Training, validation, and test data sets

In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data.^[1] Such algorithms function by making data-driven predictions or decisions,^[2] through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets.

The model is initially fit on a training data set,

Successively, the fitted model is used to predict the responses for the observations in a second data set called the validation data set.[3] The validation data set provides an unbiased evaluation of a model fit on the training data set while tuning the model's hyperparameters^[5] (e.g. the number of hidden units—layers and layer widths—in a neural network^[4]). Validation data sets can be used for regularization by early stopping (stopping training when the error on the validation data set increases, as this is a sign of over-fitting to the training data set).^[6] This simple procedure is complicated in practice by the fact that the validation data set's error may fluctuate during training, producing multiple local minima. This complication has led to the creation of many ad-hoc rules for deciding when over-fitting has truly begun.^[6]

Finally, the test data set is a data set used to provide an unbiased evaluation of a final model fit on the training data set.^[5] If the data in the test data set has never been used in training (for example in cross-validation), the test data set is also called a holdout data set. The term "validation set" is sometimes used instead of "test set" in some literature (e.g., if the original data set was partitioned into only two subsets, the test set might be referred to as the validation set).^[5]

Deciding the sizes and strategies for data set division in training, test and validation sets is very dependent on the problem and data available.^[7]

Training data set

A training data set is a

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] The goal is to produce a trained (fitted) model that generalizes well to new, unknown data.^[12] The fitted model is evaluated using “new” examples from the held-out data sets (validation and test data sets) to estimate the model’s accuracy in classifying new data.^[5] To reduce the risk of issues such as over-fitting, the examples in the validation and test data sets should not be used to train the model.^[5]

Most approaches that search through training data for empirical relationships tend to

Validation data set

A validation data set is a

The basic process of using a validation data set for model selection (as part of training data set, validation data set, and test data set) is:^[10][14]

Since our goal is to find the network having the best performance on new data, the simplest approach to the comparison of different networks is to evaluate the error function using data which is independent of that used for training. Various networks are trained by minimization of an appropriate error function defined with respect to a training data set. The performance of the networks is then compared by evaluating the error function using an independent validation set, and the network having the smallest error with respect to the validation set is selected. This approach is called the hold out method. Since this procedure can itself lead to some overfitting to the validation set, the performance of the selected network should be confirmed by measuring its performance on a third independent set of data called a test set.

An application of this process is in early stopping, where the candidate models are successive iterations of the same network, and training stops when the error on the validation set grows, choosing the previous model (the one with minimum error).

Test data set

A test data set is a

A test set is therefore a set of examples used only to assess the performance (i.e. generalization) of a fully specified classifier.[9]^[10] To do this, the final model is used to predict classifications of examples in the test set. Those predictions are compared to the examples' true classifications to assess the model's accuracy.^[11]

In a scenario where both validation and test data sets are used, the test data set is typically used to assess the final model that is selected during the validation process. In the case where the original data set is partitioned into two subsets (training and test data sets), the test data set might assess the model only once (e.g., in the

Confusion in terminology

Testing is trying something to find out about it ("To put to the proof; to prove the truth, genuineness, or quality of by experiment" according to the Collaborative International Dictionary of English) and to validate is to prove that something is valid ("To confirm; to render valid" Collaborative International Dictionary of English). With this perspective, the most common use of the terms test set and validation set is the one here described. However, in both industry and academia, they are sometimes used interchanged, by considering that the internal process is testing different models to improve (test set as a development set) and the final model is the one that needs to be validated before real use with an unseen data (validation set). "The literature on machine learning often reverses the meaning of 'validation' and 'test' sets. This is the most blatant example of the terminological confusion that pervades artificial intelligence research."^[16] Nevertheless, the important concept that must be kept is that the final set, whether called test or validation, should only be used in the final experiment.

Cross-validation

In order to get more stable results and use all valuable data for training, a data set can be repeatedly split into several training and a validation data sets. This is known as cross-validation. To confirm the model's performance, an additional test data set held out from cross-validation is normally used.

Causes of error

Omissions in the training of algorithms are a major cause of erroneous outputs.^[17] Types of such omissions include:^[17]

• Particular circumstances or variations were not included.
• Obsolete data
• Ambiguous input information
• Inability to change to new environments
• Inability to request help from a human or another AI system when needed

An example of an omission of particular circumstances is a case where a boy was able to unlock the phone because his mother registered her face under indoor, nighttime lighting, a condition which was not appropriately included in the training of the system.^[17][18]

Usage of relatively irrelevant input can include situations where algorithms use the background rather than the object of interest for object detection, such as being trained by pictures of sheep on grasslands, leading to a risk that a different object will be interpreted as a sheep if located on a grassland.^[17]

See also

• Statistical classification
• List of datasets for machine learning research
• Hierarchical classification

References