[data] why use the train, valid, test set to create model?

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!

Why separate test and validation sets? 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!

source : Introduction to Pattern Analysis,Ricardo Gutierrez-OsunaTexas A&M University, Texas A&M University

 

++Training set (60% of the original data set): This is used to build up our prediction algorithm. Our algorithm tries to tune itself to the quirks of the training data sets. In this phase we usually create multiple algorithms in order to compare their performances during the Cross-Validation Phase.

++Cross-Validation set (20% of the original data set): This data set is used to compare the performances of the prediction algorithms that were created based on the training set. We choose the algorithm that has the best performance.

++Test set (20% of the original data set): Now we have chosen our preferred prediction algorithm but we don't know yet how it's going to perform on completely unseen real-world data. So, we apply our chosen prediction algorithm on our test set in order to see how it's going to perform so we can have an idea about our algorithm's performance on unseen data.

Notes:

-It's very important to keep in mind that skipping the test phase is not recommended, because the algorithm that performed well during the cross-validation phase doesn't really mean that it's truly the best one, because the algorithms are compared based on the cross-validation set and its quirks and noises...

-During the Test Phase, the purpose is to see how our final model is going to deal in the wild, so in case its performance is very poor we should repeat the whole process starting from the Training Phase.

 

At each step that you are asked to make a decision (i.e. choose one option among several options), you must have an additional set/partition to gauge the accuracy of your choice so that you do not simply pick the most favorable result of randomness and mistake the tail-end of the distribution for the center ¹. The left is the pessimist. The right is the optimist. The center is the pragmatist. Be the pragmatist.

Step 1) Training: Each type of algorithm has its own parameter options (the number of layers in a Neural Network, the number of trees in a Random Forest, etc). For each of your algorithms, you must pick one option. That’s why you have a training set.

Step 2) Validating: You now have a collection of algorithms. You must pick one algorithm. That’s why you have a test set. Most people pick the algorithm that performs best on the validation set (and that's ok). But, if you do not measure your top-performing algorithm’s error rate on the test set, and just go with its error rate on the validation set, then you have blindly mistaken the “best possible scenario” for the “most likely scenario.” That's a recipe for disaster.

Step 3) Testing: I suppose that if your algorithms did not have any parameters then you would not need a third step. In that case, your validation step would be your test step. Perhaps Matlab does not ask you for parameters or you have chosen not to use them and that is the source of your confusion.

¹ It is often helpful to go into each step with the assumption (null hypothesis) that all options are the same (e.g. all parameters are the same or all algorithms are the same), hence my reference to the distribution.

² This image is not my own. I have taken it from this site:

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Reference

http://stats.stackexchange.com/questions/19048/what-is-the-difference-between-test-set-and-validation-set