Lazy Learning

Lazy learning is a machine learning technique where the model defers the generation of a model until a query is made.

It is used when there is a need to make predictions or classifications based on the most relevant training data at the time of the query. Lazy learning is commonly applied in scenarios such as recommendation systems and personalized services. The technique works by storing all the training data and only generating a model when new data has to be processed, allowing more relevant training data to be weighted more strongly than less relevant training data.

For example, in a recommendation system, lazy learning can be used to provide personalized recommendations based on the most recent user interactions. In a classification task, it can classify new data points by comparing them to the most similar training data points.

Lazy learning is important because it allows for more flexible and adaptive models that can provide more accurate predictions based on the most relevant data. It is a powerful approach in machine learning, enabling models to adapt to new data without the need for retraining.