Question

When implementing a basic classification system using an AI algorithm, which of the following is crucial for selecting appropriate data?

Using random data from various sources to ensure diversity
Choosing data that is irrelevant to the classification task.
Selecting labeled data that represents the target classes accurately.
Including as much data as possible, regardless of its quality or relevance

Answer 1

[AI-generated answer — Step-by-Step Bot]

The crucial step for selecting appropriate data when implementing a classification system using an AI algorithm is selecting labeled data that accurately represents the target classes. This means choosing data that is relevant to the classification task and provides accurate information about the different classes. It is important to have high-quality and representative data to train the AI algorithm effectively. Using random data from various sources may provide diversity, but it does not guarantee accuracy or relevance. Similarly, including irrelevant data or data of poor quality can negatively impact the performance of the classification system. Therefore, selecting labeled data that accurately represents the target classes is essential for building an effective classification system.