# What is devising validating and testing of algorithms animedating vkontakte ru

The training set is used to fit the models; the validation set is used to estimate prediction error for model selection; the test set is used for assessment of the generalization error of the final chosen model. The validation set is often used to tune hyper-parameters. 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! 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!

Ideally, the test set should be kept in a “vault,” and be brought out only at the end of the data analysis. In case if you don't need to choose an appropriate model from several rivaling approaches, you can just re-partition your set that you basically have only training set and test set, without performing the validation of your trained model. For example, in the deep learning community, tuning the network layer size, hidden unit number, regularization term(wether L1 or L2) depends on the validation set What is the correct way to split the sets? @stmax Not to be pedantic, but once we have our final test error and we are NOT satisfied with the result, what do we do, if we cant tune our model any further? I have often wondered about this [email protected] you can continue tuning the model, but you'll have to collect a new test set.

Performance management is the process of executing a correct program on data sets and measuring the time and space it takes to compute the results.

These timing figures are useful in that they may confirm a previously done analysis and point out logical places to perform useful optimization.

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.

When we get the problem, we should first analyse the given problem clearly and then write down some steps on the paper.(2) Validate Algorithm : Once an algorithm is devised , it is necessary to show that it computes the correct answer for all possible legal inputs . The algorithm need not as yet be expressed as a program. The purpose of validation is to assure us that this algorithm will work correctly independently of the issues concerning the programming language it will eventually be written in.

Once the validity of the method has been shown, a program can be written and a second phase begins.

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.

The concept of 'Training/Cross-Validation/Test' Data Sets is as simple as this.

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