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  2. Bootstrap (front-end framework) - Wikipedia

    en.wikipedia.org/wiki/Bootstrap_(front-end...

    Bootstrap (formerly Twitter Bootstrap) is a free and open-source CSS framework directed at responsive, mobile-first front-end web development. It contains HTML, CSS and (optionally) JavaScript -based design templates for typography, forms, buttons, navigation, and other interface components. As of May 2023, Bootstrap is the 17th most starred ...

  3. Bootstrapping (statistics) - Wikipedia

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

    Bootstrapping (statistics) Bootstrapping is a procedure for estimating the distribution of an estimator by resampling (often with replacement) one's data or a model estimated from the data. [1] Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. [2][3] This technique ...

  4. Bootstrapping - Wikipedia

    en.wikipedia.org/wiki/Bootstrapping

    Artificial intelligence and machine learning. Bootstrapping is a technique used to iteratively improve a classifier 's performance. Typically, multiple classifiers will be trained on different sets of the input data, and on prediction tasks the output of the different classifiers will be combined.

  5. Bootstrap aggregating - Wikipedia

    en.wikipedia.org/wiki/Bootstrap_aggregating

    v. t. e. Bootstrap aggregating, also called bagging (from b ootstrap agg regat ing), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting.

  6. Temporal difference learning - Wikipedia

    en.wikipedia.org/wiki/Temporal_difference_learning

    Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods. [1]

  7. Out-of-bag error - Wikipedia

    en.wikipedia.org/wiki/Out-of-bag_error

    One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement. The out-of-bag set is all data not chosen in the sampling process. When this process is repeated, such as when building a random forest, many bootstrap samples and OOB sets are created. The OOB sets can be aggregated into one dataset, but each ...

  8. Pivotal quantity - Wikipedia

    en.wikipedia.org/wiki/Pivotal_quantity

    Pivotal quantity. In statistics, a pivotal quantity or pivot is a function of observations and unobservable parameters such that the function's probability distribution does not depend on the unknown parameters (including nuisance parameters). [1] A pivot need not be a statistic — the function and its 'value' can depend on the parameters of ...

  9. Pearson correlation coefficient - Wikipedia

    en.wikipedia.org/wiki/Pearson_correlation...

    Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.

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