32 lines
1.6 KiB
Markdown
32 lines
1.6 KiB
Markdown
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---
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title: Learning Equals Representation Evaluation Optimization
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---
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## Learning Equals Representation Evaluation Optimization
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The field of machine learning has exploded in recent years and researchers have
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developed an enormous number of algorithms to choose from. Despite this great
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variety of models to choose from, they can all be distilled into three
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components.
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The three components that make a machine learning model are representation,
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evaluation, and optimization. These three are most directly related to
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supervised learning, but it can be related to unsupervised learning as well.
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**Representation** - this describes how you want to look at your data.
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Sometimes you may want to think of your data in terms of individuals (like in
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k-nearest neighbors) or like in a graph (like in Bayesian networks).
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**Evaluation** - for supervised learning purposes, you'll need to evaluate or
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put a score on how well your learner is doing so it can improve. This
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evaluation is done using an evaulation function (also known as an *objective
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function* or *scoring function*). Examples include accuracy and squared error.
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**Optimization** - using the evaluation function from above, you need to find
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the learner with the best score from this evaluation function using a choice of
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optimization technique. Examples are a greedy search and gradient descent.
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#### More Information:
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<!-- Please add any articles you think might be helpful to read before writing the article -->
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- <a href='https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf' target='_blank' rel='nofollow'>A Few Useful Things to Know about Machine Learning</a>
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