Dictionary · Notes on Measurement

samples for training used to fit a machine learning model

set of activities undertaken in order to achieve a specific goal

(1) activity in which a system or component is executed under specified conditions, the results are observed or recorded, and an evaluation is made of some aspect of the system or component; (2) to conduct an activity as in (1); (3) set of one or more test cases and procedures.

degree to which a user or other stakeholder has confidence that a product or system will behave as intended

operation that, under specified conditions, in a first step, establishes a relation between the quantity values with measurement uncertainties provided by measurement standards and corresponding indications with associated measurement uncertainties and, in a second step, uses this information to establish a relation for obtaining a measurement result from an indication

software or a program that is specific to the solution of an application problem

property or characteristic of an object that can be distinguished quantitatively or qualitatively by human or automated means

A machine learning technique that iteratively combines a set of simple and not very accurate classifiers (referred to as "weak" classifiers) into a classifier with high accuracy (a "strong" classifier) by upweighting the examples that the model is currently misclassifying

computational process that extracts patterns by analysing quantitative data from different perspectives and dimensions, categorizing them, and summarizing potential relationships and impacts

re-interpretable representation of information in a formalized manner suitable for communication, interpretation or processing

task of assigning collected data to target categories or classes.

A fairness metric that checks whether a classifier produces the same result for one individual as it does for another individual who is identical to the first, except with respect to one or more sensitive attributes. Evaluating a classifier for counterfactual fairness is one method for surfacing potential sources of bias in a model

(1) systematic determination of the extent to which an entity meets its specified criteria; (2) action that assesses the value of something

measured quantity value minus a reference quantity value

specific field of knowledge or expertise

(1) quantitative measure of the degree to which a system, component, or process possesses a given attribute; (2) defined measurement method and the measurement scale; c.f., measure in this section above

value of the target variable for a particular item of labelled input data

A fairness metric that checks whether similar individuals are classified similarly

degree to which a product or system (3.38) protects information (3.20) and data (3.11) so that persons or other products or systems have the degree of data access appropriate to their types and levels of authorization

ability of a system to recover operational condition quickly following an incident

The ability to explain or to present an ML model’s reasoning in understandable terms to a human

abstracted information about objects, events, concepts or rules, their relationships and properties, organized for goal-oriented systematic use

target variable assigned to a sample

generic description of a logical organization of operations used in a measurement

primary output of an AI system when provided with input data or information

A model that, taking inspiration from the brain, is composed of layers (at least one of which is hidden) consisting of simple connected units or neurons followed by nonlinearities

(Quantitative) (1) act or process of assigning a number or category to an entity to describe an attribute of that entity; (2) assignment of numbers to objects in a systematic way to represent properties of the object; (3) use of a metric to assign a value (e.g., a number or category) from a scale to an attribute of an entity; (4) set of operations having the object of determining a value of a measure; (5) assignment of values and labels to aspects of software engineering work products, processes, and resources plus the models that are derived from them, whether these models are developed using statistical or other techniques; (6) figure, extent, or amount obtained by measuring

process to determine or to improve the parameters of a machine learning model, based on a machine learning algorithm, by using training data

The process of converting an actual range of values into a standard range of values, typically −1 to +1 or 0 to 1

Values distant from most other values. In machine learning, any of the following are outliers: • Weights with high absolute values • Predicted values relatively far away from the actual values • Input data whose values are more than roughly 3 standard deviations from the mean Outliers often cause problems in model training. Clipping is one way of managing outliers

closeness of agreement between indications or measured quantity values obtained by replicate measurements on the same or similar objects under specified conditions

A metric for classification models that answers the following question: Out of all the possible positive labels, how many did the model correctly identify? That is: Recall = True Positive/( True Positive + false Negative)

freedom from risk which is not tolerable

freedom from intrusion into the private life or affairs of an individual when that intrusion results from undue or illegal gathering and use of data about that individual

property of consistent intended behaviour and results

logical sequence of operations, described generically, used in quantifying an attribute with respect to a specified scale

A metric for classification models. Precision identifies the frequency with which a model was correct when predicting the positive class. That is: Precision = True Positive /(True Positive + False Positive)