Description: Adaptive models are systems that adjust their parameters based on incoming data, allowing for a dynamic response to changes in the environment or available information. These models are fundamental in the fields of machine learning and artificial intelligence, as they enable algorithms to learn and improve their performance as they are exposed to new data. Adaptability is a key feature, allowing models not only to adjust to existing patterns but also to adapt to new trends or anomalies that may arise. This translates into greater accuracy and effectiveness in tasks such as prediction, classification, and pattern recognition. Adaptive models can be implemented in various architectures, including neural networks, decision trees, clustering algorithms, and recommendation systems, among others. Their ability to learn continuously and improve over time makes them valuable tools in a world where data is increasingly abundant and complex.
