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Missing not at random (MNAR) (also known as nonignorable nonresponse) is data that is neither MAR nor MCAR (i.e. the value of the variable that's missing is related to the reason it's missing). [5] To extend the previous example, this would occur if men failed to fill in a depression survey because of their level of depression.
A complete list of wrappers may be found on the main web site. [56] Howard Chu ported SQLite 3.7.7.1 to use LMDB instead of its original B-tree code, calling the result SQLightning. [57] One cited insert test of 1000 records was 20 times faster (than the original SQLite with its B-Tree implementation). [58]
Shopify is the name of its proprietary e-commerce platform for online stores and retail POS (point-of-sale) systems. The platform offers retailers a suite of services, including payments, marketing, shipping and customer engagement tools. [2] As of 2024, Shopify hosts 5.6 million active stores across more than 175 countries. [3]
An entity–attribute–value model (EAV) is a data model optimized for the space-efficient storage of sparse—or ad-hoc—property or data values, intended for situations where runtime usage patterns are arbitrary, subject to user variation, or otherwise unforeseeable using a fixed design.
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]
One agile practice, test-driven software development (TDD), is a way of unit testing such that unit-level testing is performed while writing the product code. [69] Test code is updated as new features are added and failure conditions are discovered (bugs fixed).
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Given a classification of a specific data set, there are four basic combinations of actual data category and assigned category: true positives TP (correct positive assignments), true negatives TN (correct negative assignments), false positives FP (incorrect positive assignments), and false negatives FN (incorrect negative assignments).