What is one of the checks involved in achieving classification success?

Achieving classification success often involves the concept of utilizing multiple documents in classification. This approach allows for a more robust model by tempering the potential biases or errors that may arise from relying on a single document. By integrating information from various documents, the model can better understand the context and nuances of the data, which is essential for accurate classification.

Having multiple sources not only enhances the richness of the training data but also provides the model with diverse perspectives on the classification task. This can help in reducing overfitting to any one document's specific characteristics or anomalies. Furthermore, this technique often leads to improved generalization capabilities when the model is applied to new, unseen data, ultimately contributing to higher classification accuracy.

The other options, while pertinent in various contexts, do not directly relate to the classification success in the same manner. For instance, low memory usage pertains more to the efficiency of a model rather than its effectiveness in classification. Validity of source documents deals with the authenticity and reliability of the information being classified, which is important but not a primary check for classification success itself. Temporal consistency is relevant in scenarios where time-based changes in data are considered, yet it does not broadly apply as a check for classification across different contexts.