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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]
One by one, a set is selected as inner test (validation) set and the l - 1 other sets are combined into the corresponding inner training set. This is repeated for each of the l sets. The inner training sets are used to fit model parameters, while the outer test set is used as a validation set to provide an unbiased evaluation of the model fit.
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
This can be done by interviewing the stakeholders and asking them directly (static testing) or even by releasing prototypes and having the users and stakeholders to assess them (dynamic testing). User input validation: User input (gathered by any peripheral such as a keyboard, bio-metric sensor, etc.) is validated by checking if the input ...
These parameters may be adjusted by optimizing performance on a subset (called a validation set) of the training set, or via cross-validation. Evaluate the accuracy of the learned function. After parameter adjustment and learning, the performance of the resulting function should be measured on a test set that is separate from the training set.
Static testing involves verification, whereas dynamic testing also involves validation. [24] Passive testing means verifying the system's behavior without any interaction with the software product. Contrary to active testing, testers do not provide any test data but look at system logs and traces.
The amount of overfitting can be tested using cross-validation methods, that split the sample into simulated training samples and testing samples. The model is then trained on a training sample and evaluated on the testing sample.
These methods are employed in the training of many iterative machine learning algorithms including neural networks. Prechelt gives the following summary of a naive implementation of holdout-based early stopping as follows: [9] Split the training data into a training set and a validation set, e.g. in a 2-to-1 proportion.