Instance-based learning, also known as lazy learning, is a type of supervised learning algorithm where the model learns by comparing new data instances with training examples that are stored in memory.
The algorithm does not attempt to learn a general model of the data, but rather memorizes the specific examples seen during training. When a new instance is presented, the algorithm looks for the most similar instance(s) in the training set and uses those to make a prediction.
Instance-based learning algorithms are simple and easy to implement, and can be effective when the underlying relationship between the input and output is complex or non-linear. The most commonly used instance-based learning algorithm is the k-nearest neighbors (KNN) algorithm, where the value of k determines the number of nearest neighbors to consider when making a prediction.
One advantage of instance-based learning is that it can handle high-dimensional data and complex decision boundaries, as well as adapt to changes in the underlying data distribution over time. However, it can also be computationally expensive and require a large amount of memory to store the training data.