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 probabilistic classifier into a deterministic one.
The positive class is always the second class returned when calling levels
on the training target y
.
MLJModels.BinaryThresholdPredictor
— TypeBinaryThresholdPredictor(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.