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A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
Kirwan has done some empirical validation on HEART and found that it had "a reasonable level of accuracy" but was not necessarily better or worse than the other techniques in the study. [ 5 ] [ 6 ] [ 7 ] Further theoretical validation is thus required.
This method, also known as Monte Carlo cross-validation, [21] [22] creates multiple random splits of the dataset into training and validation data. [23] For each such split, the model is fit to the training data, and predictive accuracy is assessed using the validation data. The results are then averaged over the splits.
Through the implementation of established and routine quality assurance programs, two primary functions are fulfilled: the determination of quality, and the control of quality. By monitoring the accuracy and precision of results, the quality assurance program should increase confidence in the reliability of the reported analytical results ...
The validity of a measurement tool (for example, a test in education) is the degree to which the tool measures what it claims to measure. [3] Validity is based on the strength of a collection of different types of evidence (e.g. face validity, construct validity, etc.) described in greater detail below.
In qualitative research, a member check, also known as informant feedback or respondent validation, is a technique used by researchers to help improve the accuracy, credibility, validity, and transferability (also known as applicability, internal validity, [1] or fittingness) of a study. [2]
Different approaches are used to do this (e.g., k-Fold cross-validation, leave-one-out cross-validation). The main shortcoming of judging the quality (sensitivity, specificity) of a CAA system based on retrospective performance data alone comes from the risk that the approaches used can overestimate the true performance of a given system.