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Learning-test split method

Split sample is a popular resampling method, also referred to as the learning-test split or holdout method (McLachlan, 1992), which entails a single partition of the observed data into a training set and a test set. The size of the two sets is based on a predetermined proportion p for the test set. For example, if p =, this method allots two-thirds of the observed data to the training set and one-third to the test set. Figure 10.5 illustrates this resampling method. [Pg.227]

Most current studies report an internal validation accuracy without an independent validation set. When there are a sufficiently large number of samples, the whole dataset can be split into two, one for training and one for testing (validation) this method is called hold-out validation. When the number of samples is limited, leave-one-out cross validation (LOOCV) is a popular technique. Here, a procedure is repeated N times, and each time a different sample is left out and used for testing the model learned from the remaining (N - 1) samples. The accuracy of... [Pg.420]


See other pages where Learning-test split method is mentioned: [Pg.97]    [Pg.379]    [Pg.43]    [Pg.111]    [Pg.225]    [Pg.83]    [Pg.101]    [Pg.93]    [Pg.99]    [Pg.181]   


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