Data Augmentation in Python: Everything You Need to Know - neptune.ai

In machine learning (ML), if the situation when the model does not generalize well from the training data to unseen data is called overfitting. As you might know, it is one of the trickiest obstacles in applied machine learning. The first step in tackling this problem is to actually know that your model is overfitting. That is where proper cross-validation comes in. After identifying the problem you can prevent it from happening by applying regularization or training with more data. Still, sometimes you might not have additional data to add to your initial dataset. Acquiring and labeling additional data points may also be the wrong path. Of course, in many cases, it will deliver better results, but in terms of work, it is time-consuming and expensive a lot of the time.