TransformedTargetModel

TransformedTargetModel(model; transformer=nothing, inverse=nothing, cache=true)

Wrap the supervised or semi-supervised model in a transformation of the target variable.

Here transformer one of the following:

• The Unsupervised model that is to transform the training target. By default (inverse=nothing) the parameters learned by this transformer are also used to inverse-transform the predictions of model, which means transformer must implement the inverse_transform method. If this is not the case, specify inverse=identity to suppress inversion.
• A callable object for transforming the target, such as y -> log.(y). In this case a callable inverse, such as z -> exp.(z), should be specified.

Specify cache=false to prioritize memory over speed, or to guarantee data anonymity.

Specify inverse=identity if model is a probabilistic predictor, as inverse-transforming sample spaces is not supported. Alternatively, replace model with a deterministic model, such as Pipeline(model, y -> mode.(y)).

Examples

A model that normalizes the target before applying ridge regression, with predictions returned on the original scale:

@load RidgeRegressor pkg=MLJLinearModels
model = RidgeRegressor()
tmodel = TransformedTargetModel(model, transformer=Standardizer())

A model that applies a static log transformation to the data, again returning predictions to the original scale:

tmodel2 = TransformedTargetModel(model, transformer=y->log.(y), inverse=z->exp.(y))