How do testing datasets differ from training datasets?

Testing datasets evaluate the model, while training datasets build it. This distinction is fundamental in machine learning and predictive analytics. The training dataset is used to train the model by providing it with examples from which it learns patterns, relationships, and features. This learning process adjusts the model’s parameters to minimize error in predictions.

In contrast, the testing dataset is reserved for assessing the performance of the model after it has been trained. By using a separate testing dataset, practitioners can validate how well the model generalizes to unseen data, ensuring that the model's predictive capabilities are robust and not merely a result of overfitting to the training data. This separation is crucial for understanding a model's efficacy in real-world scenarios.

The other options do not accurately represent the relationship between training and testing datasets. For instance, while it's true that testing datasets might contain less data in practice, this is not a defining characteristic that applies universally. Additionally, both datasets serve different purposes rather than both being used for training, and there is no rule that training datasets must always be larger than testing datasets.