In this question i would like to explain the difference between data set for example 100% data, ( train with 70% training, 15% test, 15% validation) , that is mean that using 85% of data for taring and after that select 15% from 85% for test ) finally used the 15% ( validation set). to validate the results . Note that the validation data set used only one time for validate but the test data used in training and testing.
The training data is used for training. Validation data is used during training to prevent overfitting.
Test data is not used at all during training. Your system should not have encountered any of the test data during the training phase. Test data is a reserved portion of your data used only to proof that your system is robust enough to work with data it has not seen before in the training.
If your system performs well enough on the test data. We can safely say that the system generalizes well on unseen data.
The training data is used for training. Validation data is used during training to prevent overfitting.
Test data is not used at all during training. Your system should not have encountered any of the test data during the training phase. Test data is a reserved portion of your data used only to proof that your system is robust enough to work with data it has not seen before in the training.
If your system performs well enough on the test data. We can safely say that the system generalizes well on unseen data.
Hi Mohamed,
In addition to Dickson's answer you'll find a good overview together with many references here:
https://machinelearningmastery.com/difference-test-validation-datasets/
And if i got your description right, you are confounding test and validation data (according to the common nomenclature).
Greetings, Sebastian
Using Testing data set, We can build the model in the ANN. Using Validation data set, we can evaluate the efficient of the model. Generally, 50% used for both Testing and Validation data set. If, you want to improve the accuracy of the model, then increase the test data set from 50% to more.
Test set is used for computing test performance and training set for training performance meanwhile validation set is usually extracted from training one and it used for early stopping. (see stopping criteria)
Training data is used to adjust the weights of the network, Validation data (some people call it development test set) is used to help tune the hyper-parameters (network architecture, activation functions, alpha...) and Test (also called final test set) is used after our final model is trained (should not be used to reajust our model nor its parameters) to get an idea of how the model will perform on unknown data.
And also for early stopping.
when the final result appears , we can observe the cause that permits the system to stop . exemple(number of eppochs eached, validation stop,etc.)
The precise definitions of Training Set, Validation Set and Test Set are given below:
Training set: A set of examples used for learning, that is to fit the parameters [i.e., weights] of the classifier.
Validation set: A set of examples used to tune the parameters [i.e., architecture, not weights] of a classifier, for example to choose the number of hidden units in a neural network.
Test set: A set of examples used only to assess the performance of a fully specified classifier.
- Badges
- Science topic
- Similar topics
- Organizational Psychology
- Training