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Bootstrapping is a procedure for estimating the distribution of an estimator by resampling data or a model. Learn the history, approach, advantages, disadvantages and recommendations of bootstrapping in statistics.
A multimodal distribution is a probability distribution with more than one mode, or local peak. Bimodal distributions are common in statistics, mathematics, and natural sciences, and can arise from mixtures of unimodal distributions.
Learn how HTML elements are modeled in browser engines and how the dimensions of those elements are derived from CSS properties. Compare the W3C box model and the Internet Explorer box model, and see the history and workarounds of the box model issue.
Ensemble learning is a technique that combines multiple learning algorithms to improve predictive performance. Learn about the different types of ensemble methods, such as bagging, boosting and stacking, and their applications in machine learning and data mining.
Bootstrap aggregating, or bagging, is a technique that improves the stability and accuracy of machine learning algorithms by generating multiple models from bootstrap samples and averaging or voting their outputs. It is often applied to decision tree methods, but can be used with any type of method.
Learn about different methods of creating new samples based on one observed sample, such as permutation tests, bootstrapping, cross-validation, jackknife and subsampling. Compare their advantages, disadvantages and applications in various fields of statistics.
A zero-inflated model is a statistical model for count data with excess zeros. Learn about the zero-inflated Poisson model, its examples, estimators and related models.
A modal window is a graphical control element that disables user interaction with the main window until it is closed. Learn about the frequent uses, problems and solutions of modal windows in user interface design, and how they differ from modeless windows and modal sheets.