ML-Schema is a collaborative, community effort with a mission to develop, maintain, and promote standard schemas for data mining and machine learning algorithms, datasets, and experiments
A relation between a run and a task, where the run achieves specifications formulated by the task.
A relation between a task and either the data or an evaluation specification pertinent to this task.
definedOn
A relation between a run and an implemantation that is being executed during the run.
A relation between an implementation of a machine learning algorithm and its hyperparameter.
hasHyperParameter
A relation between a run and data that is taken as input to the run.
hasInput
A relation between a run and either a model or model evaluation that is produced on it’s output.
A relation which represents a part-whole relationship holding between an entity and its part.
hasPart
A relation between entities and their various characteristics.
A relation between an information entity and a specification that it conforms to.
implements
A relation between a run and an algorithm, where the run realizes specifications formulated by the algorithm.
A relation between an entity and the information content entity that specifies it.
The algorithm regardless software implementation.
Data is a data item composed of data examples and it may be of a various level of granularity and complexity. With regard to granularity, it can be a whole dataset (for instance, one main table and possibly other tables), or only a single table, or only a feature (e.g., a column of a table), or only an instance (e.g., row of a table), or a single feature-value pair. With regard to complexity, data examples are characterized by their datatype, which may be arbitrarily complex (e.g., instead of a table it can be an arbitrary graph).
DataCharacteristic is a distinguishing quality or property that distinguish one data from another. Such characteristics are often statistical ones (e.g., the number of instances or the number of features of a data set). They may be also informationtheoretic measures (e.g., class entropy of a categorical data set) or geometric measures of data complexity (e.g., the highest discriminatory power of any single feature in the data set).
EvaluationMeasure is a measure to assess the performance of the model generated by the process that realizes the task. Examples are predictive accuracy or f-measure.
EvaluationProcedure is a technique to evaluate machine learning models. Examples are cross-validation and leave-one-out.
EvaluationSpecification is a specification of an evaluation measure and an evaluation procedure to assess the performance of the process that realizes the task.
Hyperparameter is a prior parameter of an implementation, i.e., a parameter which is set before its execution (e.g. C, the complexity parameter, in weka.SMO).
HyperParameterSetting is an entity which connects a hyperparameter and its value that is being set before an implementation execution.
Implementation is an executable implementation of a machine learning algorithm, a script, or a workflow. It is versioned, and sometimes belongs to a library (e.g. WEKA).
ImplementationCharacteristic is a distinguishing quality or property that distinguish one implementation from another.
Model is a generalization of a set of training data able to predict values for unseen instances. It is an output from an execution of a data mining algorithm implementation. Models have a dual nature. They can be treated as data structures and as such represented, stored and manipulated. On the other hand, they act as functions and are executed, taking as input data examples and giving as output the result of applying the function to a data example. Models can also be divided into global or local ones. A global model has global coverage of a data set, i.e., it generalizes the whole data set. A local model, such as a pattern set, is a set of local hypotheses, i.e. each applies to a limited region of the data set.
ModelCharacteristic is a distinguishing quality or property that distinguish one model from another. An example model characetristic may be interpretabilty or a complexity of the model.
ModelEvaluation is a setting of a value of the performance measure specified by the evaluation specification. It connects a measure specification with its value.
Process
Run is an execution of an implementation on a machine (computer). It is limited in time (has a start and end point), can be successful or failed.
Software is implemented computer programs, procedures, scripts or rules with associated documentation, possibly constituting an organized environment, stored in read/write memory for the purpose of being executed within a computer system.
Study is a collection of runs that belong together to do some kind of analysis on its results. This analysis can be general or very specific (e.g. a hypothesis test). Can be linked to files, data, that belong to it.
Task is a formal description of a process that needs to be completed (e.g. based on inputs and outputs). A Task is any piece of work that needs to be addressed in the data mining process. In ML Schema, it is defined based on data.
Dublin Core Metadata Element Set, Version 1.1
Contributor
A second property with the same name as this property has been declared in the dcterms: namespace (http://purl.org/dc/terms/). See the Introduction to the document "DCMI Metadata Terms" (http://dublincore.org/documents/dcmi-terms/) for an explanation.
An entity responsible for making contributions to the resource.
Examples of a Contributor include a person, an organization, or a service. Typically, the name of a Contributor should be used to indicate the entity.
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant.
Spatial topic and spatial applicability may be a named place or a location specified by its geographic coordinates. Temporal topic may be a named period, date, or date range. A jurisdiction may be a named administrative entity or a geographic place to which the resource applies. Recommended best practice is to use a controlled vocabulary such as the Thesaurus of Geographic Names [TGN]. Where appropriate, named places or time periods can be used in preference to numeric identifiers such as sets of coordinates or date ranges.
Coverage
Creator
An entity primarily responsible for making the resource.
Examples of a Creator include a person, an organization, or a service. Typically, the name of a Creator should be used to indicate the entity.
A point or period of time associated with an event in the lifecycle of the resource.
Date may be used to express temporal information at any level of granularity. Recommended best practice is to use an encoding scheme, such as the W3CDTF profile of ISO 8601 [W3CDTF].
Date
Description may include but is not limited to: an abstract, a table of contents, a graphical representation, or a free-text account of the resource.
An account of the resource.
Description
Format
The file format, physical medium, or dimensions of the resource.
Examples of dimensions include size and duration. Recommended best practice is to use a controlled vocabulary such as the list of Internet Media Types [MIME].
Recommended best practice is to identify the resource by means of a string conforming to a formal identification system.
An unambiguous reference to the resource within a given context.
Identifier
Language
Recommended best practice is to use a controlled vocabulary such as RFC 4646 [RFC4646].
A language of the resource.
Publisher
Examples of a Publisher include a person, an organization, or a service. Typically, the name of a Publisher should be used to indicate the entity.
An entity responsible for making the resource available.
Relation
Recommended best practice is to identify the related resource by means of a string conforming to a formal identification system.
A related resource.
Typically, rights information includes a statement about various property rights associated with the resource, including intellectual property rights.
Rights
Information about rights held in and over the resource.
A related resource from which the described resource is derived.
The described resource may be derived from the related resource in whole or in part. Recommended best practice is to identify the related resource by means of a string conforming to a formal identification system.
Source
Typically, the subject will be represented using keywords, key phrases, or classification codes. Recommended best practice is to use a controlled vocabulary.
Subject
The topic of the resource.
A name given to the resource.
Title
The nature or genre of the resource.
Type
Recommended best practice is to use a controlled vocabulary such as the DCMI Type Vocabulary [DCMITYPE]. To describe the file format, physical medium, or dimensions of the resource, use the Format element.