Difference between revisions of "24F Final Project: Overfitting"
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Overfitting is a phenomenon in machine learning which occurs when a learning [https://en.wikipedia.org/wiki/Algorithm 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.<ref name=”ibm”>https://www.ibm.com/topics/overfitting</ref> 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.<ref name=”ibm” /> | Overfitting is a phenomenon in machine learning which occurs when a learning [https://en.wikipedia.org/wiki/Algorithm 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.<ref name=”ibm”>https://www.ibm.com/topics/overfitting</ref> 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.<ref name=”ibm” /> | ||
Revision as of 07:27, 22 October 2022
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]
