Timeline for Kernel Density estimation function and bandwidth selection
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Jun 27, 2017 at 18:08 | comment | added | david | Thanks again...i have taken a look at it and it is becoming clearer as i go through them... | |
| Jun 27, 2017 at 12:57 | history | edited | user166243 | CC BY-SA 3.0 |
added 2004 characters in body
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| Jun 26, 2017 at 15:11 | comment | added | user166243 | you need to evaluate the fitted KDE model at your validation data points. For example if you use KernelDensity from sklearn.neighbors, then you have a function, which does that for you: kde.score_samples() | |
| Jun 26, 2017 at 15:06 | comment | added | david | Thank you for the prompt response ''user2137591''. Since am not really used to this,,,, what sign do i look out for to pick out the ''highest likelihood'' thanks | |
| Jun 26, 2017 at 14:55 | history | answered | user166243 | CC BY-SA 3.0 |