This tutorial provides a basic demonstration of how Active Learning works by demonstrating a ratio-based (least confidence) sampling strategy that results in lower overall false positive and negative rates when compared to a model trained on the entire dataset. This sampling falls under the domain of uncertanity sampling, in which new datasets are sampled based on the uncertanity that the model outputs for the corresponding label. In our example, we compare our model's false positive and false negative rates and annotate the new data based on their ratio.