data.py, which recomputes what the entry states: the script block by block · open in ColabDictionary of Applied Machine Learning: 114 machine-learning terms, each with a definition, a figure, citations and a Python demo · All terms · Demos · Start here
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See also data point dataset
Data are representations of information, recorded in a form suitable for storage, communication and processing, and the word carries three technical senses in machine learning (ML). In its general sense, it denotes recorded information in a form suitable for processing by ML methods. As a data point, it is an elementary information-carrying unit; as a dataset, it is an indexed collection of data points. A related regulatory term, personal data, is defined in the general data protection regulation (GDPR) and adopted by the EU AI Act.
data.py, which recomputes what the entry states: the script block by block · open in ColabP-recordA weather station measures the temperature at its location and stores the value $23.4\,^{\circ}\mathrm{C}$ together with a timestamp (see Fig. 1(a)). Such recorded values are data: representations of information, recorded in a form suitable for storage, communication, and processing by machine learning systems (ML systems) (International Organization for Standardization and International Electrotechnical Commission, 2015). Every machine learning (ML) method trains a model on data, and the usefulness of the hypothesis the method learns is limited by the quality and quantity of the available data.
The word data is used in three technical senses, summarized in Fig. 1, and additionally in a legal sense. In its broadest use, data refer to recorded information. This sense applies when it does not matter whether a single data point or a collection of them is meant. Examples are raw sensor measurements such as the temperature reading in Fig. 1(a) and compound terms such as data parallelism, networked data, and data augmentation.
P-datapointAs a formal unit, a data point is the fundamental information-carrying unit (Fig. 1(b)). Its information is contained in two types of properties: features, which are easy to measure or compute, and labels, which are difficult to measure or compute or need human annotation. The formal model of a domain set, a label set and a labeling function that assigns each domain point its label is given by Shalev-Shwartz and Ben-David (2014, Sect. 2.1).
P-datasetAs a formal collection, a dataset
$\mathcal{D} = \{{\mathbf z}^{(1)}, \ldots, {\mathbf z}^{(m)}\}$
is the indexed collection of data points on which model
training and validation are performed
(Fig. 1(c)) (Goodfellow et al., 2016, Sect. 5.1).
The quality and the quantity of data that limit a learned
hypothesis are properties of this collection: the
labels of its data points and their number.
See also: data point, dataset, personal data, general data protection regulation, EU AI Act.
Published version: doi.org/10.1007/978-981-92-2012-0_1-1, in Dictionary of Applied Machine Learning (Springer Nature Singapore). Please cite that version. This page corresponds to version 1 of that entry.
@incollection{dictml_data,
author = {Jung, Alexander},
editor = {Olioumtsevits, Konstantina and Schnoor, Ekkehard},
title = {data},
booktitle = {Dictionary of Applied Machine Learning},
publisher = {Springer Nature Singapore},
year = {2026},
doi = {10.1007/978-981-92-2012-0_1-1},
isbn = {978-981-92-2012-0},
url = {https://doi.org/10.1007/978-981-92-2012-0_1-1}
}