What is overfitting in machine learning?

Overfitting in machine learning refers to a scenario where a model has learned the training data to such an extent that it not only captures the underlying patterns but also the noise and outliers present in that data. This excessive fitting leads to a model that performs exceptionally well on the training set but struggles to generalize to new, unseen data, resulting in poor performance when tested outside the training dataset.

In the context of machine learning, it is crucial for models to balance the complexity of the learning algorithm with the ability to generalize. When a model overfits, it effectively memorizes the training examples rather than learning to predict based on the relationships and patterns that might be relevant in broader applications. Thus, while it might exhibit high accuracy on training data, its predictive capabilities on test data will likely suffer because it has not captured the true relationships relevant for inference.

The other options do not accurately describe the concept of overfitting. For instance, describing a model making predictions without any data misrepresents how models function, as they require data to generate predictions. A model that fails to recognize simple patterns or one that cannot learn from data entirely represents issues of underfitting or a lack of learning capability, rather than overfitting. Understanding overfitting