Incremental learning is a machine learning approach in which a model updates its knowledge as new data becomes available over time. It is useful when data arrives continuously, changes over time, or is too large to process all at once. The approach must manage risks such as drift, forgetting, and accumulating bias.
A method of machine learning, in which input data is continuously used to extend the existing model's knowledge i.e. to further train the model. It represents a dynamic technique of supervised and unsupervised learning that can be applied when training data becomes available gradually over time or its size is out of system memory limits. Algorithms that can facilitate incremental learning are known as incremental machine learning algorithms.