Timeline for Dealing with NAN values in my training dataset
Current License: CC BY-SA 4.0
4 events
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| May 27, 2020 at 10:57 | comment | added | Rim Sleimi |
Is it expected to lose data (rows and columns) when you use simpleImputer ?
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| May 27, 2020 at 7:04 | comment | added | Johan | The point of my second example is to detect Nans and Infinites and replace them with the nodata value of your tiff. How to deal with these nodata values is really up to you. You can fill them with a static value, with a different observation, or simply exclude them from analysis. | |
| May 26, 2020 at 16:06 | comment | added | Rim Sleimi |
I did apply a cloud mask, but I also used linear interpolation to fill the gaps. I did also use np.seterr(divide='ignore', invalid='ignore') to suppress the "divide by zero" or "divide by NaN" warning because I noticed that I was getting that warning. The code you wrote does it only detect Nans (and Infinite values)? should I input them? using mean values or median?
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| May 26, 2020 at 15:40 | history | answered | Johan | CC BY-SA 4.0 |