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  2. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets. The model is initially fit on a training data set, [3] which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. [4]

  3. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    t. e. Machine learning ( ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]

  4. Exploratory testing - Wikipedia

    en.wikipedia.org/wiki/Exploratory_testing

    Exploratory testing is an approach to software testing that is concisely described as simultaneous learning, test design and test execution. Cem Kaner, who coined the term in 1984, defines exploratory testing as "a style of software testing that emphasizes the personal freedom and responsibility of the individual tester to continually optimize the quality of his/her work by treating test ...

  5. Code review - Wikipedia

    en.wikipedia.org/wiki/Code_review

    This definition of code review distinguishes it from related software quality assurance techniques, such as static code analysis, self checks, testing, and pair programming. In static code analysis the main checking is performed by an automated program, in self checks only the author checks the code, in testing the execution of the code is an ...

  6. Software inspection - Wikipedia

    en.wikipedia.org/wiki/Software_inspection

    In addition to helping teams find and fix bugs, code reviews are useful both for cross-training programmers on the code being reviewed and for helping junior developers learn new programming techniques. Peer reviews. Peer reviews are considered an industry best-practice for detecting software defects early and learning about software artifacts.

  7. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events.

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