What is the minimum number of examples required for validation in communication mining?

What is the minimum number of examples required for validation in communication mining?

• A. 15
• B. 25
• C. 50
• D. 100

Answer: (B)

In the context of communication mining, validation is essential to ensure that the analysis performed on communication data, such as emails, messages, or documents, yields reliable and meaningful insights. The minimum number of examples required for validation reflects a balance between having a representative sample size and the practicalities of data collection and analysis. Having 25 examples as a minimum for validation allows for adequate variability and representation of different communication patterns or themes. This sample size helps in identifying trends, potential outliers, and ensures that the model's performance can be measured effectively against a sufficient range of data. Smaller sample sizes may lead to skewed results or an inability to validate the findings reliably, while significantly larger sizes may be cumbersome to manage. Choosing 25 as the minimum reflects both practical experience in the field and methodologies that encourage statistical significance without being overly burdensome in terms of data requirements and resources.

In the context of communication mining, validation is essential to ensure that the analysis performed on communication data, such as emails, messages, or documents, yields reliable and meaningful insights. The minimum number of examples required for validation reflects a balance between having a representative sample size and the practicalities of data collection and analysis.

Having 25 examples as a minimum for validation allows for adequate variability and representation of different communication patterns or themes. This sample size helps in identifying trends, potential outliers, and ensures that the model's performance can be measured effectively against a sufficient range of data. Smaller sample sizes may lead to skewed results or an inability to validate the findings reliably, while significantly larger sizes may be cumbersome to manage.

Choosing 25 as the minimum reflects both practical experience in the field and methodologies that encourage statistical significance without being overly burdensome in terms of data requirements and resources.