Fill in the blank: The ______________ _______________ is a logical description of a transformation of the dataset.

The term that best fits in the blank is "Preprocessing function." In the context of machine learning, a preprocessing function refers to a systematic method that involves transforming raw data into a format that is suitable for modeling. This transformation could include normalization, encoding categorical variables, handling missing values, or any operation that makes the dataset more effective for training a model.

A preprocessing function defines a series of steps to clean and prepare the data, ensuring that any inefficiencies or biases in the dataset are addressed before the actual modeling begins. By using such functions, machine learning engineers help improve the quality of the input data, which directly affects the performance and accuracy of the machine learning models trained on that data.

The other options are relevant concepts in machine learning but do not accurately capture the complete essence of transforming the dataset. Data Transformation is a broader term that could imply any modification to the data, which is not as specific as a preprocessing function. Model Function typically refers to the logic that dictates how input data is turned into predictions by the trained machine learning model, rather than focusing on data transformation. Feature Descriptor usually denotes attributes or characteristics extracted from data, serving the purpose of providing information about the features employed in modeling, rather than detailing the process of transforming the data itself.