Verification and validation are independent procedures that are used together for checking that a product, service, or system meets requirements and specifications and that it fulfills its intended purpose.
These are critical components of a quality management system such as ISO 9000.
By John Paul Mueller, Luca Massaron In a perfect world, you could perform a test on data that your machine learning algorithm has never learned from before.
However, waiting for fresh data isn’t always feasible in terms of time and costs.
In this case, the test split data isn’t a good practice because it causes another kind of overfitting called snooping.
This is not an indication of a security issue such as a virus or attack.
The training set is used to fit the models; the validation set is used to estimate prediction error for model selection; the test set is used for assessment of the generalization error of the final chosen model. The validation set is often used to tune hyper-parameters. Training set: a set of examples used for learning: to fit the parameters of the classifier In the MLP case, we would use the training set to find the “optimal” weights with the back-prop rule Validation set: a set of examples used to tune the parameters of a classifier In the MLP case, we would use the validation set to find the “optimal” number of hidden units or determine a stopping point for the back-propagation algorithm Test set: a set of examples used only to assess the performance of a fully-trained classifier In the MLP case, we would use the test to estimate the error rate after we have chosen the final model (MLP size and actual weights) After assessing the final model on the test set, YOU MUST NOT tune the model any further! The error rate estimate of the final model on validation data will be biased (smaller than the true error rate) since the validation set is used to select the final model After assessing the final model on the test set, YOU MUST NOT tune the model any further!
Ideally, the test set should be kept in a “vault,” and be brought out only at the end of the data analysis. In case if you don't need to choose an appropriate model from several rivaling approaches, you can just re-partition your set that you basically have only training set and test set, without performing the validation of your trained model. For example, in the deep learning community, tuning the network layer size, hidden unit number, regularization term(wether L1 or L2) depends on the validation set What is the correct way to split the sets? @stmax Not to be pedantic, but once we have our final test error and we are NOT satisfied with the result, what do we do, if we cant tune our model any further? I have often wondered about this [email protected] you can continue tuning the model, but you'll have to collect a new test set.
It is a process that is used to evaluate whether a product, service, or system complies with regulations, specifications, or conditions imposed at the start of a development phase.
Verification can be in development, scale-up, or production. Validation is intended to ensure a product, service, or system (or portion thereof, or set thereof) results in a product, service, or system (or portion thereof, or set thereof) that meets the operational needs of the user.