# Glossary

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

## Basics

### hyper-parameters

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. Hyper-Parameters in our sense may specify configuration (eg, number of parallel processes) even when this does not effect the end-product of learning. (But we exclude verbosity level.)

### model (object of abstract type `Model`

)

Object collecting together hyperameters of a single algorithm. Models are classified either as *supervised* or *unsupervised* models (eg, "transformers"), with corresponding subtypes `Supervised <: Model`

and `Unsupervised <: Model`

.

### 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 hyper-parameters. 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 refer 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 a learning network (see below) the training arguments are nodes, instead of concrete data, but which can be *called* to (lazily) return concrete data.

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 machines in a learning network may share the same model, and multiple learning nodes may share the same 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).

### node (object of type `Node`

)

Essentially a machine (whose arguments are possibly other nodes) wrapped in an associated operation (e.g., `predict`

or `inverse_transform`

). It consists primarily of:

- An operation, static or dynamic.
- A machine, or
`nothing`

if the operation is static. - Upstream connections to other nodes, specified by a list of
*arguments*(one for each argument of the operation). These are the arguments on which the operation "acts" when the node`N`

is called, as in`N()`

.

### learning network

An acyclic directed graph implicit in the connections of a collection of source(s) and nodes.

### wrapper

Any model with one or more other models as hyper-parameters.

### composite model

Any wrapper, or any learning network, "exported" as a model (see Composing Models).