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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]
Data type validation is customarily carried out on one or more simple data fields. The simplest kind of data type validation verifies that the individual characters provided through user input are consistent with the expected characters of one or more known primitive data types as defined in a programming language or data storage and retrieval ...
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.
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.
FoodData Central is USDA's integrated data system that contains five types of data containing information on food and nutrient profiles: [2] Standard Reference, using earlier approaches to determining nutrient profiles of foods in the marketplace, provides a comprehensive list of values for nutrients and food components that are derived from calculations and analyses.
For example, the French national database [13] provides information, where possible, on minimum and maximum values found from the different data sources used, the number of samples used to determine the selected value, a reference code corresponding to data sources for a given value, and a confidence code characterising the quality of the given ...
In statistics, the jackknife (jackknife cross-validation) is a cross-validation technique and, therefore, a form of resampling. It is especially useful for bias and variance estimation. The jackknife pre-dates other common resampling methods such as the bootstrap .
Sorting a set of unlabelled weights by weight using only a balance scale requires a comparison sort algorithm. A comparison sort is a type of sorting algorithm that only reads the list elements through a single abstract comparison operation (often a "less than or equal to" operator or a three-way comparison) that determines which of two elements should occur first in the final sorted list.