An Effective Yet Underrated Technique To Improve Model Performance

Robust ML models are driven by diverse training data. Here's a simple yet highly effective technique that can help you create a diverse dataset and increase model performance.

One way to increase data diversity is using data augmentation.

The idea is to create new samples by transforming the available samples. This can prevent overfitting, improve performance, and build robust models.

For images, you can use imgaug (linked in comments). It provides a variety of augmentation techniques such as flipping, rotating, scaling, adding noise to images, and many more.

Find more info: Imgaug.

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Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn.