Data shuffling is an integral preprocessing technique frequently employed to improve model learning. Data shuffling is designed to counter potential issues arising from patterns in the sequential order of training samples, which can lead to overfitting. Furthermore, it can help mitigate the effects of significant imbalances or discrepancies in the distribution of different types of objects or images between the training and validation sets.
When Is Data Shuffling Useful?
Data shuffling proves invaluable when imbalances in the distribution of different image or object types exist across the training and validation sets, particularly when the training set’s distribution inadequately reflects that of the validation set. In the previous lessons, we encountered examples of such an imbalance in color distribution and image brightness distribution. Other examples include cases in which the validation set predominantly contains larger objects, whereas the training set primarily comprises smaller objects, or if specific object classes frequent in the validation set are absent or rare in the training set.
It’s critical to note that the aim of data shuffling isn’t to create identical distributions in the training and validation sets – that would lead to a model fragile to distribution drift with the production/test set. Rather, the objective is to broaden the training distribution to encompass samples similar to those present in the validation set (and hopefully in the production/test set). Data Shuffling and redistribution can often achieve this.
Illustrating Data Shuffling with an Example
Consider a scenario where your initial training and validation sets both contain daytime (high-brightness) and nighttime (low-brightness) images, but the distribution between these categories significantly differs. The ratio of daytime to nighttime images in the training set is 9:1, meaning nighttime images comprise only 10% of your training data. Conversely, in the validation set, the ratio is reversed (1:9), with nighttime images making up 90% of the data.
In this case, if you proceed to train and validate your model with these sets as they are, your model may end up being biased towards daytime images due to their dominance in the training set. This could hinder the model’s robustness, particularly for predicting nighttime images, because it has not been adequately trained on these examples.
If you’re agnostic about the percentage of nighttime to daytime images in your production set, it would be beneficial to create a model that is more robust and well-rounded. This means improving the model’s ability to handle nighttime images, which would require a higher representation of these images in your training set.
To achieve this, you can perform data shuffling in the following way:
Combine the Sets: Merge your training and validation sets into a single, larger set. This set will now have a mixed distribution of daytime and nighttime images.
Shuffle: Randomly shuffle the combined set. This ensures that the subsequent division into a new training and validation set is not biased by the original arrangement of images.
Redistribute: Divide the shuffled dataset back into a training set and a validation set. You can do this by preserving the same total number of images in each set as before, but now, due to the shuffling, both sets should have a more balanced representation of daytime and nighttime images.
This reshuffling process should result in an enhanced training set that includes a higher percentage of nighttime images, thus increasing the potential robustness of your model to handle such images effectively.
While data shuffling is an effective way to mitigate distribution imbalances, there are situations where it might not be viable or appropriate.
Benchmarking: When you’re comparing the performance of different models, or the same model with different hyperparameters, it’s important to keep the training and validation sets constant. This is often the case when you’re using well-established public datasets. Altering the data distribution by shuffling and redistributing might provide better training performance but would invalidate the benchmarking process, as comparisons would no longer be on equal footing.
Extreme Imbalance: In extreme cases of class imbalance (common in medical imaging or anomaly detection), shuffling might not be enough to solve the problem, and more advanced techniques might be needed. We will discuss one such technique – weighted loss function implementation – later on in this course.
Remember, data shuffling should be seen as a tool in your machine learning toolkit. It’s not a one-size-fits-all solution and should be used judiciously, considering the specific requirements and constraints of your project.