Non-Parametric Model

Non-parametric machine learning algorithms try to make assumptions about the data given the patterns observed from similar instances. For example, a popular non-parametric machine learning algorithm is the K-Nearest Neighbor algorithm that looks at similar training patterns for new instances. The only assumption it makes about the data set is that the training patterns that are the most similar are most likely to have a similar result. While non-parametric machine learning algorithms are often slower and require large amounts of data, they are rather flexible as they minimize the assumptions they make about the data.