In the process of machine learning, which step involves presenting data in a usable form?

The step that involves presenting data in a usable form is data visualization. This process is essential in machine learning as it transforms raw data into graphical representations, making it easier for analysts and decision-makers to interpret trends, patterns, and insights drawn from the data. Effective data visualization aids in understanding the complexities of the data, allowing practitioners to explore relationships and distributions, ultimately leading to better-informed decisions and improved model performance.

In this context, while data cleansing, standardization, and collection are crucial steps in preparing data for machine learning, they do not focus specifically on presenting data in a format that is visually interpretable. Data cleansing involves correcting or removing erroneous data, standardization refers to adjusting data to a common scale, and data collection is the process of gathering raw data from various sources. Each of these steps prepares the data but does not inherently focus on how to present it visually for analysis and interpretation.