Supervised models

Mathematical assumptions

At present, MLJ's performance estimate functionality (resampling using evaluate/evaluate!) tacitly assumes that feature-label pairs of observations (X1, y1), (X2, y2), (X2, y2), ... are being modelled as identically independent random variables (i.i.d.), and constructs some kind of representation of an estimate of the conditional probability p(y | X) (y and X single observations). It may be that a model implementing the MLJ interface has the potential to make predictions under weaker assumptions (e.g., time series forecasting models). However the output of the compulsory predict method described below should be the output of the model under the i.i.d assumption.

In the future, newer methods may be introduced to handle weaker assumptions (see, e.g., The predict_joint method below).

The following sections were written with Supervised models in mind, but also cover material relevant to general models:

• Summary of methods
• The form of data for fitting and predicting
• The fit method
• The fitted_params method
• The predict method
• The predict_joint method
• Training losses
• Feature importances
• Trait declarations
• Iterative models and the update! method
• Implementing a data front end
• Supervised models with a transform method
• Models that learn a probability distribution