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Feature extraction

Next, you need to establish whether the information gathered is suitable for the intended audience.

If we have gathered a lot of data, it is unlikely that our analysis will utilise the whole data set. We need to consider the scope of our analysis and what data is required. This is known as feature extraction.

For example, what feature would you use to select the buttons depicted on the left to sew on the shirts depicted on the right?

This illustration shows a selection of buttons on the left-hand side and two shirts on the right-hand side. There are two types of buttons: yellow triangular-shaped buttons and round red buttons. The two shirts are plain: one shirt is yellow and one shirt is red.

What feature would you use to select the buttons to sew onto these shirts? Would you still use the same feature?

This illustration shows a selection of buttons on the left-hand side and two shirts on the right-hand side. There are two types of buttons: yellow triangular-shaped buttons and round red buttons. The two shirts are patterned: the yellow shirt has a pattern of small yellow triangles and the red shirt has a pattern of small red dots.

If you sign up for the Coventry University program ‘AI Technologies for Business and Management’, the next short course (Fundamental Machine Learning for AI) will illustrate how to apply feature extraction to your analysis.

Your task

Consider the following extract from the records of a farm:

This table lists the different cattle by breed, age, colour, weight and daily milk production(click to expand)

Which features would you use to analyse the following aspects?

  • Average weight of a breed
  • Average milk production of a breed
  • Most common names for different breeds
  • Correlation between weight and daily production
  • Correlation between colour and milk production
  • Total farm output
  • Average output per cow

As you can see, different analyses require different subsets, or features, of the data.

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This article is from the free online course:

Using Artificial Intelligence (AI) Technologies for Business Planning and Decision-making

Coventry University