What process involves placing the values of continuous variables into specific, discrete intervals?

The process of placing the values of continuous variables into specific, discrete intervals is known as data binning. This technique is fundamental in data preprocessing, particularly when preparing data for analysis or when simplifying complex datasets. By dividing continuous data into categories or "bins," it allows for easier interpretation, visualization, and can help to reduce noise and improve the performance of machine learning algorithms.

Binning can assist in creating a clearer understanding of underlying patterns in the data and is especially beneficial when working with large datasets that may contain outliers or are otherwise challenging to interpret in their original continuous form. This method can convert continuous data into categorical data, enabling the application of various statistical methods that require categorical input.

The other processes mentioned serve different purposes. For instance, data encoding involves converting categorical values into a numerical format. Data normalization refers to adjusting the scale of data, often bringing all values into range without changing their distribution. Data transformation encompasses a broader category of techniques that includes both binning and normalization, but it does not specifically focus on creating discrete intervals for continuous variables. Thus, data binning is the most accurate term for the described process.