Have you ever faced a situation where your machine learning model is biased towards one class, simply because your training dataset is unbalanced? I’m sure I’m not the only one who’s been there, done that.
I’m working on a classification project with 5 output categories, but my training dataset is heavily skewed towards one dominant class (class 5, to be exact). As expected, my models always lean towards this class, and I’m struggling to make them learn the characteristics of the other classes.
One way to tackle this issue is by balancing the training dataset. But, it’s not as simple as it sounds. I tried using SMOTETomek for oversampling, but my models didn’t respond well. So, I’m on the hunt for alternative solutions.
The Setup
I’m working with 6 classification ML models, which will eventually be combined into an ensemble. The models include RandomForest, DecisionTree, ExtraTrees, AdaBoost, NaiveBayes, KNN, GradientBoosting, and SVM. I’m also standardizing the data using StandardScaler.
Options for Balancing the Dataset
So, what are my options for balancing the training dataset? Here are a few ideas I’ve gathered so far:
– Undersampling: Reduce the number of instances of the dominant class to match the size of the minority class.
– Oversampling: Increase the number of instances of the minority class to match the size of the dominant class.
– SMOTE: Synthetic Minority Over-sampling Technique, which generates new instances of the minority class based on existing ones.
– Class weighting: Assign different weights to different classes, so that the model pays more attention to the minority class.
– Data augmentation: Apply random transformations to the minority class to increase its size and diversity.
What’s Next?
I’m still exploring these options and would love to hear from others who have faced similar challenges. Do you have any suggestions or success stories to share?
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*Further reading: Handling Imbalanced Datasets in Machine Learning*