dataaugmentaatio
Data augmentation is a technique used in machine learning and data science to artificially increase the size of a dataset by creating modified versions of existing data. This process is particularly useful when the original dataset is small or imbalanced, as it can help improve the performance and generalization of machine learning models. Data augmentation can be applied to various types of data, including images, text, and audio.
In the context of image data, common augmentation techniques include rotation, scaling, flipping, cropping, and color
The primary goal of data augmentation is to enhance the robustness and accuracy of machine learning models
However, it is important to use data augmentation judiciously. Overly aggressive augmentation can introduce unrealistic data