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The bootstrap sample is taken from the original by using sampling with replacement (e.g. we might 'resample' 5 times from [1,2,3,4,5] and get [2,5,4,4,1]), so, assuming N is sufficiently large, for all practical purposes there is virtually zero probability that it will be identical to the original "real" sample. This process is repeated a large ...
When bootstrap aggregating is performed, two independent sets are created. One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement.
The best example of the plug-in principle, the bootstrapping method. Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean, median, proportion, odds ratio ...
In statistics, the bootstrap error-adjusted single-sample technique (BEST or the BEAST) is a non-parametric method that is intended to allow an assessment to be made of the validity of a single sample.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
In statistical hypothesis testing, a type I error, or a false positive, is the erroneous rejection of a true null hypothesis. A type II error, or a false negative, is the erroneous failure in bringing about appropriate rejection of a false null hypothesis. [1]
See also: the {{}} template. The #if function selects one of two alternatives based on the truth value of a test string. {{#if: test string | value if true | value if false}} As explained above, a string is considered true if it contains at least one non-whitespace character.
To input any character, the user simply presses the keys corresponding to the strokes of a character then select from a list of matching characters. The list of suggestions to choose from becomes more and more specific as more digits of the code are entered. [1] The system will not recognize a character input with an incorrect stroke order. [1]