What is lazy learning algorithm?

Lazy learning algorithms are machine learning algorithms that avoid generalizing beyond the training data during the training phase and instead rely on lazy evaluation during inference. This means that the algorithm does not attempt to learn a general representation of the problem domain, but instead memorizes the entire training dataset to make predictions.

Lazy learning algorithms are often used for tasks like data clustering, classification, and regression. They are particularly useful when the data is too large or complex to be easily processed by traditional, non-lazy algorithms. Examples of lazy learning algorithms include the k-Nearest Neighbor (KNN) algorithm and Locally Weighted Regression (LWR).

The advantages of lazy learning algorithms are that they are easy to implement and can handle complex and changing data sets. However, they can suffer from overfitting when the training data is noisy or imbalanced. Additionally, they are computationally expensive during the inference phase, as they need to search the entire training dataset to make a prediction.