Training,testing and validation

#1

i know what is training of the model,but i am confused between validation and testing of the model. Can you tell the difference between the two.

#2

Hey @Rajan

Below are listed the difference between the three :

Training set: a set of examples used for learning: to fit the parameters of the classifier In the Multilayer Perceptron (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!

If this clears your doubt, you may mark it as resolved.

Thank You.

#3

I’m marking your doubt as resolved since we have not heard from you in a while.
Thank you