adatgazdagítás

Adatgazdagítás, also known as data augmentation, is a technique used in machine learning and data analysis to artificially increase the size and diversity of a dataset. This process involves creating modified versions of existing data to improve the performance and robustness of machine learning models. Data augmentation is particularly useful when the original dataset is small or imbalanced, as it helps to prevent overfitting and enhances the model's ability to generalize from the training data.

There are various methods of data augmentation, depending on the type of data being used. For image data, common techniques include rotation, flipping, cropping, and color adjustment. These transformations create new images that are similar to the original but vary in subtle ways, helping the model to learn invariant features. In text data, augmentation can involve synonym replacement, random insertion, or back-translation using machine translation models. For time-series data, methods like jittering, scaling, and window warping can be employed to generate new sequences.

Data augmentation is widely used in computer vision, natural language processing, and other fields where large amounts of labeled data are required. By artificially expanding the dataset, data augmentation helps to mitigate the challenges associated with data scarcity and imbalance, ultimately leading to more accurate and reliable machine learning models. However, it is important to ensure that the augmented data remains realistic and relevant to the task at hand, as overly aggressive augmentation can introduce noise and degrade model performance.