How many pinned examples are typically associated with each child label in an analytics taxonomy?

How many pinned examples are typically associated with each child label in an analytics taxonomy?

• A. 5 - 15 examples
• B. 25 - 75 examples
• C. 50 - 100 examples
• D. 100 - 150 examples

Answer: (B)

In an analytics taxonomy, the practice of associating pinned examples with each child label is critical for ensuring the effectiveness and accuracy of machine learning models. The range of 25 to 75 examples per child label is optimal as it provides sufficient diversity and representation without overwhelming the model. This balance allows the model to learn the nuances of each category while still being general enough to handle variations in real-world data. Pinning a smaller number of examples, such as less than 25, might not provide enough context or variance for the model to learn effectively. Conversely, using too many examples, such as over 75, can lead to diminishing returns, where additional examples may introduce noise rather than clarifying distinctions between categories. This helps maintain a focused training dataset that is both manageable and comprehensive, guiding the model to understand broader patterns while familiarizing it with specific cases within each child label.

In an analytics taxonomy, the practice of associating pinned examples with each child label is critical for ensuring the effectiveness and accuracy of machine learning models. The range of 25 to 75 examples per child label is optimal as it provides sufficient diversity and representation without overwhelming the model. This balance allows the model to learn the nuances of each category while still being general enough to handle variations in real-world data.

Pinning a smaller number of examples, such as less than 25, might not provide enough context or variance for the model to learn effectively. Conversely, using too many examples, such as over 75, can lead to diminishing returns, where additional examples may introduce noise rather than clarifying distinctions between categories. This helps maintain a focused training dataset that is both manageable and comprehensive, guiding the model to understand broader patterns while familiarizing it with specific cases within each child label.