39 lines
2.0 KiB
Markdown
39 lines
2.0 KiB
Markdown
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---
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title: Supervised Learning
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---
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## Supervised Learning
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In supervised learning, we know what the correct output should be. Supervised learning problems can be categorized into regression and classification. A regression problem is where you map input to a continuous output. A classification problem, in contrast, is where you map (group) inputs into discrete categories.
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### Regression
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Given data about used cars such as their mileage, you can predict their market prices. Since price is a continuous variable, this is a regression problem. In another example, Microsoft released a web app that predicts age based on picture. Again, as age is continuous rather than discrete or categorical, this is also a regression problem.
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### Classification
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The regression problems above can be turned into classification problems. Suppose you want to look for a used car less than X dollars. Then the output would be if the used car fits the price that you set. Similarly, age prediction can be a classification problem if we are looking to predict if a submitted picture belongs to someone under 18, and therefore should not be allowed to buy cigarettes.
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### Discussion points:
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- What is special about supervised learning?
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- In what scenario would you use it in?
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- Caveats or traps to think about?
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- What are some example models?
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#### Example 1:
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> Given data about the size of houses on the real estate market, try to predict their price.
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Price as a function of size is a continuous output, so this is a regression problem.
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#### Example 2:
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(a) Regression - For continuous-response values. For example given a picture of a person, we have to predict their age on the basis of the given picture
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(b) Classification - for categorical response values, where the data can be separated into specific “classes”. For example given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
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#### Suggested Reading:
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- https://en.wikipedia.org/wiki/Supervised_learning
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- https://stackoverflow.com/a/1854449/6873133
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