Learn about the key factors that affect the performance of a learner system in machine learning and AI, and why “good data structures” are not included as a major determinant.
Table of Contents
Question
Which of the factors affect the performance of the learner system does not include?
A. Good data structures
B. Representation scheme used
C. Training scenario
D. Type of feedback
Answer
A. Good data structures
Explanation
The performance of a learner system in machine learning is influenced by several critical factors such as:
- Representation Scheme Used: This determines how the data and the problem are presented to the learning algorithm, impacting how well the algorithm can learn patterns.
- Training Scenario: The setup and quality of the training data, including the diversity and relevance of examples, play a pivotal role in system performance.
- Type of Feedback: Feedback mechanisms like supervised learning (labelled data) or reinforcement learning (rewards and penalties) influence learning efficiency and accuracy.
Good data structures, while essential for implementing algorithms efficiently, are not considered a factor directly affecting the learning capability of the system. They are a low-level detail relevant to computational performance rather than learning efficacy.
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