{"id":3500,"date":"2025-10-05T00:00:00","date_gmt":"2025-10-04T22:00:00","guid":{"rendered":"https:\/\/tecnologia.euroinnova.com\/overfitting\/"},"modified":"2025-10-07T15:00:09","modified_gmt":"2025-10-07T13:00:09","slug":"overfitting","status":"publish","type":"post","link":"https:\/\/tecnologia.euroinnova.com\/en\/overfitting","title":{"rendered":"Overfitting"},"content":{"rendered":"

The term \u00aboverfitting<\/strong>\u00bbin machine learning refers to a problem that arises when a model fits too well to the training data<\/strong>, This leads to a reduction in their ability to generalise well on new data that have not been seen during the training process.  <\/p>\n

In other words, the model fits very well with the particularities<\/strong> and the noise<\/strong> present in the training data set, but loses the ability to identify meaningful patterns that can be applied to previously unseen data. This concept is also known as \u00aboveradjustment<\/strong>\u00ab. <\/p>\n

Consequences of over-adjustment<\/h2>\n

The over-fitted models<\/strong> often exhibit high accuracy on the training data set, but show poor accuracy on new data, known as the test set or validation set.  <\/p>\n

Overfitting occurs because the model tries to find rules of thumb in the training sample that, in reality, do not exist and, instead, the model tries to find rules of thumb in the training sample that, in reality, do not exist, finds structures and patterns in the noise of the training sample<\/strong>. <\/p>\n

Some signals<\/strong> that indicate that a model may be overtrained are: <\/p>\n