Overfitting is a flaw in machine learning where a model learns the training data too well, including noise, random outliers, and errors that were made in the dataset. This results in a model that looks perfect in training but is unable to adjust to real-world data as it’s asked to, as it’s searching for “familiar” patterns that were present in the training data.
To prevent this issue, developers use techniques that force the model to focus only on the most significant trends. This often includes using multiple sets of data to train a model, ensuring that the model is understanding how to process data as a whole and not simply memorizing one set of training data.
