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The ultimate goal of any BigML resource is making predictions. Now, BigML provides three types of thresholds, one for each of the certainty measures that BigML offers for your classification model predictions: probabilities, confidences, and votes. All classification models (Decision Trees, Ensembles, Deepnets, and Logistic Regressions) return a per-class probability along with the prediction. Thus you can apply a probability threshold for the positive class for any model. Decision trees and non-boosted ensembles predictions also come with a per-class confidence, a pessimistic measure of the model certainty, so you can apply a confidence threshold for these models. Finally, only for non-boosted ensembles, BigML offers another metric called votes that takes into account the percentage of trees in the ensemble to predict a given class. As an alternative to the probability or the confidence, you can also apply a vote threshold for these models. These three metrics will be explained in detail during our webinar.
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