Timeline for How to know the model has started overfitting?
Current License: CC BY-SA 4.0
18 events
| when toggle format | what | by | license | comment | |
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| S Aug 5, 2020 at 17:27 | history | suggested | Zephyr | CC BY-SA 4.0 |
Corrected link quote
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| Aug 5, 2020 at 14:09 | review | Suggested edits | |||
| S Aug 5, 2020 at 17:27 | |||||
| Sep 29, 2017 at 10:26 | answer | added | dileep balineni | timeline score: 8 | |
| Sep 28, 2017 at 20:37 | answer | added | David Makovoz | timeline score: 2 | |
| Sep 26, 2017 at 10:54 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Aug 27, 2017 at 7:06 | answer | added | Juan Antonio Gomez Moriano | timeline score: 5 | |
| Aug 22, 2017 at 16:06 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Jul 23, 2017 at 15:59 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Jun 23, 2017 at 15:12 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| May 24, 2017 at 15:01 | comment | added | figs_and_nuts | @Grasshopper In a nutshell. The first instance of the model is created after 280 epoch (refer to the question asked) and the second instance of the model is created after 15 epoch. Now the book goes on to suggest epoch 280 as the one where the over-fitting has started. I am finding it hard to swallow that. any help or thoughts that you can provide are much appreciated. | |
| May 24, 2017 at 14:56 | history | edited | figs_and_nuts | CC BY-SA 3.0 |
Unindented the part that was not a part of the excerpt
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| May 24, 2017 at 14:54 | comment | added | figs_and_nuts | @Grasshopper let us say the model is trying to predict one of 4 classes {A, B, C, D}. Test data labels (in order) are (A, B, C, D). Now in one instance the model throws probabilities as (I will be labeling the predictions along) ((0.28, 0.24, 0.24, 0.24)(A), (0.24,0.28,0.24,0.24)(B), (0.24,0.24,0.28,0.24)(C), (0.24,0.24,0.24,0.28)(D)) and in another the model throws ((1,0,0,0)(A), (0,1,0,0)(B), (0.24,0.26,0.25,0.25)(B), (0,0,0,1)(D)). What i mean by low confidence is the first instance. please note the classification accuracy is 100% in the first instance and yet the cost is higher | |
| May 24, 2017 at 14:05 | comment | added | Grasshopper | What do you mean by 'making decisions with low confidence'? | |
| May 24, 2017 at 5:19 | answer | added | Bashar Haddad | timeline score: 0 | |
| S May 24, 2017 at 3:44 | history | suggested | user380 | CC BY-SA 3.0 |
quote formatting, spelling
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| May 23, 2017 at 22:41 | review | Suggested edits | |||
| S May 24, 2017 at 3:44 | |||||
| May 22, 2017 at 23:27 | comment | added | Emre | The model is not aware of the test set. It stands in as a proxy for unseen data. Therefore, if it comes from a representative distribution, you can use it to determine when overfitting occurs. If you wish, you can create yet another hold out set and see if this assumption holds. | |
| May 22, 2017 at 21:00 | history | asked | figs_and_nuts | CC BY-SA 3.0 |