# More on Probabilistic Predictors

Although one can call predict_mode on a probabilistic binary classifier to get deterministic predictions, a more flexible strategy is to wrap the model using BinaryThresholdPredictor, as this allows the user to specify the threshold probability for predicting a positive class. This wrapping converts a probablistic classifer into a deterministic one.

The positive class is always the second class returned when calling levels on the training target y.

MLJModels.BinaryThresholdPredictorType
BinaryThresholdPredictor(model; threshold=0.5)

Wrap the Probabilistic model, model, assumed to support binary classification, as a Deterministic model, by applying the specified threshold to the positive class probability. Can also be applied to outlier detection models that predict normalized scores - in the form of appropriate UnivariateFinite distributions - that is, models that subtype AbstractProbabilisticUnsupervisedDetector or AbstractProbabilisticSupervisedDetector.

By convention the positive class is the second class returned by levels(y), where y is the target.

If threshold=0.5 then calling predict on the wrapped model is equivalent to calling predict_mode on the atomic model.