Description: Instance-Based Learning (IBL) is an approach within machine learning that focuses on comparing new instances of problems with previously seen instances during the training phase. This method is based on the idea that decisions can be made by identifying similarities between current instances and those that have already been classified or labeled. Unlike other methods that create a general model from the data, IBL stores training instances and uses similarity algorithms to make predictions. This allows the system to be highly flexible and adaptive, as it can adjust to new situations without the need for complete retraining. Key features of IBL include its ability to handle nonlinear data and its resistance to overfitting, as it relies on concrete examples rather than generalizations. This approach is particularly relevant in contexts where data is limited or where relationships between variables are complex and difficult to model. In summary, Instance-Based Learning is a powerful technique that enables artificial intelligence systems to learn more intuitively and directly from specific examples.
