I understand how/why one goes about breaking data up into a train/test/cross validation set; however, it is unclear how this works analogously when the more rigorous nested k-folds method. Additionally, can you describe the motivational difference between a k-folds method and a nested k-folds method. The link below provides mathematical details for the nested k-folds method.
Thesis Understanding Random Forests: From Theory to Practice
The purpose in layman's terms is explained in the link below: http://www.quora.com/How-do-random-forests-work-in-laymans-terms
dear friend
The aim of these kind of Cross validation is to
1. see that the train set and the test set are independent.
2. To evaluate the average performance of the classifiers.
3. To see how the algorithm will behave given another dataset is given....
Nested CV is done for more rigorous testing and training.
You just see my publications , you will find more details....
Mandal, I., Sairam, N. New machine-learning algorithms for prediction of Parkinson's disease (2014) International Journal of Systems Science, 45 (3), pp. 647-666. DOI: 10.1080/00207721.2012.724114
Mandal, I., Sairam, N. Accurate telemonitoring of Parkinson's disease diagnosis using robust inference system (2013) International Journal of Medical Informatics, 82 (5), pp. 359-377. DOI: 10.1016/j.ijmedinf.2012.10.006
Mandal, I., Sairam, N. Accurate prediction of coronary artery disease using reliable diagnosis system (2012) Journal of Medical Systems, 36 (5), pp. 3353-3373. DOI: 10.1007/s10916-012-9828-0
Mandal, I., Sairam, N. Enhanced classification performance using computational intelligence (2011) Communications in Computer and Information Science, 204 CCIS, pp. 384-391. DOI: 10.1007/978-3-642-24043-0_39
Mandal, I. Software reliability assessment using artificial neural network (2010) ICWET 2010 - International Conference and Workshop on Emerging Trends in Technology 2010, Conference Proceedings, pp. 698-699. DOI: 10.1145/1741906.1742067
Mandal, I. A low-power content-addressable memory (CAM) using pipelined search scheme (2010) ICWET 2010 - International Conference and Workshop on Emerging Trends in Technology 2010, Conference Proceedings, pp. 853-858. DOI: 10.1145/1741906.1742103
Indrajit Mandal, Developing New Machine Learning Ensembles for Quality Spine Diagnosis, Knowledge-Based Systems, Available online 19 October 2014, ISSN 0950-7051, http://dx.doi.org/10.1016/j.knosys.2014.10.012.
http://www.sciencedirect.com/science/article/pii/S0950705114003797
Mandal, I. A novel approach for accurate identification of splice junctions based on hybrid algorithms (2014) Journal of Biomolecular Structure and Dynamics
pp. 1-10 | DOI: 10.1080/07391102.2014.944218 PMID: 25203504
http://bit.ly/1vQZsei
Thanks a lot.
I hope this will help you.
Best,
Dr.Indrajit Mandal
Nested cross-validation is commonly used to answer a different question, compared to regular cross-validation.
Standard cross-validation is used to compute an estimate of generalization performance, while using the entire training set for both training and (independent) testing. In machine learning, cross-validation is often used in the tuning phase, where we must find a suitable set of hyperparameters (each potential set is evaluated via cross-validation).
Nested cross-validation is on a higher level of inference. It wraps a second cross-validation procedure around the standard one. This can be used for various things, including:
- evaluating the performance of a full modeling pipeline, including the tuning phase. Standard cross-validation, in contrast, evaluates a single model.
- evaluating the stability of models, by assessing how much the estimated generalization performance varies in the outer folds. For a stable model, this variation will be small.
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