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.
Artificial Intelligence is transforming the way in which companies relate to their customers, how work is managed, the way they work, the way in which [...]
Read More »Achieving business goals and tracking success is an important aspect of improving any business. In sales, measuring the progress of [...]
Read More »ERP stands for Enterprise Resource Planning and is a computerized planning and business management system capable of integrating the information [...]
Read More »The massive implementation of cloud services in companies has transformed the way in which business transactions were carried out, since it has [...]
Read More »
