Machine learning

As designers, we are asked to solve a problem. We are given some data and an expected output. The first step is to frame the problem in a way that a machine can understand it, and in a way that carries meaning for a human. The following six broad approaches are what we can take to precisely define our machine learning problem:

Exploratory: Here, we analyze data, looking for patterns such as a trend or relationship between variables. Exploration will often lead to a hypothesis such as linking diet with disease, or crime rate with urban dwellings.

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Descriptive: Here, we try to summarize specific features of our data. For instance, the average life expectancy, average temperature, or the number of left-handed people in a population.

Inferential: An inferential question is one that attempts to support a hypothesis, for instance, proving (or disproving) a general link between life expectancy and income by using different data sets.

Predictive: Here, we are trying to anticipate future behavior. For instance, predicting life expectancy by analyzing income.

Casual: This is an attempt to find out what causes something. Does low income cause a lower life expectancy?

Mechanistic: This tries to answer questions such as “what are the mechanisms that link income with life expectancy?”

For each one of the above investigation types, come up with at least three questions that you have to resolve with a biometric system.

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