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 | 1 | initial version |
the range of your floatinput data does not matter for the KNN classifier.
however, it DOES matter for other classifiers, like SVM, ANN_MLP, where you should normalize it. so, if there's any chance you would change it -- normalize !
sidenote: the size/length of your features are far more important than the range of the data. throwing whole images (you mentioned: "pixels") might be a bad idea, unless you have tons of those (the ratio of image count / size is somewhat "even")
| | 2 | No.2 Revision |
the range of your floatinput data does not matter for the KNN classifier.
however, it DOES matter for other classifiers, like SVM, ANN_MLP, where you should normalize it. so, if there's any chance you would change it -- normalize !
sidenote: the size/length of your features are far more important than the range of the data. throwing whole images (you mentioned: "pixels") at KNearest might be a bad idea, unless you have tons of those (the ratio of image count / size is somewhat "even")
