Short Answer 
Derivative classifiers are machine learning algorithms that derive new classifications from the outputs of primary classifiers without needing access to the original training data. They operate independently of the original dataset and focus on leveraging established results to make predictions, highlighted by their ability to function efficiently based solely on previous classifications.
Step 1: Understanding Derivative Classifiers
Derivative classifiers are specialized algorithms or systems in machine learning that utilize the outputs of primary classifiers to make decisions or predictions. They are designed to derive new classifications based on already established results, ensuring they maintain accuracy without needing direct access to the original training dataset.
Step 2: Key Characteristics of Derivative Classifiers
One of the notable features of derivative classifiers is their independence from the original data. They operate exclusively on the results provided by the primary classifiers. Important characteristics include:
- They do not need access to the data that was used to train the primary classifier.
- They are capable of making predictions based on outputs from existing classifiers.
- They efficiently leverage the information already processed by primary classifiers.
Step 3: Correct Answer Explanation
In the context of the question regarding what derivative classifiers do not require, option C emerges as the correct answer. The reasons are clear:
- Derivative classifiers do not require access to the original dataset, as they rely on the existing classifier’s outputs.
- This independence allows them to operate efficiently while ensuring they provide relevant insights.
- The focus is solely on the classifications made, rather than the underlying data used to create those classifications.