ducatyb:

A DataSet object can be thought of as some tabular data, whose rows and columns are examples and features respectively. The length of a DataSet object is the number of examples, which equals to the number of its rows. Each column corresponds to one feature.


I disagree with that analysis. Here we are in a situation where we create several training samples from one example by applying some preprocessing, which is generally called feature extraction. In tabular data feature extraction often means adding new columns to the dataset (although it sometimes means removing some), which leads to people often calling the columns features. Here in this example, it means adding more rows, wo calling the rows the features makes perfect sense to me.
We could however add a remark to explain this