What is important when creating an IBM Watson Visual Recognition classifier?

Providing an approximately equal number of both positive and negative examples of pictures to train with is crucial when creating an IBM Watson Visual Recognition classifier. A balanced dataset helps the model learn to differentiate between classes effectively. If the training data is imbalanced, the model may become biased toward the majority class, leading to poor classification performance and accuracy when it encounters the minority class in real-world scenarios.

For example, if there are significantly more images of cats compared to dogs in the training data, the classifier may learn to identify 'cat' much better than 'dog,' ultimately reducing its reliability. A well-balanced dataset fosters a more robust and generalizable classifier that can recognize various examples across both classes it’s trained on.

In creating classifiers, other options mentioned do have their merits, but they do not address this critical factor of balance in training data, which directly influences the classifier's learning and performance capabilities.