Lazy Learning

Lazy learning is a paradigm in machine learning where the algorithm does not construct a general model during the training phase but waits until it receives a query to make predictions or classifications. In this approach, the learning system retains the training dataset, and any generalization or inference process is performed only upon receiving a new query.

This contrasts with eager learning, where the model is built and trained on the dataset before any query is made. The advantage of lazy learning is that it can adapt to new or changing data more easily, as it does not commit to a specific model during the training phase. However, this can also result in slower response times during the query phase, as the system needs to process the entire dataset to respond to each query.

‍

A classic example of a lazy learning algorithm is the k-nearest neighbors (KNN) algorithm. In KNN, when a prediction is required for a new data point, the algorithm searches through the entire training dataset for the k most similar instances (the nearest neighbors) and bases the prediction on the output values of these k instances.

The "laziness" comes from the fact that KNN does not build a general internal model but simply stores the training dataset and performs calculations for each new query. This approach is particularly useful in scenarios where the dataset is constantly changing or when the model needs to be highly adaptable to new data without retraining from scratch.

‍