What does overtrain mean?
Overtrain means to train excessively, either as an action performed by someone or as something that happens to a person, animal, or system. The word is especially common in machine learning, where a model may be overtrained by receiving too many updates or being exposed to too many examples. This can cause the model to learn the training data too closely, including irrelevant details, and perform poorly on unfamiliar inputs. Outside computing, overtraining can describe an athlete or animal pushed beyond an appropriate level of practice, and it may also suggest that someone has studied a subject without developing sound understanding. Although “overdo” can sometimes provide a loose synonym, overtrain is more precise because it identifies excessive training as the cause of the problem.
verbTo train someone or something excessively, especially beyond the point of maximum effectiveness. In computing, it often refers to providing a machine-learning model with more training data or processing than needed.
- To train a person, animal, or machine excessively.
- To train a machine-learning model for too long or on too much data, thereby reducing its ability to generalize.
"The researchers stopped the model from overtraining on the small dataset."
"The team adjusted the schedule to avoid overtraining the runners."
"The model began to overtrain once the researchers added many more examples to the dataset."
In machine learning, more training is not always better: a model that studies too long can become an expert on yesterday and a stranger to tomorrow.
Reviewed by Deb Chak, Editor. AI-assisted content curated by RJS Tech Solutions LLP.
Where does overtrain come from?
Overtrain was formed in the 20th century by combining the prefix over- with train, in the sense of teaching or preparing through practice. Its use became especially common in artificial intelligence and machine learning as researchers described models that had been trained beyond their useful capacity. The related noun overtraining names the condition or process itself.
Related word forms
How overtrain is actually used
The term is especially common in computing and machine learning, where it can describe either excessive training overall or the specific problem of a model learning its training data too closely. It can also be used more generally of people, animals, and athletes.
Easily confused with overtrain
Overtraining means training excessively in general, whereas overfitting specifically means that a model has learned the training examples so closely that it performs poorly on new data.