Timeline for Bootstrapping for neural network validation
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 16, 2017 at 19:04 | vote | accept | Nicola Paoletti | ||
| Oct 12, 2017 at 22:36 | comment | added | Michael R. Chernick | This answer does not really say much about the bootstrap. It involves resampling from the original data set with replacement. It is used when parametric assumptions are questionable and it often is used because it has better properties then alternative methods. | |
| Oct 12, 2017 at 21:57 | comment | added | Nicola Paoletti | Yes, just to confirm, the workflow would be: 1. Train the model using samples drawn from $\mathbb{R}^n$; 2. Sample N times (e.g. N=1000) the testing set from $\mathbb{R}^n$; 3. Obtain statistics (e.g. mean accuracy and S.E.) by validating the trained model with all test datasets | |
| Oct 12, 2017 at 20:56 | comment | added | X. Zhang | It looks good. But a more standard way is to obtain data needed for your model(training/testing) in the first place. For example obtain 10,000 samples. Then divided them into training and testing data. (7,000 for training and 3,000 for testing). From my understanding, what you are doing is training the model used the obtained 7,000 samples and then sampling another 3,000 data from $\mathbb{R}^n$ for testing. This is a little bit odd, but will not cause any problem for validating your model. | |
| Oct 12, 2017 at 20:44 | comment | added | Nicola Paoletti | Many thanks for clarifying the meaning of bootstrap. What do you think of the validation scheme I described (i.e., resample only the test set from $\mathbb{R}^n$ and keep the network fixed)? | |
| Oct 12, 2017 at 20:40 | review | First posts | |||
| Oct 12, 2017 at 22:36 | |||||
| Oct 12, 2017 at 20:36 | history | answered | X. Zhang | CC BY-SA 3.0 |