What defines non-parametric algorithms?

Non-parametric algorithms are distinguished by their lack of assumptions regarding the specific distribution of the data. This characteristic allows them to be more flexible and adaptable to various types of datasets, enabling them to model complex relationships without being constrained by a predefined number of parameters or a specific form of data distribution.

This is particularly advantageous in real-world applications where data may not conform to classic statistical principles, making non-parametric methods a valuable tool in data science.

In contrast, the other aspects mentioned in the options do not accurately capture the essence of non-parametric methods. For instance, the idea that non-parametric algorithms assume a predetermined number of parameters is incorrect, as these methods are not restricted to a fixed set of parameters. Furthermore, non-parametric algorithms do not solely operate on linear data; they can be applied to non-linear datasets as well, thus allowing for a wider range of applications. Lastly, while some data preprocessing is commonly necessary in data science, it is not a defining requirement for non-parametric algorithms—they are generally robust to varying levels of preprocessing.