AutoML handles imbalanced datasets by implementing several techniques that aim to improve model performance and ensure reliable predictions. An imbalanced dataset occurs when one class significantly outnumbers another, which can lead models to perform poorly on the minority class. AutoML systems typically incorporate strategies like re-sampling, adjustment of class weights, and usage of specialized algorithms that are better suited for dealing with such data discrepancies.
One common approach is re-sampling, which includes both up-sampling the minority class and down-sampling the majority class. Up-sampling involves duplicating instances of the minority class to balance the dataset, giving the model more examples to learn from. Conversely, down-sampling reduces instances of the majority class, allowing the model to focus more on learning about the minority class. AutoML frameworks can often automate these re-sampling processes and help in determining the right balance for a given problem. Some systems also employ synthetic data generation techniques, like SMOTE (Synthetic Minority Over-sampling Technique), which creates artificial data points for the minority class based on the feature space.
Another effective strategy that AutoML employs is adjusting class weights during model training. By assigning higher weights to the minority class and lower weights to the majority class, the model can be incentivized to pay more attention to the minority class during learning. This means that misclassifying a minority instance will incur a greater penalty than misclassifying a majority instance, which can lead to improved classifier performance. Some AutoML tools also offer built-in methods that apply ensemble techniques specifically designed for imbalanced datasets, such as balanced random forests, which can combine the benefits of both sampling and robust model training methodologies, resulting in better overall prediction accuracy for the minority class.