Timeline for Why do you need to scale data in KNN
Current License: CC BY-SA 3.0
11 events
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Apr 21, 2021 at 13:07 | comment | added | das Keks | I checked the answer before the edit and I think it was way better with the different scales. You already had the normalization visually and could see how the point should be classified but at the same time you could see that the scales are different which leads to the shown nearest neighbors. I suggest going back to the initial images. | |
Mar 11, 2019 at 19:33 | comment | added | kedarps | @Undertherainbow That is correct! | |
Mar 11, 2019 at 12:33 | comment | added | Undertherainbow | @kedarps Can I ask exactly how you normalized the data? Did you just subtract the mean and divide by the standard deviation? | |
Jun 26, 2017 at 20:10 | comment | added | kedarps | @whuber, I misunderstood your first comment. Fixed the plots, hopefully it's better now! | |
Jun 26, 2017 at 20:09 | history | edited | kedarps | CC BY-SA 3.0 |
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Jun 26, 2017 at 19:57 | comment | added | whuber♦ | That difficulty actually is the point of your response! One way to overcome it is not to use such an extreme range of scales. A 5:1 difference in scales, rather than a 1000:1 difference, would still make your point nicely. Another way is to draw the picture faithfully: the top scatterplot will seem to be a vertical line of points. | |
Jun 26, 2017 at 19:55 | comment | added | kedarps | I found it difficult to fit all data points in the same scale for both figures. Hence, I mentioned in a note that scales of axes are different. | |
Jun 26, 2017 at 19:54 | history | edited | kedarps | CC BY-SA 3.0 |
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Jun 26, 2017 at 19:30 | comment | added | whuber♦ | This answer is exactly right, but I fear the illustrations might be deceptive because of the distortions involved. The point might be better made by drawing them both so that the two axes in each are at the same scale. | |
Jun 26, 2017 at 19:01 | history | edited | kedarps | CC BY-SA 3.0 |
fixed figure, text consitency
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Jun 26, 2017 at 18:56 | history | answered | kedarps | CC BY-SA 3.0 |