Linear Regression game: Difference between revisions
From Algolit
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by Algolit | by Algolit | ||
| − | + | [https://gitlab.constantvzw.org/algolit/mundaneum/tree/master/exhibition/6-Learners/Game_documentation Sources on Gitlab] | |
| − | By playing this game you will realize that as a player you have a lot of decisions to make. You will experience what it means to create a coherent dataset, to decide what is in and what is not in. If all goes well, you will feel the urge to change your data in order to obtain better results. This is part of the art of approximation that is at the basis of all machine learning | + | Linear Regression is one of the best-known and best-understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables. |
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| + | By playing this game you will realize that as a player you have a lot of decisions to make. You will experience what it means to create a coherent dataset, to decide what is in and what is not in. If all goes well, you will feel the urge to change your data in order to obtain better results. This is part of the art of approximation that is at the basis of all machine learning practices. | ||
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Latest revision as of 18:21, 4 June 2019
by Algolit
Linear Regression is one of the best-known and best-understood algorithms in statistics and machine learning. It has been around for almost 200 years. It is an attractive model because the representation is so simple. In statistics, linear regression is a statistical method that allows to summarize and study relationships between two continuous (quantitative) variables.
By playing this game you will realize that as a player you have a lot of decisions to make. You will experience what it means to create a coherent dataset, to decide what is in and what is not in. If all goes well, you will feel the urge to change your data in order to obtain better results. This is part of the art of approximation that is at the basis of all machine learning practices.
Concept & realisation: An Mertens