What s is the difference between train validation and test set in neural networks

Question

I m using this library to implement a learning agent   I have generated the training cases  but I don t know for sure what the validation and test sets are  The teacher says      70  should be train cases  10  will be test cases and the rest 20  should be validation cases    edit  I have this code for training  but I have no idea when to stop training     def train self  train  validation  N 0 3  M 0 1         N  learning rate       M  momentum factor     accuracy   list       while True           error   0 0         for p in train              input  target   p             self update input              error   error   self backPropagate target  N  M          print  validation          total   0         for p in validation              input  target   p             output   self update input              total    sum  abs target - output  for target  output in zip target  output     calculates sum of absolute diference between target and output          accuracy append total          print min accuracy          print sum accuracy -5    5          if i   100    0          print  error  -14f    error         if    lt                 break   edit  I can get an average error of 0 2 with validation data  after maybe 20 training iterations  that should be 80    average error   sum of absolute difference between validation target and output  given the validation data input size of validation data   1         avg error 0 520395          validation         0 246937882684 2         avg error 0 272367            validation         0 228832420879 3         avg error 0 249578             validation         0 216253590304             22         avg error 0 227753         validation         0 200239244714 23         avg error 0 227905             validation         0 199875013416

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In simple words define Training set  Test set  Validation set  Training set  Is used for finding Nearest neighbors  Validation set  Is for finding different k which is applying to train set  Test set  Is used for finding the maximum accuracy and unseen data in future

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Say you train a model on a training set and then measure its performance on a test set  You think that there is still room for improvement and you try tweaking the hyper-parameters   If the model is a Neural Network - hyper-parameters are the number of layers  or nodes in the layers    Now you get a slightly better performance  However  when the model is subjected to another data   not in the testing and training set   you may not get the same level of accuracy  This is because you introduced some bias while tweaking the hyper-parameters to get better accuracy on the testing set  You basically have adapted the model and hyper-parameters to produce the best model for that particular training set   A common solution is to split the training set further to create a validation set  Now you have   training set testing set validation set   You proceed as before but this time you use the validation set to test the performance and tweak the hyper-parameters  More specifically  you train multiple models with various hyper-parameters on the reduced training set  i e   the full training set minus the validation set   and you select the model that performs best on the validation set    Once you ve selected the best performing model on the validation set  you train the best model on the full training set  including the valida- tion set   and this gives you the final model   Lastly  you evaluate this final model on the test set to get an estimate of the generalization error

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The training and validation sets are used during training   for each epoch     for each training data instance         propagate error through the network         adjust the weights         calculate the accuracy over training data     for each validation data instance         calculate the accuracy over the validation data     if the threshold validation accuracy is met         exit training     else         continue training   Once you re finished training  then you run against your testing set and verify that the accuracy is sufficient   Training Set  this data set is used to adjust the weights on the neural network   Validation Set  this data set is used to minimize overfitting  You re not adjusting the weights of the network with this data set  you re just verifying that any increase in accuracy over the training data set actually yields an increase in accuracy over a data set that has not been shown to the network before  or at least the network hasn t trained on it  i e  validation data set   If the accuracy over the training data set increases  but the accuracy over the validation data set stays the same or decreases  then you re overfitting your neural network and you should stop training   Testing Set  this data set is used only for testing the final solution in order to confirm the actual predictive power of the network

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We create a validation set to   Measure how well a model generalizes  during training  Tell us when to stop training a model When the validation loss stops decreasing  and  especially when the validation loss starts increasing and the    training loss is still decreasing    Why validation set used

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Would appreciate any thoughts on the situation with 3 data sets  Say a logistic regression model is fitted yielding the following accuracy  Gini   Train  70   Test 58  and Out-of-time validation  66   Actually all the possible combinations of predictors bring the same results with quite a huge drop between train and test data sets  The sample size is around 8k divided into train and test 70 30  OOT sample contains a few thousands of cases  Regularization  ensembles didn t help in solving this  I doubt whether this is something I should concern if OOT performance is acceptable and close to train sample performance

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Cross-validation set is used for model selection  for example  select the polynomial model with the least amount of errors for a given parameter set  The test set is then used to report the generalization error on the selected model  From here  https   www coursera org learn machine-learning lecture QGKbr model-selection-and-train-validation-test-sets

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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  generalization  of a fully specified classifier   From ftp   ftp sas com pub neural FAQ1 txt section  quot What are the population  sample  training set  design set  validation quot  The error surface will be different for different sets of data from your data set  batch learning   Therefore if you find a very good local minima for your test set data  that may not be a very good point  and may be a very bad point in the surface generated by some other set of data for the same problem  Therefore you need to compute such a model which not only finds a good weight configuration for the training set but also should be able to predict new data  which is not in the training set  with good error  In other words the network should be able to generalize the examples so that it learns the data and does not simply remembers or loads the training set by overfitting the training data  The validation data set is a set of data for the function you want to learn  which you are not directly using to train the network  You are training the network with a set of data which you call the training data set  If you are using gradient based algorithm to train the network then the error surface and the gradient at some point will completely depend on the training data set thus the training data set is being directly used to adjust the weights  To make sure you don t overfit the network you need to input the validation dataset to the network and check if the error is within some range  Because the validation set is not being using directly to adjust the weights of the netowork  therefore a good error for the validation and also the test set indicates that the network predicts well for the train set examples  also it is expected to perform well when new example are presented to the network which was not used in the training process  Early stopping is a way to stop training  There are different variations available  the main outline is  both the train and the validation set errors are monitored  the train error decreases at each iteration  backprop and brothers  and at first the validation error decreases  The training is stopped at the moment the validation error starts to rise  The weight configuration at this point indicates a model  which predicts the training data well  as well as the data which is not seen by the network   But because the validation data actually affects the weight configuration indirectly to select the weight configuration  This is where the Test set comes in  This set of data is never used in the training process  Once a model is selected based on the validation set  the test set data is applied on the network model and the error for this set is found  This error is a representative of the error which we can expect from absolutely new data for the same problem  EDIT  Also  in the case you do not have enough data for a validation set  you can use crossvalidation to tune the parameters as well as estimate the test error

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Training Dataset  The sample of data used to fit the model   Validation Dataset  The sample of data used to provide an unbiased evaluation of a model fit on the training dataset while tuning model hyperparameters  The evaluation becomes more biased as skill on the validation dataset is incorporated into the model configuration   Test Dataset  The sample of data used to provide an unbiased evaluation of a final model fit on the training dataset

[artificial-intelligence] What's is the difference between train, validation and test set, in neural networks?

In simple words define Training set, Test set, Validation set

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