What is an advantage of separate sampling in model building?

Separate sampling in model building is advantageous because it ensures that the model is developed on one dataset while its performance is validated on a different dataset. This process typically leads to a model that can maintain similar predictive power while requiring fewer cases for training. By segmenting the dataset into training and validation parts, you mitigate the risk of overfitting, where the model may perform well on the data it was trained on but poorly on unseen data.

This approach allows for more efficient use of data, as you can obtain a strong model with potentially less information, thereby streamlining the model-building process. It balances the need for robust learning from the training set while ensuring that the validation set effectively tests the model's generalizability. In turn, this can lead to better insights and more efficient resource use in developing predictive analytics.

In contrast, the other choices do not align with the benefits of separate sampling. For instance, creating a model with greater complexity does not necessarily improve predictive accuracy and could actually detract from the model's effectiveness. Requiring an increase in the total number of cases needed is contrary to the efficiency gained through separate sampling. Lastly, while analyzing larger case counts can be beneficial, it is not a direct outcome of separate sampling but rather a feature