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  2. Data augmentation - Wikipedia

    en.wikipedia.org/wiki/Data_augmentation

    Data augmentation is a statistical technique which allows maximum likelihood estimation from incomplete data. [1] [2] Data augmentation has important applications in Bayesian analysis, [3] and the technique is widely used in machine learning to reduce overfitting when training machine learning models, [4] achieved by training models on several slightly-modified copies of existing data.

  3. 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]

  4. 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.

  5. Stepwise regression - Wikipedia

    en.wikipedia.org/wiki/Stepwise_regression

    The main approaches for stepwise regression are: Forward selection, which involves starting with no variables in the model, testing the addition of each variable using a chosen model fit criterion, adding the variable (if any) whose inclusion gives the most statistically significant improvement of the fit, and repeating this process until none improves the model to a statistically significant ...

  6. Regularization (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Regularization_(mathematics)

    In machine learning, a key challenge is enabling models to accurately predict outcomes on unseen data, not just on familiar training data. Regularization is crucial for addressing overfitting—where a model memorizes training data details but can't generalize to new data. The goal of regularization is to encourage models to learn the broader ...

  7. Bias–variance tradeoff - Wikipedia

    en.wikipedia.org/wiki/Bias–variance_tradeoff

    It is an often made fallacy [3] [4] to assume that complex models must have high variance. High variance models are "complex" in some sense, but the reverse needs not be true. [5] In addition, one has to be careful how to define complexity. In particular, the number of parameters used to describe the model is a poor measure of complexity.

  8. Roblox - Wikipedia

    en.wikipedia.org/wiki/ROBLOX

    Roblox occasionally hosts real-life and virtual events. They have in the past hosted events such as BloxCon, which was a convention for ordinary players on the platform. [46] Roblox operates annual Easter egg hunts [52] and also hosts an annual event called the "Bloxy Awards", an awards ceremony that also functions as a fundraiser. The 2020 ...

  9. Reduced chi-squared statistic - Wikipedia

    en.wikipedia.org/wiki/Reduced_chi-squared_statistic

    Download QR code; Print/export Download as PDF; ... indicates that the model is "overfitting" the data: either the model is improperly fitting noise, ...