Composing Models

Three common ways of combining multiple models together have out-of-the-box implementations in MLJ:

• Linear Pipelines (Pipeline)- for unbranching chains that take the output of one model (e.g., dimension reduction, such as PCA) and make it the input of the next model in the chain (e.g., a classification model, such as EvoTreeClassifier). To include transformations of the target variable in a supervised pipeline model, see Target Transformations.
• Homogeneous Ensembles (EnsembleModel) - for blending the predictions of multiple supervised models all of the same type, but which receive different views of the training data to reduce overall variance. The technique implemented here is known as observation bagging.
• Model Stacking - (Stack) for combining the predictions of a smaller number of models of possibly different types, with the help of an adjudicating model.

Additionally, more complicated model compositions are possible using:

• Learning Networks - "blueprints" for combining models in flexible ways; these are simple transformations of your existing workflows which can be "exported" to define new, stand-alone model types.