Answer: To transform one data frame into another by applying rule-based transformations.

In Spark ML, the primary purpose of a Transformer is to transform one data frame into another by applying rule-based transformations. While Transformers can involve learning during the transformation process, their core function isn't solely about parameter learning like a machine learning model. Fitting is a concept more associated with model training. Transformers can be used within a pipeline along with models, but their primary role is the data manipulation itself. Transformers themselves aren't models, although they can be part of the pipeline that leads to model creation. Their focus is on data preparation and feature engineering. This function is more aligned with data transfer protocols or distributed computing frameworks, not the core functionality of Transformers within Spark ML. Transformers provide essential building blocks for data manipulation in Spark ML pipelines. They allow you to perform various transformations on your data, such as encoding categorical features, scaling numerical features, or creating new features based on existing ones. This prepared data can then be used for training models or other machine learning tasks.

Author: LeetQuiz Editorial Team