24F Final Project: Overfitting
By Thomas Zhang
Overfitting is a phenomenon in machine learning which occurs when a learning algorithm fits too closely (or even exactly) to its training data, resulting in a model that is unable to make accurate predictions on new data.[1] More generally, it means that a machine learning model has learned the training data too well, including noise and random fluctuations, leading to decreased performance when presented with new data. This is a major problem as the ability of machine learning models to make predictions/decisions and classify data has many real-world applications; overfitting interferes with a model’s ability to generalize well to new data, directly affecting its ability to do the classification and prediction tasks it was intended for.[1]
