I found this confusing when I use the neural network toolbox in Matlab.
It divided the raw data set into three parts:
training set
validation set
test set
I notice in many training or learning algorithm, the data is often divided into 2 parts, the training set and the test set.
My questions are:
what is the difference between validation set and test set?
Is the validation set really specific to neural network? Or it is optional.
To go further, is there a difference between validation and testing in context of machine learning?
I will answer your last question first.
Validation set is different from test set. Validation set actually can be regarded as a part of training set, because it is used to build your model, neural networks or others. It is usually used for parameter selection and to avoild overfitting. If your model is non-linear (like NN) is trained on a training set only, it is very likely to get 100% accuracy and overfit, thus get very poor performance on test set. Thus a validation set, which is independant from the training set, is used for parameter selection. In this process, people usually use "Cross Validation" http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29.
On the contrary, test test is only used to test the performance of a trained model.
To answer the other two questions.
1. It is different. Validation set is used for tuning the parameters of a model. Test set is used for performance evaluation.
2. Not specific for NN. It is used for almost all machine learning models.
I will answer your last question first.
Validation set is different from test set. Validation set actually can be regarded as a part of training set, because it is used to build your model, neural networks or others. It is usually used for parameter selection and to avoild overfitting. If your model is non-linear (like NN) is trained on a training set only, it is very likely to get 100% accuracy and overfit, thus get very poor performance on test set. Thus a validation set, which is independant from the training set, is used for parameter selection. In this process, people usually use "Cross Validation" http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29.
On the contrary, test test is only used to test the performance of a trained model.
To answer the other two questions.
1. It is different. Validation set is used for tuning the parameters of a model. Test set is used for performance evaluation.
2. Not specific for NN. It is used for almost all machine learning models.
The training data set is used to construct a predictive relationship for all machine learning models. Most approaches that search through training data for empirical relationships tend to overfit the data, meaning that they can identify relationships in the training data that do not hold in general. A test set is a set of data that is independent of the training data. If a model fit to the training set also fits the test set well, minimal overfitting has taken place.
In order to avoid overfitting, , it is necessary to have a validation set in addition to the training and test sets
The validation set is used to compare their performances and decide to select a model among different models (In ANN, comparison of ANN models with different number of hidden layers for instance .
finally, the test set is used to obtain the performance characteristics such as accuracy (RMSE,...). The test data set is not used in model building process.
Check out "Evaluating Classifiers" by C Elkan
http://cseweb.ucsd.edu/~elkan/250Bwinter2012/classifiereval.pdf
It will surely answer your questions.
Dear friend
Greetings.
I would like to add some spice to the above answers.
Let me define the above terms in simple words.
Training set is the one that has to used for the training of the algorithm (here ANN). class is known for all the samples.
Validation set is used to find out how accurate is the Algorithm. To calculate the efficiency of the algorithm in terms of RMSE, acc% etc. class is known for all the samples.
Test set is the one which is used for prediction where the class is not be known. Here the algorithm is used for finding the class of unknown samples.
I hope this helps you.
Best regards
Dr.Indrajit Mandal, Ph.D.
Dear Noushath,
For ANN modeling (or other ML algorithms) we typically partition the dataset into three parts: a training set (say, 60%), a validation set (e.g. 20%) and a test set (e.g. 20%). Normally, you are training the network with the training set to adjust the weights. To make sure you don't overfit the network and also fine-tune models you need to input the validation set to the network and check if the error is within some range (This set is not being using directly to adjust the weights but used to give the optimal number of hidden units or determine a stopping point for the back-propagation algorithm). Finally, the accuracy of the model on the test data gives a realistic estimate of the performance of the model on completely unseen data and in order to confirm the actual predictive power of the network.
To summarize all:
Training set --> to fit the parameters [i.e., weights]
Validation set --> to tune the parameters [i.e., architecture]
Test set --> to assess the performance [i.e., generalization and predictive power]
Test set is used to test the data set which is input to your model.
validation test is used to validate the output produced by your model
Training set is used to train your model.
Validation/Calibration data is used to build/train/calibrate the model while testing/validationb data is used for the testing of the developed/trained/calibrated model.
Actually, there are two approaches in the literature:
1. Divide the whole data into two parts: training/calibration set and testing/validation set. Most common approach is to use 75% of the data for calibration and remaining 25% for the testing
2. Dived the whole data set into three sets: Calibraiton, validation and testing. Most commonly, 50% of the data is used for developing the model, remaining 25% for validation of the developed model and remaining 25% is used for testing the model.
You can use any of the above approaches for developing your neural network/machine learing models. I personally prefer first approach
Can anyone tell me for a given input ,how to identify or classify the data into training data or test data ?
Thanks all for your valuable inputs.
I found similar question on StackExchange, that might be useful for some researchers, follow the link
http://stackoverflow.com/questions/2976452/whats-is-the-difference-between-train-validation-and-test-set-in-neural-networ
By the question of Noushath, we got helpful answers, Thanks everybody for your support!
Basic difference between Training ,Validation and test sets are as follows:
1.Training Set: This is the data that used by the training algorithm to adjust the weights of the network.
2.Validation set(Development set):This data is used during training to assess how well the
network is currently performing — the performance of the network on this data may
be used to guide the training in some way (e.g. controlling the learning rate, deciding
when to stop training, choosing between several trained networks).
3.Test set:This is the genuine test data — and, ideally, should be used once
only, after training is complete.
From my understanding, the parameters of a model are selected based on the a training dataset and then the performance of the model with selected parameters is evaluated using a testing process.
In the training process, if all training dataset is used once, there may be a problem with over-fitting. In order to avoid this over-fitting issue, the training dataset is often separated for cross validation (CV) process. In this process, the data can be divided into different folds, the each fold will be used to 'validate' the model which is trained with other folds. An objective function is formed and the the parameters are selected based on the optimization of that objective function in cross validation process.
From this perspective, your questions can be answered as follows:
1. Validation set is used for determining the parameters of the model, and test set is used for evaluate the performance of the model in an unseen (real world) dataset .
2. Validation set is optional, and it is aimed to avoid over-fitting problem.
3. Again, the validation set is for tuning the parameters, and the test set is used for the evaluation purposes.
Validation set is used for minimizing the overfitting. It is a part of training dataset which finds out the fine tuned parameters for our algorithm. Test dataset is used to evaluate the performance of the algorithm. This has to be carried out after training.
my question regarding the validation set is that when we have to apply the validation set to the inputs to check the error?
Should they be fed at each iteration after the weights are updated?
or they must be applied to the network every several iteration ?
Test set include the input output data on which the training of NN is performed. The validation set also includes input output data (having same ranges as of the training set samples) but these are not used during the training. But these samples are also generated from the same unknown function or system.
Training set used for learning the parameters of the model [i.e., weights] of the classifier. Validation set used to learning the hyper-parameters [i.e., Loss function, Learning Rate,... ] . Test set used to assess the performance of a trained classifier.
-Training set is used for adjust the weights of the model.
- validation set is used for avoid the over fitting and adjust the hyper parameters(i.e loss function, learning rate).
-Test set is used to evaluate the trained model.
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