A data scientist is conducting exploratory data analysis (EDA) on a dataset that contains information about product suppliers. The dataset records the country where each product supplier is located…

Adding a new column with full country names merely provides redundant text data and does not convert the categorical codes into a numerical format suitable for many machine learning models.

Similarity encoding (or embedding) can be used to represent categories in a lower-dimensional space, but it may involve some information loss depending on the specific method and desired dimensionality, and it's generally more complex than one-hot encoding for preserving all distinct categorical information.

Mapping country codes to continent names significantly reduces dimensionality but results in substantial information loss, as the distinct identity of individual countries is no longer preserved, directly violating the 'no information loss' requirement.

One-hot encoding converts categorical variables into a numerical format by creating a new binary column for each unique category. This method ensures that no information is lost from the original categorical data, and for nominal categories, it is often the most straightforward way to numerically represent them without compromising data integrity, resulting in the smallest increase in dimensionality while meeting the 'no information loss' requirement.