Does anyone can share how to do training, test, and validation set?
I would like to build the model with only 50 data. I divided a data set into training and validation samples in Minitab statistical software. Then i did stepwise regression to build a model from training dataset. Then, i substracted the predicted value to the real values from a validation data set, in order to know the residual.
My questions are:
1. Can i divide into 2 split data?
2. I have no idea how to determine which is training set and validation set? for my case, i assume one is training, the other is validation set.
3. How to know that the model is good enough?
4. Overall, for what i have did, is it acceptable way?since i used Minitab.
Please share your opinion, because i have no experience to predict a model through training, test, and validation set.
Dear Euis Nurul Hidayah. How many input variables you have? If there are many then the dataset is small. In such cases you may use a technique called leave one out (LOO) cross validation. I also suggest to model your data with artificial neural networks using LOO Xvalidation. NeuroSolutions software has such training facility.
www.neurosolutions.com/
I understand you have a data with only 50 observations. This number is too little for splitting up the dataset into parts.
Hello! Basically, I agree with Mehmet Sinan Lyisoy, regarding to your dataset being too small for creating training and validation sets (in the case of regression analysis, a recommended sample size is about 20 observations per variable). The creation of training and validation sets is similar but a bit different to cross-validation. Upon having a large enough dataset, researchers can divide the data set into two sets so the researcher can work on one set (the training set) building the statistical model. Then, the statistical model is tested/validated using the other dataset (the validation set). The model built using the training dataset should fit/generalize to the validation dataset. I hope this helps. Cheers!
Yes, sample is small for splitting. In this case cross-validation could be an alternative. See "Cross-validation (statistics)" on Wikipedia.
Dear Euis Nurul Hidayah. How many input variables you have? If there are many then the dataset is small. In such cases you may use a technique called leave one out (LOO) cross validation. I also suggest to model your data with artificial neural networks using LOO Xvalidation. NeuroSolutions software has such training facility.
www.neurosolutions.com/
Dear all,
Thank you for your response. I really appreciate it.
I will try to do cross validation. I need to learn more and try to understand for statistical analysis. Thanks.
Dear Euis Nurul Hidayah,
I agree with the above-mentioned answers/suggestions. The sure thing is with so small set of data the degree of the uncertainty will be high and the model is unstable. Nevertheless, probably the answer to your question is either LOO validation (as Mr. Omid dain), or full cross validation. Both of then can never be an complete alternative to test set validation.
See my papers about "100-fold cross-validation for small sample " method on RG.
Training, Validating, and Testing
Generally, in Artificial Neural Networks (ANN) modeling, 70 %, 15 %, 15 % of data are used for training, validating, and testing, respectively. The ANN is adjusted according to the training set. Validation set represents the generalization of network. The training stops when generalization stops improving. Testing set shows the overall performance of the network.
You can use following R Package to minimise the efforts for Prediction Algorithms Comparison analysis for long term prediction or one/two step ahead forecasting. You can set the environment like test/training data size, error performance metrics and many more.
https://cran.r-project.org/web/packages/PredictTestbench/index.html
How to use:
http://www.neerajbokde.com/cran/predicttestbench
https://cran.r-project.org/web/packages/PredictTestbench/index.html
See my theory "k-fold cross-validation for small samle method".
I compare 8 LDFs using six different datasets. Download my papers from RG after 2012.
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