Most machine learning algorithms have several settings that use the behavior of algorithm. These settings are called hyperparameters. The values of hyperparameters adopted by algorithm itself (though design a nested learning procedure where one learning algorithm learns hyperparameters learning algorithm).
The subset guide of hyperparameters the validation set. Typically, one uses about 80% of the training data for training and 20% for validation. Since the validation set to “train” the hyperparameters, the validation set error will underestimate the generalization error, though typically by a smaller amount than the training error. hyperparameter optimization is complete, the generalization error estimated using the test set.
In practice, when test set has been used repeatedly the performance algorithms over , if we consider all the attempts from the scientific community at beating the reported state-of-the-art performance test set, we having optimistic evaluations with the test set . Benchmarks can thus become stale reflect the field performance of a trained system. Thankfully, the community tends on to new (and usually more ambitious and larger) benchmark datasets.
Dividing the dataset into training set and test set problematic if it the test set being small. A littletest set implies statistical uncertainty estimated average test error, making it difficult that algorithm A works better than algorithm B on the given task. When the dataset has thousands of examples or more, not issue. When the dataset small, there are alternative procedures, one to use all of the examples estimation of the mean test error, at of increased computational cost. These procedures have supportedof repeating the training and testing computation on different randomly chosen subsets or splits of dataset. common k-fold cross-validation procedure a partition of the dataset by splitting it into k non-overlapping subsets. The test error may then be estimated by taking test error across k trials. I, the i-th subset of test set, training set. One problem is that there exist no unbiased estimators of the variance of such average error estimators (Bengio and Grandvalet, 2004 ), but approximations are typically used.