Data Augmentation for Machine Learning Models
Data augmentation is a technique we use in machine learning to increase the size and diversity of training datasets. Furthermore, it involves applying various transformations to existing data samples to create new ones.
This will improve the performance and generalization of machine learning models. In this article, we will explore the concept of data augmentation, how it works and what are its benefits.
What is data augmentation?
As we mentioned already, it’s a technique we use to increase the amount of traning data. Therefore, we can make more efficient models whenever we’re dealing with a sparse dataset.
We increase the dataset size by applying various transformations to the existing data samples. Furthermore, we need to apply transformations that are relevant to the data type of the dataset.
In case we’re dealing with images, we’re going to use transformations such as rotation, horizontal or vertical flipping, scalling and croping.
If we’re working with audio files, we can add noise to them.
And if we’re working with text, we can introduce variation to it by replacing words with synonyms or changing word order.
Benefits of data augmentation
There are several benefits to using data augmentation in machine learning.
Improves performance
By increasing the size and diversity of the training data, we can improve the performance of machine learning models. In addition, this is particularly useful whenever we have limited access to large amounts of training data.
Generalization
We can help machine learning models generalize better to new data by exposing them to a wider variety of data samples. Furthermore, this can help preventing a common problem of overfitting.
Cost-effective
Whenever we’re dealing with a sparse dataset, this can be a cost effective way of increasing the size. Further, this can be particularly useful for cases where collecting large amounts of data can be expensive or time consuming.
Conclusion
To conclude, data augmentation is a powerful technique in machine learning for improving machine learning models performance, generalization and handling real-world scenarios.
I hope this article helped you gain a better understanding about data augmentation and perhaps even motivate you to learn even more.
