Overfitting is a term used in machine learning to describe a model that has been overfitted to the training data, resulting in poor performance on new or unseen data. That is, the model has learned the training data "by heart", rather than capturing the underlying relationships in the data. This can occur when the model is too complex or is trained for too long, leading to an increased ability of the model to fit the training data rather than generalising to new data. Methods to avoid over-fitting include cross-validation, reducing model complexity and adding regularisation.
Hoy, 3 de octubre, hemos estado en los prestigiosos "Premios SCALEUPS B2B organizada por la Fundación Empresa y Sociedad, para hablaros de la Medici [...]
Read More »Data Mining is a process of exploration and analysis of large amounts of data, with the objective of discovering patterns, relationships and trends that can be [...]
Read More »We often wonder what examples of AI we can find in our environment, and the fact is that artificial intelligence is a concept that in English has [...]
Read More »Artificial intelligence is changing the world at breakneck speed and you're probably wondering when it will surpass artificial intelligence in the [...]
Read More »