Glossary

Note: This glossary includes some detail intended mainly for MLJ developers.

Basics

task (object of type `Task`)

Data plus a learning objective (e.g., "probabilistic prediction of Sales"). In MLJ a task does not include a description of how the completed task is to be evaluated.

Parameters on which some learning algorithm depends, specified before the algorithm is applied, and where learning is interpreted in the broadest sense. For example, PCA feature reduction is a "preprocessing" transformation "learning" a projection from training data, governed by a dimension hyperparameter. Hyperparameters in our sense may specify configuration (eg, number of parallel processes) even when this does not effect the end-product of learning. (But we exlcude verbosity level.)

model (object of abstract type `Model`)

Object collecting together hyperameters of a single algorithm. Most models are classified either as supervised or unsupervised models (generally, "transformers").

fit-result (type generally defined outside of MLJ)

Also known as "learned" or "fitted" parameters, these are "weights", "coefficients", or similar paramaters learned by an algorithm, after adopting the prescribed hyperparameters. For example, decision trees of a random forest, the coefficients and intercept of a linear model, or the rotation and projection matrices of PCA reduction scheme.

operation

Data-manipulating operations (methods) parameterized by some fit-result. For supervised learners, the predict, predict_mean, predict_median, or predict_mode methods; for transformers, the transform or inverse_transform method. An operation may also refert to an ordinary data-manipulating method that does not depend on a fit-result (e.g., a broadcasted logarithm) which is then called static operation for clarity. An operation that is not static is dynamic.

machine (object of type `Machine`)

An object consisting of:

(1) A model

(2) A fit-result (undefined until training)

(3) Training arguments (one for each data argument of the model's associated fit method). A training argument is data used for training. Generally, there are two training arguments for supervised models, and just one for unsuperivsed models.

In addition machines store "report" metadata, for recording algorithm-specific statistics of training (eg, internal estimate of generalization error, feature importances); and they cache information allowing the fit-result to be updated without repeating unnecessary information.

Machines are trained by calls to a fit method which may be passed an optional argument specifying the rows of data to be used in training.

Learning Networks and Composite Models

Note: Multiple nodal machines may share the same model, and multiple learning nodes may share the same nodal machine.

source node (object of type `Source`)

A container for training data and point of entry for new data in a learning network (see below).

nodal machine (object of type `NodalMachine`)

Like a machine with the following exceptions:

(1) Training arguments are source nodes or regular nodes (see below) in the learning network, instead of data.

(2) The object internally records dependencies on other other nodal machines, as implied by the training arguments, and so on.

node (object of type `Node`)

Essentially a nodal machine wrapped in an associated operation (e.g., predict or inverse_transform). It detail, it consists of:

(1) An operation, static or dynamic.

(2) A nodal machine, void if the operation is static.

(3) Upstream connections to other learning or source nodes, specified by a list of arguments (one for each argument of the operation).

(4) Metadata recording the dependencies of the object's machine, and the dependecies on other nodal machines implied by its arguments, and the training arguments of its nodel machine.