What is the range of examples required to test a trainable classifier?

The range of examples required to test a trainable classifier is substantial because a larger dataset allows for more accurate training and validation of the model. With 200 to 10,000 examples, it provides a sufficient variety of data points that reflect the nuances and complexities of real-world scenarios. Having this many examples helps ensure that the classifier can learn from a diverse set of instances and generalizes well when presented with new, unseen data.

A smaller dataset may lead to overfitting, where the classifier learns the details of the training examples too well, resulting in poor performance in real-world applications. Conversely, a dataset that is too large would not only be impractical but may also lead to diminishing returns in improvement for the classifier's performance. Thus, the specified range strikes a balance, making it optimal for effective training and reliable results in testing scenarios.