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  2. Wizard (software) - Wikipedia

    en.wikipedia.org/wiki/Wizard_(software)

    A software wizard or setup assistant or multi-step form is a user interface that leads a user through a sequence of small steps, [1] [2] like a dialog box to configure a program for the first time. They are used to make complex, unfamiliar tasks easier by breaking them into smaller pieces.

  3. Double descent - Wikipedia

    en.wikipedia.org/wiki/Double_descent

    This statistics -related article is a stub. You can help Wikipedia by expanding it.

  4. Overfitting - Wikipedia

    en.wikipedia.org/wiki/Overfitting

    In mathematical modeling, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit to additional data or predict future observations reliably". [1] An overfitted model is a mathematical model that contains more parameters than can be justified by the data. [2]

  5. Early stopping - Wikipedia

    en.wikipedia.org/wiki/Early_stopping

    In machine learning, early stopping is a form of regularization used to avoid overfitting when training a model with an iterative method, such as gradient descent. Such methods update the model to make it better fit the training data with each iteration.

  6. Shrinkage (statistics) - Wikipedia

    en.wikipedia.org/wiki/Shrinkage_(statistics)

    This idea is complementary to overfitting and, separately, to the standard adjustment made in the coefficient of determination to compensate for the subjective effects of further sampling, like controlling for the potential of new explanatory terms improving the model by chance: that is, the adjustment formula itself provides "shrinkage." But ...

  7. Bias–variance tradeoff - Wikipedia

    en.wikipedia.org/wiki/Bias–variance_tradeoff

    In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as we increase the number of tunable parameters in a model, it becomes more ...

  8. Bayesian information criterion - Wikipedia

    en.wikipedia.org/wiki/Bayesian_information_criterion

    When fitting models, it is possible to increase the maximum likelihood by adding parameters, but doing so may result in overfitting. Both BIC and AIC attempt to resolve this problem by introducing a penalty term for the number of parameters in the model; the penalty term is larger in BIC than in AIC for sample sizes greater than 7. [1]

  9. Blender (software) - Wikipedia

    en.wikipedia.org/wiki/Blender_(software)

    The Blender ID is a unified login for Blender software and service users, providing a login for Blender Studio, the Blender Store, the Blender Conference, Blender Network, Blender Development Fund, and the Blender Foundation Certified Trainer Program.