14. Feature Engineering for Domains: Domain-Specific Learning

In [1]:

# setup
from mlwpy import *
%matplotlib inline

import cv2

In a perfect world, our standard classification algorithms would be provided with data that is relevant, plentiful, tabular (formatted in a table), and naturally discriminitive when we look at it as points in space.

In reality, we may be stuck with data that is

  1. Only an approximation of our target task

  2. Limited in quantity or covers only some of many possibilities

  3. Misaligned with the prediction method(s) we are trying to apply to it

  4. Written as text or images which are decidedly not in an example-feature table

Issues 1 and 2 are specific to a learning problem you are focused on. We discussed issue 3 in Chapters 10 and ...

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