbalanced datasets? Share your experiences in the comments below!