How many labels should exist for each mode in the training set according to standards?

In the context of training machine learning models, particularly for AI applications like those developed with UiPath, the number of labels in the training set plays a crucial role in determining the quality and effectiveness of the model. The standard guideline suggests having a balanced number of labels to ensure the model can adequately learn to differentiate between classes and respond appropriately to diverse inputs.

Having 25 labels for each mode in the training set strikes an effective balance between diversity and comprehensiveness. This quantity allows the model to capture enough variation in the data to make informed decisions while preventing overfitting, where the model learns too much from the training data and performs poorly on unseen data.

Additionally, sufficient labels help in creating a robust training dataset that provides varied examples across different scenarios, leading to improved accuracy and effectiveness in real-world applications. While fewer labels may lead to insufficient learning and more labels could complicate the model without adding significant value, 25 serves as a standard that supports effective training.