Re:Machine Learning 101: An Intro to Utilizing Decision Trees
by nl on Friday September 30, 2016 @08:18AM
(#12611985)
That's a very image-centric explanation, and I'm not at all sure it makes things any easier.
Conceptually, in the image case, features are "things" in images that ML tools use to perform tasks.
A image with lots of blue it it has a chance that it is of sky.
An image with lots of hard edges might be something human made - a house, a book etc.
Pixels are really a distraction - there are alternative representations of images which don't use pixels at all. Think wavelet based compressed sensing techniques.
Conceptually, in the image case, features are "things" in images that ML tools use to perform tasks.
A image with lots of blue it it has a chance that it is of sky.
An image with lots of hard edges might be something human made - a house, a book etc.
Pixels are really a distraction - there are alternative representations of images which don't use pixels at all. Think wavelet based compressed sensing techniques.