Generalisation refers to the ability of an artificial intelligence or machine learning model to apply its learning to new situations or data that are not in its original training set. In other words, generalisation implies that a model is able to learn general patterns and characteristics of a dataset and apply that knowledge to new data.
Generalisation is a fundamental aspect of machine learning, since the goal of training a model is to enable it to make accurate predictions on data it has never seen before. If a model is only able to make accurate predictions on the data used to train it, it is said to have overfitted or memorised the training set.
The generalisability of a model can be improved by various techniques, such as regularisation, cross-validation, feature selection and collecting more training data. In general, the larger and more diverse the training data set, the better the generalisability of the model.
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 (AI) derives from a series of models or branches that can be used in different areas of people's lives, as well as in different areas of [...]
Read More »Collecting debts, nowadays, is becoming an arduous task for many companies or freelancers. More and more banks, debt collection [...]
Read More »Artificial intelligence is increasingly present in companies and its growth is being applied in practically all sectors. When the end [...]
Read More »
