In machine learning, a non-parametric algorithm is a type of algorithm that does not make any assumptions about the underlying distribution of the data. This is in contrast to parametric algorithms, which assume that the data follows a specific distribution, such as a normal distribution.
Non-parametric algorithms are often used when the underlying distribution of the data is unknown or cannot be easily modeled. Instead of making assumptions about the distribution, non-parametric algorithms rely on the data itself to make predictions or decisions. This makes them more flexible and adaptable to a wide range of data types and distributions.
Examples of non-parametric algorithms include K-Nearest Neighbors (KNN), Decision Trees, Random Forests, and Support Vector Machines (SVMs). These algorithms are often used in classification and regression tasks, and can be effective in a wide range of applications. However, they can also be computationally expensive and may require more data to achieve good performance compared to parametric algorithms.